Spark sql xóa thẻ html

Question

Một trong những khái niệm trừu tượng chính trong Apache Spark là SparkSQL

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1, tương tự như cấu trúc

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1 được tìm thấy trong R và Pandas. Một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1 tương tự như một bảng và hỗ trợ kiểu chức năng (bản đồ/thu nhỏ/bộ lọc/v.v. ) hoạt động và hoạt động SQL (chọn, dự án, tổng hợp)

Nội dung chính Show

- sự thi công -
__trong đó__
- thông tin -
lược đồ
in lược đồ
trình diễn
phân vùng lại
kết hợp lại
— biến đổi —
apply_mapping
Ví dụ. Sử dụng apply_mapping để đổi tên trường và thay đổi loại trường
drop_fields
Ví dụ. Sử dụng drop_fields để xóa các trường khỏi # Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame. # Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name. # Replace EXAMPLE-FRIENDS-DATA with your table name. from pyspark.context import SparkContext from awsglue.context import GlueContext # Create GlueContext sc = SparkContext.getOrCreate() glueContext = GlueContext(sc) # Create a DynamicFrame from Glue Data Catalog glue_source_database = "MY-EXAMPLE-DATABASE" glue_source_table = "EXAMPLE-FRIENDS-DATA" friends = glueContext.create_dynamic_frame.from_catalog( database=glue_source_database, table_name=glue_source_table ) print("Schema for friends DynamicFrame before calling drop_fields:") friends.printSchema() # Remove location.county, remove friends.age, remove age friends = friends.drop_fields(paths=["age", "location.county", "friends.age"]) print("Schema for friends DynamicFrame after removing age, county, and friend age:") friends.printSchema() 6
Ví dụ. Sử dụng bộ lọc để có được lựa chọn các trường được lọc
Ví dụ. Sử dụng tham gia để kết hợp Schema for friends DynamicFrame before calling drop_fields: root |-- name: string |-- age: int |-- location: struct | |-- state: string | |-- county: string |-- friends: array | |-- element: struct | | |-- name: string | | |-- age: int Schema for friends DynamicFrame after removing age, county, and friend age: root |-- name: string |-- location: struct | |-- state: string |-- friends: array | |-- element: struct | | |-- name: string 2
bản đồ
Ví dụ. Sử dụng bản đồ để áp dụng một chức năng cho mọi bản ghi trong # Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame. # Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name. # Replace EXAMPLE-FRIENDS-DATA with your table name. from pyspark.context import SparkContext from awsglue.context import GlueContext # Create GlueContext sc = SparkContext.getOrCreate() glueContext = GlueContext(sc) # Create a DynamicFrame from Glue Data Catalog glue_source_database = "MY-EXAMPLE-DATABASE" glue_source_table = "EXAMPLE-FRIENDS-DATA" friends = glueContext.create_dynamic_frame.from_catalog( database=glue_source_database, table_name=glue_source_table ) print("Schema for friends DynamicFrame before calling drop_fields:") friends.printSchema() # Remove location.county, remove friends.age, remove age friends = friends.drop_fields(paths=["age", "location.county", "friends.age"]) print("Schema for friends DynamicFrame after removing age, county, and friend age:") friends.printSchema() 6
hợp nhấtKhung động
Ví dụ. Sử dụng mergeDynamicFrame để hợp nhất hai Schema for friends DynamicFrame before calling drop_fields: root |-- name: string |-- age: int |-- location: struct | |-- state: string | |-- county: string |-- friends: array | |-- element: struct | | |-- name: string | | |-- age: int Schema for friends DynamicFrame after removing age, county, and friend age: root |-- name: string |-- location: struct | |-- state: string |-- friends: array | |-- element: struct | | |-- name: string 2 dựa trên khóa chính
quan hệ hóa
Ví dụ. Sử dụng quan hệ hóa để làm phẳng lược đồ lồng nhau trong # Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame. # Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name. # Replace EXAMPLE-FRIENDS-DATA with your table name. from pyspark.context import SparkContext from awsglue.context import GlueContext # Create GlueContext sc = SparkContext.getOrCreate() glueContext = GlueContext(sc) # Create a DynamicFrame from Glue Data Catalog glue_source_database = "MY-EXAMPLE-DATABASE" glue_source_table = "EXAMPLE-FRIENDS-DATA" friends = glueContext.create_dynamic_frame.from_catalog( database=glue_source_database, table_name=glue_source_table ) print("Schema for friends DynamicFrame before calling drop_fields:") friends.printSchema() # Remove location.county, remove friends.age, remove age friends = friends.drop_fields(paths=["age", "location.county", "friends.age"]) print("Schema for friends DynamicFrame after removing age, county, and friend age:") friends.printSchema() 6
đổi tên_field
Ví dụ. Sử dụng rename_field để đổi tên các trường trong # Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame. # Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name. # Replace EXAMPLE-FRIENDS-DATA with your table name. from pyspark.context import SparkContext from awsglue.context import GlueContext # Create GlueContext sc = SparkContext.getOrCreate() glueContext = GlueContext(sc) # Create a DynamicFrame from Glue Data Catalog glue_source_database = "MY-EXAMPLE-DATABASE" glue_source_table = "EXAMPLE-FRIENDS-DATA" friends = glueContext.create_dynamic_frame.from_catalog( database=glue_source_database, table_name=glue_source_table ) print("Schema for friends DynamicFrame before calling drop_fields:") friends.printSchema() # Remove location.county, remove friends.age, remove age friends = friends.drop_fields(paths=["age", "location.county", "friends.age"]) print("Schema for friends DynamicFrame after removing age, county, and friend age:") friends.printSchema() 6
giải quyếtLựa chọn
Ví dụ. Sử dụng giải quyếtChoice để xử lý một cột chứa nhiều loại
select_fields
Ví dụ. Sử dụng select_fields để tạo một # Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame. # Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name. # Replace EXAMPLE-FRIENDS-DATA with your table name. from pyspark.context import SparkContext from awsglue.context import GlueContext # Create GlueContext sc = SparkContext.getOrCreate() glueContext = GlueContext(sc) # Create a DynamicFrame from Glue Data Catalog glue_source_database = "MY-EXAMPLE-DATABASE" glue_source_table = "EXAMPLE-FRIENDS-DATA" friends = glueContext.create_dynamic_frame.from_catalog( database=glue_source_database, table_name=glue_source_table ) print("Schema for friends DynamicFrame before calling drop_fields:") friends.printSchema() # Remove location.county, remove friends.age, remove age friends = friends.drop_fields(paths=["age", "location.county", "friends.age"]) print("Schema for friends DynamicFrame after removing age, county, and friend age:") friends.printSchema() 6 mới với các trường đã chọn
Ví dụ. Sử dụng đầu nối để ghi các trường mẫu từ # Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame. # Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name. # Replace EXAMPLE-FRIENDS-DATA with your table name. from pyspark.context import SparkContext from awsglue.context import GlueContext # Create GlueContext sc = SparkContext.getOrCreate() glueContext = GlueContext(sc) # Create a DynamicFrame from Glue Data Catalog glue_source_database = "MY-EXAMPLE-DATABASE" glue_source_table = "EXAMPLE-FRIENDS-DATA" friends = glueContext.create_dynamic_frame.from_catalog( database=glue_source_database, table_name=glue_source_table ) print("Schema for friends DynamicFrame before calling drop_fields:") friends.printSchema() # Remove location.county, remove friends.age, remove age friends = friends.drop_fields(paths=["age", "location.county", "friends.age"]) print("Schema for friends DynamicFrame after removing age, county, and friend age:") friends.printSchema() 6 lên Amazon S3
split_fields
Ví dụ. Sử dụng split_fields để chia các trường đã chọn thành một # Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame. # Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name. # Replace EXAMPLE-FRIENDS-DATA with your table name. from pyspark.context import SparkContext from awsglue.context import GlueContext # Create GlueContext sc = SparkContext.getOrCreate() glueContext = GlueContext(sc) # Create a DynamicFrame from Glue Data Catalog glue_source_database = "MY-EXAMPLE-DATABASE" glue_source_table = "EXAMPLE-FRIENDS-DATA" friends = glueContext.create_dynamic_frame.from_catalog( database=glue_source_database, table_name=glue_source_table ) print("Schema for friends DynamicFrame before calling drop_fields:") friends.printSchema() # Remove location.county, remove friends.age, remove age friends = friends.drop_fields(paths=["age", "location.county", "friends.age"]) print("Schema for friends DynamicFrame after removing age, county, and friend age:") friends.printSchema() 6 riêng biệt
Ví dụ. Sử dụng split_rows để chia các hàng trong # Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame. # Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name. # Replace EXAMPLE-FRIENDS-DATA with your table name. from pyspark.context import SparkContext from awsglue.context import GlueContext # Create GlueContext sc = SparkContext.getOrCreate() glueContext = GlueContext(sc) # Create a DynamicFrame from Glue Data Catalog glue_source_database = "MY-EXAMPLE-DATABASE" glue_source_table = "EXAMPLE-FRIENDS-DATA" friends = glueContext.create_dynamic_frame.from_catalog( database=glue_source_database, table_name=glue_source_table ) print("Schema for friends DynamicFrame before calling drop_fields:") friends.printSchema() # Remove location.county, remove friends.age, remove age friends = friends.drop_fields(paths=["age", "location.county", "friends.age"]) print("Schema for friends DynamicFrame after removing age, county, and friend age:") friends.printSchema() 6
Ví dụ. Sử dụng unbox để bỏ hộp một trường chuỗi thành một cấu trúc
không hợp nhau
Ví dụ. Sử dụng unnest để biến các trường lồng nhau thành các trường cấp cao nhất
unnest_ddb_json
— lỗi —
khẳng địnhErrorThreshold
lỗiAsDynamicKhung
Ví dụ. Sử dụng errorAsDynamicFrame để xem các bản ghi lỗi
lỗiĐếm
giai đoạnLỗiĐếm
Làm cách nào để xóa các thẻ HTML trong SQL?
Làm cách nào để xóa các thẻ HTML?
Làm cách nào để xóa thẻ HTML khỏi DataFrame của gấu trúc?
Làm cách nào để xóa tất cả các thẻ HTML khỏi chuỗi trong C#?

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

4 mạnh mẽ và được sử dụng rộng rãi, nhưng chúng có những hạn chế đối với các hoạt động trích xuất, chuyển đổi và tải (ETL). Đáng kể nhất, chúng yêu cầu một lược đồ được chỉ định trước khi tải bất kỳ dữ liệu nào. SparkSQL giải quyết vấn đề này bằng cách thực hiện hai lần chuyển dữ liệu—lần đầu tiên để suy luận lược đồ và lần thứ hai để tải dữ liệu. Tuy nhiên, suy luận này bị hạn chế và không giải quyết được thực tế của dữ liệu lộn xộn. Ví dụ: cùng một trường có thể thuộc loại khác trong các bản ghi khác nhau. Apache Spark thường bỏ cuộc và báo cáo loại là

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

5 bằng cách sử dụng văn bản gốc của trường. Điều này có thể không chính xác và bạn có thể muốn kiểm soát tốt hơn cách giải quyết sự khác biệt trong giản đồ. Và đối với các tập dữ liệu lớn, việc chuyển bổ sung dữ liệu nguồn có thể rất tốn kém

Để giải quyết những hạn chế này, AWS Glue giới thiệu

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6. Một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 tương tự như một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1, ngoại trừ mỗi bản ghi là tự mô tả, do đó ban đầu không cần lược đồ. Thay vào đó, AWS Glue tính toán nhanh lược đồ khi được yêu cầu và mã hóa rõ ràng các điểm không nhất quán của lược đồ bằng cách sử dụng loại lựa chọn (hoặc liên kết). Bạn có thể giải quyết những điểm không nhất quán này để làm cho bộ dữ liệu của mình tương thích với các kho lưu trữ dữ liệu yêu cầu lược đồ cố định

Tương tự, một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

9 đại diện cho một bản ghi logic trong một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6. Nó giống như một hàng trong Spark

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1, ngoại trừ việc nó tự mô tả và có thể được sử dụng cho dữ liệu không tuân theo một lược đồ cố định

Bạn có thể chuyển đổi

Schema for friends DynamicFrame before calling drop_fields:
root
|-- name: string
|-- age: int
|-- location: struct
|    |-- state: string
|    |-- county: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string
|    |    |-- age: int

Schema for friends DynamicFrame after removing age, county, and friend age:
root
|-- name: string
|-- location: struct
|    |-- state: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string

2 thành và từ

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

4 sau khi bạn giải quyết bất kỳ sự không nhất quán nào trong lược đồ

- sự thi công -

trong đó

Schema for friends DynamicFrame before calling drop_fields:
root
|-- name: string
|-- age: int
|-- location: struct
|    |-- state: string
|    |-- county: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string
|    |    |-- age: int

Schema for friends DynamicFrame after removing age, county, and friend age:
root
|-- name: string
|-- location: struct
|    |-- state: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string

4

Schema for friends DynamicFrame before calling drop_fields:
root
|-- name: string
|-- age: int
|-- location: struct
|    |-- state: string
|    |-- county: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string
|    |    |-- age: int

Schema for friends DynamicFrame after removing age, county, and friend age:
root
|-- name: string
|-- location: struct
|    |-- state: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string

5 – Tham chiếu đến khung dữ liệu trong Máy ảo Java (JVM)

Schema for friends DynamicFrame before calling drop_fields:
root
|-- name: string
|-- age: int
|-- location: struct
|    |-- state: string
|    |-- county: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string
|    |    |-- age: int

Schema for friends DynamicFrame after removing age, county, and friend age:
root
|-- name: string
|-- location: struct
|    |-- state: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string

6 – Một đối tượng lớp GlueContext

Schema for friends DynamicFrame before calling drop_fields:
root
|-- name: string
|-- age: int
|-- location: struct
|    |-- state: string
|    |-- county: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string
|    |    |-- age: int

Schema for friends DynamicFrame after removing age, county, and friend age:
root
|-- name: string
|-- location: struct
|    |-- state: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string

7 – Chuỗi tên tùy chọn, trống theo mặc định

fromDF

Schema for friends DynamicFrame before calling drop_fields:
root
|-- name: string
|-- age: int
|-- location: struct
|    |-- state: string
|    |-- county: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string
|    |    |-- age: int

Schema for friends DynamicFrame after removing age, county, and friend age:
root
|-- name: string
|-- location: struct
|    |-- state: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string

8

Chuyển đổi một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1 thành một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 bằng cách chuyển đổi các trường

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1 thành trường

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

9. Trả về

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới

Một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

9 đại diện cho một bản ghi logic trong một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6. Nó tương tự như một hàng trong Spark

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1, ngoại trừ việc nó tự mô tả và có thể được sử dụng cho dữ liệu không tuân theo một lược đồ cố định

# Example: Use filter to create a new DynamicFrame
# with a filtered selection of records

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create DynamicFrame from Glue Data Catalog
medicare = glueContext.create_dynamic_frame.from_options(
    "s3",
    {
        "paths": [
            "s3://awsglue-datasets/examples/medicare/Medicare_Hospital_Provider.csv"
        ]
    },
    "csv",
    {"withHeader": True},
)

# Create filtered DynamicFrame with custom lambda
# to filter records by Provider State and Provider City
sac_or_mon = medicare.filter(
    f=lambda x: x["Provider State"] in ["CA", "AL"]
    and x["Provider City"] in ["SACRAMENTO", "MONTGOMERY"]
)

# Compare record counts
print("Unfiltered record count: ", medicare.count())
print("Filtered record count:  ", sac_or_mon.count())

7 – Apache Spark SQL

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1 để chuyển đổi (bắt buộc)

Schema for friends DynamicFrame before calling drop_fields:
root
|-- name: string
|-- age: int
|-- location: struct
|    |-- state: string
|    |-- county: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string
|    |    |-- age: int

Schema for friends DynamicFrame after removing age, county, and friend age:
root
|-- name: string
|-- location: struct
|    |-- state: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string

6 – Đối tượng lớp GlueContext chỉ định ngữ cảnh cho biến đổi này (bắt buộc)

Schema for friends DynamicFrame before calling drop_fields:
root
|-- name: string
|-- age: int
|-- location: struct
|    |-- state: string
|    |-- county: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string
|    |    |-- age: int

Schema for friends DynamicFrame after removing age, county, and friend age:
root
|-- name: string
|-- location: struct
|    |-- state: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string

7 – Tên của kết quả

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 (bắt buộc)

toDF

Unfiltered record count:  163065
Filtered record count:   564

2

Chuyển đổi một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 thành Apache Spark

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1 bằng cách chuyển đổi

Unfiltered record count:  163065
Filtered record count:   564

5 thành các trường

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1. Trả về

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1 mới

Một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

9 đại diện cho một bản ghi logic trong một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6. Nó tương tự như một hàng trong Spark

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1, ngoại trừ việc nó tự mô tả và có thể được sử dụng cho dữ liệu không tuân theo một lược đồ cố định

# Example: Use join to combine data from three DynamicFrames

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Load DynamicFrames from Glue Data Catalog
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
memberships = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="memberships_json"
)
orgs = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="organizations_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()
print("Schema for the memberships DynamicFrame:")
memberships.printSchema()
print("Schema for the orgs DynamicFrame:")
orgs.printSchema()

# Join persons and memberships by ID
persons_memberships = persons.join(
    paths1=["id"], paths2=["person_id"], frame2=memberships
)

# Rename and drop fields from orgs
# to prevent field name collisions with persons_memberships
orgs = (
    orgs.drop_fields(["other_names", "identifiers"])
    .rename_field("id", "org_id")
    .rename_field("name", "org_name")
)

# Create final join of all three DynamicFrames
legislators_combined = orgs.join(
    paths1=["org_id"], paths2=["organization_id"], frame2=persons_memberships
).drop_fields(["person_id", "org_id"])

# Inspect the schema for the joined data
print("Schema for the new legislators_combined DynamicFrame:")
legislators_combined.printSchema()

1 – Danh sách các tùy chọn. Chỉ định loại mục tiêu nếu bạn chọn loại hành động

# Example: Use join to combine data from three DynamicFrames

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Load DynamicFrames from Glue Data Catalog
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
memberships = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="memberships_json"
)
orgs = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="organizations_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()
print("Schema for the memberships DynamicFrame:")
memberships.printSchema()
print("Schema for the orgs DynamicFrame:")
orgs.printSchema()

# Join persons and memberships by ID
persons_memberships = persons.join(
    paths1=["id"], paths2=["person_id"], frame2=memberships
)

# Rename and drop fields from orgs
# to prevent field name collisions with persons_memberships
orgs = (
    orgs.drop_fields(["other_names", "identifiers"])
    .rename_field("id", "org_id")
    .rename_field("name", "org_name")
)

# Create final join of all three DynamicFrames
legislators_combined = orgs.join(
    paths1=["org_id"], paths2=["organization_id"], frame2=persons_memberships
).drop_fields(["person_id", "org_id"])

# Inspect the schema for the joined data
print("Schema for the new legislators_combined DynamicFrame:")
legislators_combined.printSchema()

2 và

# Example: Use join to combine data from three DynamicFrames

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Load DynamicFrames from Glue Data Catalog
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
memberships = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="memberships_json"
)
orgs = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="organizations_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()
print("Schema for the memberships DynamicFrame:")
memberships.printSchema()
print("Schema for the orgs DynamicFrame:")
orgs.printSchema()

# Join persons and memberships by ID
persons_memberships = persons.join(
    paths1=["id"], paths2=["person_id"], frame2=memberships
)

# Rename and drop fields from orgs
# to prevent field name collisions with persons_memberships
orgs = (
    orgs.drop_fields(["other_names", "identifiers"])
    .rename_field("id", "org_id")
    .rename_field("name", "org_name")
)

# Create final join of all three DynamicFrames
legislators_combined = orgs.join(
    paths1=["org_id"], paths2=["organization_id"], frame2=persons_memberships
).drop_fields(["person_id", "org_id"])

# Inspect the schema for the joined data
print("Schema for the new legislators_combined DynamicFrame:")
legislators_combined.printSchema()

3. Ví dụ bao gồm những điều sau đây

>>>toDF([ResolveOption("a.b.c", "KeepAsStruct")])
>>>toDF([ResolveOption("a.b.c", "Project", DoubleType())])

- thông tin -

đếm

# Example: Use join to combine data from three DynamicFrames

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Load DynamicFrames from Glue Data Catalog
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
memberships = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="memberships_json"
)
orgs = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="organizations_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()
print("Schema for the memberships DynamicFrame:")
memberships.printSchema()
print("Schema for the orgs DynamicFrame:")
orgs.printSchema()

# Join persons and memberships by ID
persons_memberships = persons.join(
    paths1=["id"], paths2=["person_id"], frame2=memberships
)

# Rename and drop fields from orgs
# to prevent field name collisions with persons_memberships
orgs = (
    orgs.drop_fields(["other_names", "identifiers"])
    .rename_field("id", "org_id")
    .rename_field("name", "org_name")
)

# Create final join of all three DynamicFrames
legislators_combined = orgs.join(
    paths1=["org_id"], paths2=["organization_id"], frame2=persons_memberships
).drop_fields(["person_id", "org_id"])

# Inspect the schema for the joined data
print("Schema for the new legislators_combined DynamicFrame:")
legislators_combined.printSchema()

4 – Trả về số hàng trong

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1 bên dưới

lược đồ

# Example: Use join to combine data from three DynamicFrames

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Load DynamicFrames from Glue Data Catalog
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
memberships = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="memberships_json"
)
orgs = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="organizations_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()
print("Schema for the memberships DynamicFrame:")
memberships.printSchema()
print("Schema for the orgs DynamicFrame:")
orgs.printSchema()

# Join persons and memberships by ID
persons_memberships = persons.join(
    paths1=["id"], paths2=["person_id"], frame2=memberships
)

# Rename and drop fields from orgs
# to prevent field name collisions with persons_memberships
orgs = (
    orgs.drop_fields(["other_names", "identifiers"])
    .rename_field("id", "org_id")
    .rename_field("name", "org_name")
)

# Create final join of all three DynamicFrames
legislators_combined = orgs.join(
    paths1=["org_id"], paths2=["organization_id"], frame2=persons_memberships
).drop_fields(["person_id", "org_id"])

# Inspect the schema for the joined data
print("Schema for the new legislators_combined DynamicFrame:")
legislators_combined.printSchema()

6 – Trả về giản đồ của

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 này, hoặc nếu không có sẵn, trả về giản đồ của

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1 bên dưới

in lược đồ

# Example: Use join to combine data from three DynamicFrames

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Load DynamicFrames from Glue Data Catalog
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
memberships = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="memberships_json"
)
orgs = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="organizations_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()
print("Schema for the memberships DynamicFrame:")
memberships.printSchema()
print("Schema for the orgs DynamicFrame:")
orgs.printSchema()

# Join persons and memberships by ID
persons_memberships = persons.join(
    paths1=["id"], paths2=["person_id"], frame2=memberships
)

# Rename and drop fields from orgs
# to prevent field name collisions with persons_memberships
orgs = (
    orgs.drop_fields(["other_names", "identifiers"])
    .rename_field("id", "org_id")
    .rename_field("name", "org_name")
)

# Create final join of all three DynamicFrames
legislators_combined = orgs.join(
    paths1=["org_id"], paths2=["organization_id"], frame2=persons_memberships
).drop_fields(["person_id", "org_id"])

# Inspect the schema for the joined data
print("Schema for the new legislators_combined DynamicFrame:")
legislators_combined.printSchema()

9 – In lược đồ của

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1 bên dưới

trình diễn

("`this.old.name`", "string", "thisNewName", "string")

01 – In một số hàng được chỉ định từ

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1 bên dưới

phân vùng lại

("`this.old.name`", "string", "thisNewName", "string")

03 – Trả về một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới với phân vùng

("`this.old.name`", "string", "thisNewName", "string")

05

kết hợp lại

("`this.old.name`", "string", "thisNewName", "string")

06 – Trả về một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới với phân vùng

("`this.old.name`", "string", "thisNewName", "string")

05

— biến đổi —

apply_mapping

("`this.old.name`", "string", "thisNewName", "string")

09

Áp dụng một ánh xạ khai báo cho một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 và trả về một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới với những ánh xạ đó được áp dụng cho các trường mà bạn chỉ định. Các trường không xác định được bỏ qua từ

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới

```
("`this.old.name`", "string", "thisNewName", "string")
```
13 – Danh sách các bộ dữ liệu ánh xạ (bắt buộc). Mỗi bao gồm. (cột nguồn, loại nguồn, cột đích, loại đích)
Nếu cột nguồn có dấu chấm "
```
("`this.old.name`", "string", "thisNewName", "string")
```
14" trong tên, bạn phải đánh dấu ngược "
```
("`this.old.name`", "string", "thisNewName", "string")
```
15" xung quanh cột đó. Ví dụ, để ánh xạ
```
("`this.old.name`", "string", "thisNewName", "string")
```
16 (chuỗi) tới
```
("`this.old.name`", "string", "thisNewName", "string")
```
17, bạn sẽ sử dụng bộ dữ liệu sau
```
("`this.old.name`", "string", "thisNewName", "string")
```
```
("`this.old.name`", "string", "thisNewName", "string")
```
18 – Một chuỗi duy nhất được sử dụng để xác định thông tin trạng thái (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
19 – Một chuỗi được liên kết với báo cáo lỗi cho chuyển đổi này (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
20 – Số lượng lỗi gặp phải trong quá trình chuyển đổi này mà quy trình sẽ loại bỏ lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi
```
("`this.old.name`", "string", "thisNewName", "string")
```
21 – Số lượng lỗi gặp phải cho đến và bao gồm cả quá trình chuyển đổi này mà tại đó quy trình sẽ xảy ra lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi

Ví dụ. Sử dụng apply_mapping để đổi tên trường và thay đổi loại trường

Ví dụ mã sau đây cho biết cách sử dụng phương pháp

("`this.old.name`", "string", "thisNewName", "string")

22 để đổi tên các trường đã chọn và thay đổi loại trường

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

drop_fields

("`this.old.name`", "string", "thisNewName", "string")

23

Gọi biến đổi lớp FlatMap để xóa các trường khỏi

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6. Trả về một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới với các trường được chỉ định bị loại bỏ

("`this.old.name`", "string", "thisNewName", "string")

26 – Danh sách các chuỗi. Mỗi cái chứa đường dẫn đầy đủ đến một nút trường mà bạn muốn loại bỏ. Bạn có thể sử dụng ký hiệu dấu chấm để chỉ định các trường lồng nhau. Ví dụ: nếu trường

("`this.old.name`", "string", "thisNewName", "string")

27 là con của trường

Schema for friends DynamicFrame before calling drop_fields:
root
|-- name: string
|-- age: int
|-- location: struct
|    |-- state: string
|    |-- county: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string
|    |    |-- age: int

Schema for friends DynamicFrame after removing age, county, and friend age:
root
|-- name: string
|-- location: struct
|    |-- state: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string

7 trong cây, bạn chỉ định

("`this.old.name`", "string", "thisNewName", "string")

29 cho đường dẫn

```
("`this.old.name`", "string", "thisNewName", "string")
```
18 – Một chuỗi duy nhất được sử dụng để xác định thông tin trạng thái (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
19 – Một chuỗi được liên kết với báo cáo lỗi cho chuyển đổi này (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
20 – Số lượng lỗi gặp phải trong quá trình chuyển đổi này mà quy trình sẽ loại bỏ lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi
```
("`this.old.name`", "string", "thisNewName", "string")
```
21 – Số lượng lỗi gặp phải cho đến và bao gồm cả quá trình chuyển đổi này mà tại đó quy trình sẽ xảy ra lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi

Ví dụ. Sử dụng drop_fields để xóa các trường khỏi # Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame. # Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name. # Replace EXAMPLE-FRIENDS-DATA with your table name. from pyspark.context import SparkContext from awsglue.context import GlueContext # Create GlueContext sc = SparkContext.getOrCreate() glueContext = GlueContext(sc) # Create a DynamicFrame from Glue Data Catalog glue_source_database = "MY-EXAMPLE-DATABASE" glue_source_table = "EXAMPLE-FRIENDS-DATA" friends = glueContext.create_dynamic_frame.from_catalog( database=glue_source_database, table_name=glue_source_table ) print("Schema for friends DynamicFrame before calling drop_fields:") friends.printSchema() # Remove location.county, remove friends.age, remove age friends = friends.drop_fields(paths=["age", "location.county", "friends.age"]) print("Schema for friends DynamicFrame after removing age, county, and friend age:") friends.printSchema() 6

Ví dụ về mã này sử dụng phương pháp

("`this.old.name`", "string", "thisNewName", "string")

35 để xóa các trường lồng nhau và cấp cao nhất đã chọn khỏi

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6

tập dữ liệu mẫu

Ví dụ sử dụng tập dữ liệu sau được biểu thị bằng bảng

("`this.old.name`", "string", "thisNewName", "string")

37 trong mã

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

mã ví dụ

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

Schema for friends DynamicFrame before calling drop_fields:
root
|-- name: string
|-- age: int
|-- location: struct
|    |-- state: string
|    |-- county: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string
|    |    |-- age: int

Schema for friends DynamicFrame after removing age, county, and friend age:
root
|-- name: string
|-- location: struct
|    |-- state: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string

lọc

("`this.old.name`", "string", "thisNewName", "string")

38

Trả về một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới chứa tất cả

Unfiltered record count:  163065
Filtered record count:   564

5 trong đầu vào

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 đáp ứng chức năng vị từ đã chỉ định

("`this.old.name`", "string", "thisNewName", "string")

42

("`this.old.name`", "string", "thisNewName", "string")

42 – Hàm vị từ áp dụng cho

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6. Hàm phải lấy một đối số là

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

9 và trả về True nếu

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

9 đáp ứng các yêu cầu của bộ lọc hoặc Sai nếu không (bắt buộc)

Một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

9 đại diện cho một bản ghi logic trong một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6. Nó tương tự như một hàng trong Spark

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1, ngoại trừ việc nó tự mô tả và có thể được sử dụng cho dữ liệu không tuân theo một lược đồ cố định

```
("`this.old.name`", "string", "thisNewName", "string")
```
18 – Một chuỗi duy nhất được sử dụng để xác định thông tin trạng thái (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
19 – Một chuỗi được liên kết với báo cáo lỗi cho chuyển đổi này (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
20 – Số lượng lỗi gặp phải trong quá trình chuyển đổi này mà quy trình sẽ loại bỏ lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi
```
("`this.old.name`", "string", "thisNewName", "string")
```
21 – Số lượng lỗi gặp phải cho đến và bao gồm cả quá trình chuyển đổi này mà tại đó quy trình sẽ xảy ra lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi

Ví dụ. Sử dụng bộ lọc để có được lựa chọn các trường được lọc

Ví dụ này sử dụng phương pháp

("`this.old.name`", "string", "thisNewName", "string")

54 để tạo một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới bao gồm một lựa chọn đã lọc của các trường của

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 khác

Giống như phương thức

("`this.old.name`", "string", "thisNewName", "string")

57,

("`this.old.name`", "string", "thisNewName", "string")

54 nhận một hàm làm đối số được áp dụng cho từng bản ghi trong bản gốc

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6. Hàm lấy một bản ghi làm đầu vào và trả về giá trị Boolean. Nếu giá trị trả về là true, bản ghi sẽ được đưa vào kết quả

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6. Nếu nó sai, bản ghi sẽ bị bỏ qua

# Example: Use filter to create a new DynamicFrame
# with a filtered selection of records

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create DynamicFrame from Glue Data Catalog
medicare = glueContext.create_dynamic_frame.from_options(
    "s3",
    {
        "paths": [
            "s3://awsglue-datasets/examples/medicare/Medicare_Hospital_Provider.csv"
        ]
    },
    "csv",
    {"withHeader": True},
)

# Create filtered DynamicFrame with custom lambda
# to filter records by Provider State and Provider City
sac_or_mon = medicare.filter(
    f=lambda x: x["Provider State"] in ["CA", "AL"]
    and x["Provider City"] in ["SACRAMENTO", "MONTGOMERY"]
)

# Compare record counts
print("Unfiltered record count: ", medicare.count())
print("Filtered record count:  ", sac_or_mon.count())

Unfiltered record count:  163065
Filtered record count:   564

tham gia

("`this.old.name`", "string", "thisNewName", "string")

61

Thực hiện phép nối đẳng thức với một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 khác và trả về kết quả là

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6

```
("`this.old.name`", "string", "thisNewName", "string")
```
64 – Danh sách các khóa trong khung này để tham gia
```
("`this.old.name`", "string", "thisNewName", "string")
```
65 – Danh sách các khóa trong khung khác để tham gia

("`this.old.name`", "string", "thisNewName", "string")

66 – Người còn lại

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 tham gia

```
("`this.old.name`", "string", "thisNewName", "string")
```
18 – Một chuỗi duy nhất được sử dụng để xác định thông tin trạng thái (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
19 – Một chuỗi được liên kết với báo cáo lỗi cho chuyển đổi này (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
20 – Số lượng lỗi gặp phải trong quá trình chuyển đổi này mà quy trình sẽ loại bỏ lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi
```
("`this.old.name`", "string", "thisNewName", "string")
```
21 – Số lượng lỗi gặp phải cho đến và bao gồm cả quá trình chuyển đổi này mà tại đó quy trình sẽ xảy ra lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi

Ví dụ. Sử dụng tham gia để kết hợp Schema for friends DynamicFrame before calling drop_fields: root |-- name: string |-- age: int |-- location: struct | |-- state: string | |-- county: string |-- friends: array | |-- element: struct | | |-- name: string | | |-- age: int Schema for friends DynamicFrame after removing age, county, and friend age: root |-- name: string |-- location: struct | |-- state: string |-- friends: array | |-- element: struct | | |-- name: string 2

Ví dụ này sử dụng phương pháp

("`this.old.name`", "string", "thisNewName", "string")

73 để thực hiện phép nối trên ba

Schema for friends DynamicFrame before calling drop_fields:
root
|-- name: string
|-- age: int
|-- location: struct
|    |-- state: string
|    |-- county: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string
|    |    |-- age: int

Schema for friends DynamicFrame after removing age, county, and friend age:
root
|-- name: string
|-- location: struct
|    |-- state: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string

2. AWS Glue thực hiện liên kết dựa trên các khóa trường mà bạn cung cấp. Kết quả

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 chứa các hàng từ hai khung ban đầu nơi các khóa được chỉ định khớp với nhau

Lưu ý rằng biến đổi

("`this.old.name`", "string", "thisNewName", "string")

73 giữ nguyên tất cả các trường. Điều này có nghĩa là các trường mà bạn chỉ định khớp sẽ xuất hiện trong DynamicFrame kết quả, ngay cả khi chúng dư thừa và chứa các khóa giống nhau. Trong ví dụ này, chúng tôi sử dụng

("`this.old.name`", "string", "thisNewName", "string")

35 để xóa các khóa thừa này sau khi tham gia

# Example: Use join to combine data from three DynamicFrames

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Load DynamicFrames from Glue Data Catalog
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
memberships = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="memberships_json"
)
orgs = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="organizations_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()
print("Schema for the memberships DynamicFrame:")
memberships.printSchema()
print("Schema for the orgs DynamicFrame:")
orgs.printSchema()

# Join persons and memberships by ID
persons_memberships = persons.join(
    paths1=["id"], paths2=["person_id"], frame2=memberships
)

# Rename and drop fields from orgs
# to prevent field name collisions with persons_memberships
orgs = (
    orgs.drop_fields(["other_names", "identifiers"])
    .rename_field("id", "org_id")
    .rename_field("name", "org_name")
)

# Create final join of all three DynamicFrames
legislators_combined = orgs.join(
    paths1=["org_id"], paths2=["organization_id"], frame2=persons_memberships
).drop_fields(["person_id", "org_id"])

# Inspect the schema for the joined data
print("Schema for the new legislators_combined DynamicFrame:")
legislators_combined.printSchema()

("`this.old.name`", "string", "thisNewName", "string")

0

bản đồ

("`this.old.name`", "string", "thisNewName", "string")

78

Trả về một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới là kết quả của việc áp dụng chức năng ánh xạ đã chỉ định cho tất cả các bản ghi trong bản gốc

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6

("`this.old.name`", "string", "thisNewName", "string")

42 – Chức năng lập bản đồ để áp dụng cho tất cả các bản ghi trong

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6. Hàm phải lấy một đối số là

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

9 và trả về một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

9 mới (bắt buộc)

Một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

9 đại diện cho một bản ghi logic trong một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6. Nó tương tự như một hàng trong Apache Spark

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1, ngoại trừ việc nó tự mô tả và có thể được sử dụng cho dữ liệu không tuân theo một lược đồ cố định

```
("`this.old.name`", "string", "thisNewName", "string")
```
18 – Một chuỗi duy nhất được sử dụng để xác định thông tin trạng thái (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
19 – Một chuỗi có liên quan đến lỗi trong quá trình chuyển đổi (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
20 – Số lỗi tối đa có thể xảy ra trong quá trình chuyển đổi trước khi nó bị lỗi (không bắt buộc). Mặc định là số không
```
("`this.old.name`", "string", "thisNewName", "string")
```
21 – Tổng số lỗi tối đa có thể xảy ra trước khi xử lý lỗi (tùy chọn). Mặc định là số không

Ví dụ. Sử dụng bản đồ để áp dụng một chức năng cho mọi bản ghi trong # Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame. # Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name. # Replace EXAMPLE-FRIENDS-DATA with your table name. from pyspark.context import SparkContext from awsglue.context import GlueContext # Create GlueContext sc = SparkContext.getOrCreate() glueContext = GlueContext(sc) # Create a DynamicFrame from Glue Data Catalog glue_source_database = "MY-EXAMPLE-DATABASE" glue_source_table = "EXAMPLE-FRIENDS-DATA" friends = glueContext.create_dynamic_frame.from_catalog( database=glue_source_database, table_name=glue_source_table ) print("Schema for friends DynamicFrame before calling drop_fields:") friends.printSchema() # Remove location.county, remove friends.age, remove age friends = friends.drop_fields(paths=["age", "location.county", "friends.age"]) print("Schema for friends DynamicFrame after removing age, county, and friend age:") friends.printSchema() 6

Ví dụ này cho thấy cách sử dụng phương pháp

("`this.old.name`", "string", "thisNewName", "string")

57 để áp dụng một hàm cho mọi bản ghi của một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6. Cụ thể, ví dụ này áp dụng một hàm có tên là

("`this.old.name`", "string", "thisNewName", "string")

95 cho mỗi bản ghi để hợp nhất một số trường địa chỉ thành một loại duy nhất

("`this.old.name`", "string", "thisNewName", "string")

96

("`this.old.name`", "string", "thisNewName", "string")

1

("`this.old.name`", "string", "thisNewName", "string")

2

hợp nhấtKhung động

("`this.old.name`", "string", "thisNewName", "string")

97

Hợp nhất

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 này với một dàn

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 dựa trên các khóa chính được chỉ định để xác định bản ghi. Các bản ghi trùng lặp (các bản ghi có cùng khóa chính) không được sao chép. Nếu không có bản ghi phù hợp trong khung phân loại, thì tất cả các bản ghi (bao gồm cả bản sao) sẽ được giữ lại từ nguồn. Nếu khung phân tầng có các bản ghi phù hợp, thì các bản ghi từ khung phân tầng sẽ ghi đè lên các bản ghi trong nguồn trong AWS Glue

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

00 – Dàn dựng

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 để hợp nhất

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

02 – Danh sách các trường khóa chính để khớp với các bản ghi từ nguồn và dàn khung động

```
("`this.old.name`", "string", "thisNewName", "string")
```
18 – Một chuỗi duy nhất được sử dụng để truy xuất siêu dữ liệu về chuyển đổi hiện tại (tùy chọn)

# Example: Use join to combine data from three DynamicFrames

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Load DynamicFrames from Glue Data Catalog
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
memberships = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="memberships_json"
)
orgs = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="organizations_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()
print("Schema for the memberships DynamicFrame:")
memberships.printSchema()
print("Schema for the orgs DynamicFrame:")
orgs.printSchema()

# Join persons and memberships by ID
persons_memberships = persons.join(
    paths1=["id"], paths2=["person_id"], frame2=memberships
)

# Rename and drop fields from orgs
# to prevent field name collisions with persons_memberships
orgs = (
    orgs.drop_fields(["other_names", "identifiers"])
    .rename_field("id", "org_id")
    .rename_field("name", "org_name")
)

# Create final join of all three DynamicFrames
legislators_combined = orgs.join(
    paths1=["org_id"], paths2=["organization_id"], frame2=persons_memberships
).drop_fields(["person_id", "org_id"])

# Inspect the schema for the joined data
print("Schema for the new legislators_combined DynamicFrame:")
legislators_combined.printSchema()

1 – Một chuỗi các cặp tên-giá trị JSON cung cấp thông tin bổ sung cho quá trình chuyển đổi này. Đối số này hiện không được sử dụng

("`this.old.name`", "string", "thisNewName", "string")

19 – Một

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

06. Bất kỳ chuỗi nào có liên quan đến lỗi trong quá trình chuyển đổi này

("`this.old.name`", "string", "thisNewName", "string")

20 – Một

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

08. Số lượng lỗi trong chuyển đổi nhất định mà quá trình xử lý cần loại bỏ lỗi

("`this.old.name`", "string", "thisNewName", "string")

21 – Một

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

08. Tổng số lỗi lên đến và bao gồm cả chuyển đổi này mà quá trình xử lý cần loại bỏ lỗi

Phương thức này trả về một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới thu được bằng cách hợp nhất

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 này với dàn

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 được trả về chứa bản ghi A trong những trường hợp này

Nếu

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

15 tồn tại trong cả khung nguồn và khung dàn dựng, thì

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

15 trong khung dàn dựng được trả về

Nếu

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

15 có trong bảng nguồn và

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

18 không có trong

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

19, thì

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

15 không được cập nhật trong bảng phân tầng

Khung nguồn và khung dàn dựng không cần phải có cùng một lược đồ

Ví dụ. Sử dụng mergeDynamicFrame để hợp nhất hai Schema for friends DynamicFrame before calling drop_fields: root |-- name: string |-- age: int |-- location: struct | |-- state: string | |-- county: string |-- friends: array | |-- element: struct | | |-- name: string | | |-- age: int Schema for friends DynamicFrame after removing age, county, and friend age: root |-- name: string |-- location: struct | |-- state: string |-- friends: array | |-- element: struct | | |-- name: string 2 dựa trên khóa chính

Ví dụ mã sau đây cho thấy cách sử dụng phương pháp

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

22 để hợp nhất một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 với một "dàn dựng"

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6, dựa trên khóa chính

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

25

tập dữ liệu mẫu

Ví dụ sử dụng hai

Schema for friends DynamicFrame before calling drop_fields:
root
|-- name: string
|-- age: int
|-- location: struct
|    |-- state: string
|    |-- county: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string
|    |    |-- age: int

Schema for friends DynamicFrame after removing age, county, and friend age:
root
|-- name: string
|-- location: struct
|    |-- state: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string

2 từ một

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

27 được gọi là

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

28. Sau đây là danh sách các khóa trong

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

28

("`this.old.name`", "string", "thisNewName", "string")

3

mã ví dụ

("`this.old.name`", "string", "thisNewName", "string")

4

("`this.old.name`", "string", "thisNewName", "string")

5

quan hệ hóa

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

30

Chuyển đổi một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 thành một biểu mẫu phù hợp với cơ sở dữ liệu quan hệ. Việc tương quan hóa một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 đặc biệt hữu ích khi bạn muốn di chuyển dữ liệu từ môi trường NoSQL như DynamoDB sang cơ sở dữ liệu quan hệ như MySQL

Biến đổi tạo ra một danh sách các khung bằng cách bỏ lồng các cột lồng nhau và xoay các cột mảng. Bạn có thể nối các cột mảng được xoay vòng vào bảng gốc bằng cách sử dụng khóa nối được tạo trong giai đoạn không liên kết

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

33 – Tên của bảng gốc

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

34 – Đường dẫn mà phương thức có thể lưu trữ các phân vùng của bảng tổng hợp ở định dạng CSV (tùy chọn). Các bảng tổng hợp được đọc lại từ đường dẫn này

# Example: Use join to combine data from three DynamicFrames

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Load DynamicFrames from Glue Data Catalog
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
memberships = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="memberships_json"
)
orgs = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="organizations_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()
print("Schema for the memberships DynamicFrame:")
memberships.printSchema()
print("Schema for the orgs DynamicFrame:")
orgs.printSchema()

# Join persons and memberships by ID
persons_memberships = persons.join(
    paths1=["id"], paths2=["person_id"], frame2=memberships
)

# Rename and drop fields from orgs
# to prevent field name collisions with persons_memberships
orgs = (
    orgs.drop_fields(["other_names", "identifiers"])
    .rename_field("id", "org_id")
    .rename_field("name", "org_name")
)

# Create final join of all three DynamicFrames
legislators_combined = orgs.join(
    paths1=["org_id"], paths2=["organization_id"], frame2=persons_memberships
).drop_fields(["person_id", "org_id"])

# Inspect the schema for the joined data
print("Schema for the new legislators_combined DynamicFrame:")
legislators_combined.printSchema()

1 – Từ điển các tham số tùy chọn

```
("`this.old.name`", "string", "thisNewName", "string")
```
18 – Một chuỗi duy nhất được sử dụng để xác định thông tin trạng thái (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
19 – Một chuỗi được liên kết với báo cáo lỗi cho chuyển đổi này (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
20 – Số lượng lỗi gặp phải trong quá trình chuyển đổi này mà quy trình sẽ loại bỏ lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi
```
("`this.old.name`", "string", "thisNewName", "string")
```
21 – Số lượng lỗi gặp phải cho đến và bao gồm cả quá trình chuyển đổi này mà tại đó quy trình sẽ xảy ra lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi

Ví dụ. Sử dụng quan hệ hóa để làm phẳng lược đồ lồng nhau trong # Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame. # Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name. # Replace EXAMPLE-FRIENDS-DATA with your table name. from pyspark.context import SparkContext from awsglue.context import GlueContext # Create GlueContext sc = SparkContext.getOrCreate() glueContext = GlueContext(sc) # Create a DynamicFrame from Glue Data Catalog glue_source_database = "MY-EXAMPLE-DATABASE" glue_source_table = "EXAMPLE-FRIENDS-DATA" friends = glueContext.create_dynamic_frame.from_catalog( database=glue_source_database, table_name=glue_source_table ) print("Schema for friends DynamicFrame before calling drop_fields:") friends.printSchema() # Remove location.county, remove friends.age, remove age friends = friends.drop_fields(paths=["age", "location.county", "friends.age"]) print("Schema for friends DynamicFrame after removing age, county, and friend age:") friends.printSchema() 6

Ví dụ mã này sử dụng phương thức

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

41 để làm phẳng một lược đồ lồng nhau thành một biểu mẫu phù hợp với cơ sở dữ liệu quan hệ

tập dữ liệu mẫu

Ví dụ sử dụng một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 được gọi là

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

43 với lược đồ sau.

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

43 có nhiều trường lồng nhau, chẳng hạn như

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

45,

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

46 và

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

47, sẽ được làm phẳng bởi biến đổi

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

41

("`this.old.name`", "string", "thisNewName", "string")

6

mã ví dụ

("`this.old.name`", "string", "thisNewName", "string")

7

Đầu ra sau đây cho phép bạn so sánh lược đồ của trường lồng nhau có tên là

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

47 với bảng mà biến đổi

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

41 đã tạo. Lưu ý rằng các bản ghi của bảng liên kết trở lại bảng chính bằng khóa ngoại có tên là

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

25 và cột

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

52 đại diện cho các vị trí của mảng

("`this.old.name`", "string", "thisNewName", "string")

8

đổi tên_field

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

53

Đổi tên một trường trong

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 này và trả về một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới với trường được đổi tên

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

56 – Đường dẫn đầy đủ đến nút bạn muốn đổi tên

Nếu tên cũ có dấu chấm trong đó, thì

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

57 sẽ không hoạt động trừ khi bạn đặt dấu gạch ngược xung quanh nó (_______74_______58). Ví dụ: để thay thế

("`this.old.name`", "string", "thisNewName", "string")

16 bằng

("`this.old.name`", "string", "thisNewName", "string")

17, bạn sẽ gọi rename_field như sau

("`this.old.name`", "string", "thisNewName", "string")

9

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

61 – Tên mới, dưới dạng đường dẫn đầy đủ

```
("`this.old.name`", "string", "thisNewName", "string")
```
18 – Một chuỗi duy nhất được sử dụng để xác định thông tin trạng thái (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
19 – Một chuỗi được liên kết với báo cáo lỗi cho chuyển đổi này (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
20 – Số lượng lỗi gặp phải trong quá trình chuyển đổi này mà quy trình sẽ loại bỏ lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi
```
("`this.old.name`", "string", "thisNewName", "string")
```
21 – Số lượng lỗi gặp phải cho đến và bao gồm cả quá trình chuyển đổi này mà tại đó quy trình sẽ xảy ra lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi

Ví dụ. Sử dụng rename_field để đổi tên các trường trong # Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame. # Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name. # Replace EXAMPLE-FRIENDS-DATA with your table name. from pyspark.context import SparkContext from awsglue.context import GlueContext # Create GlueContext sc = SparkContext.getOrCreate() glueContext = GlueContext(sc) # Create a DynamicFrame from Glue Data Catalog glue_source_database = "MY-EXAMPLE-DATABASE" glue_source_table = "EXAMPLE-FRIENDS-DATA" friends = glueContext.create_dynamic_frame.from_catalog( database=glue_source_database, table_name=glue_source_table ) print("Schema for friends DynamicFrame before calling drop_fields:") friends.printSchema() # Remove location.county, remove friends.age, remove age friends = friends.drop_fields(paths=["age", "location.county", "friends.age"]) print("Schema for friends DynamicFrame after removing age, county, and friend age:") friends.printSchema() 6

Ví dụ mã này sử dụng phương pháp

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

67 để đổi tên các trường trong một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6. Lưu ý rằng ví dụ sử dụng chuỗi phương thức để đổi tên nhiều trường cùng một lúc

mã ví dụ

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

0

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

1

giải quyếtLựa chọn

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

69

Giải quyết một loại lựa chọn trong

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 này và trả về

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

72 – Danh sách các điểm mơ hồ cụ thể cần giải quyết, mỗi điểm ở dạng một bộ.

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

73

Có hai cách để sử dụng

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

74. Đầu tiên là sử dụng đối số

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

72 để chỉ định một chuỗi các trường cụ thể và cách giải quyết chúng. Chế độ khác cho

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

74 là sử dụng đối số

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

77 để chỉ định một giải pháp duy nhất cho tất cả

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

78

Các giá trị cho

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

72 được chỉ định dưới dạng các bộ được tạo thành từ các cặp

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

73. Giá trị

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

81 xác định một yếu tố không rõ ràng cụ thể và giá trị

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

82 xác định độ phân giải tương ứng. Sau đây là các hành động có thể

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

83 – Cố gắng chuyển tất cả các giá trị sang loại đã chỉ định. Ví dụ.

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

84

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

85 – Chuyển đổi từng loại riêng biệt thành một cột có tên

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

86_

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

87. Nó giải quyết sự mơ hồ tiềm ẩn bằng cách làm phẳng dữ liệu. Ví dụ: nếu

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

88 có thể là một

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

89 hoặc một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

5, thì giải pháp sẽ là tạo ra hai cột có tên là

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

91 và

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

92 trong kết quả là

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

94 – Giải quyết sự mơ hồ tiềm ẩn bằng cách sử dụng

("`this.old.name`", "string", "thisNewName", "string")

96 để biểu thị dữ liệu. Ví dụ: nếu dữ liệu trong một cột có thể là một

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

89 hoặc một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

5, thì hành động

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

94 sẽ tạo ra một cột gồm các cấu trúc trong kết quả là

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6. Mỗi cấu trúc chứa cả một

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

89 và một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

5

project:________74_______87 – Giải quyết sự mơ hồ tiềm ẩn bằng cách chiếu tất cả dữ liệu vào một trong các loại dữ liệu có thể. Ví dụ: nếu dữ liệu trong một cột có thể là một

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

89 hoặc một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

5, thì việc sử dụng một hành động

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

05 sẽ tạo ra một cột trong kết quả là

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 trong đó tất cả các giá trị

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

89 đã được chuyển đổi thành chuỗi

Nếu

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

81 xác định một mảng, hãy đặt dấu ngoặc vuông trống sau tên của mảng để tránh nhầm lẫn. Ví dụ: giả sử bạn đang làm việc với dữ liệu có cấu trúc như sau

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

2

Bạn có thể chọn phiên bản số thay vì chuỗi của giá bằng cách đặt

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

81 thành

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

10 và đặt

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

82 thành

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

12

Bạn chỉ có thể sử dụng một trong các tham số

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

72 và

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

77. Nếu tham số

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

72 không phải là

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

16, thì tham số

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

77 phải là một chuỗi rỗng. Ngược lại, nếu

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

77 không phải là một chuỗi rỗng, thì tham số

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

72 phải là

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

16

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

77 – Chỉ định một giải pháp duy nhất cho tất cả

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

78. Bạn có thể sử dụng điều này trong trường hợp không biết danh sách đầy đủ của

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

78 trước thời gian chạy. Ngoài các hành động được liệt kê trước đó cho

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

72, lập luận này cũng hỗ trợ cho hành động sau

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

25 – Cố gắng chuyển từng

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

26 sang loại tương ứng trong bảng Danh mục dữ liệu đã chỉ định

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

27 – Cơ sở dữ liệu Danh mục dữ liệu để sử dụng với hành động

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

25

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

29 – Bảng Danh mục dữ liệu để sử dụng với hành động

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

25

```
("`this.old.name`", "string", "thisNewName", "string")
```
18 – Một chuỗi duy nhất được sử dụng để xác định thông tin trạng thái (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
19 – Một chuỗi được liên kết với báo cáo lỗi cho chuyển đổi này (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
20 – Số lượng lỗi gặp phải trong quá trình chuyển đổi này mà quy trình sẽ loại bỏ lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi
```
("`this.old.name`", "string", "thisNewName", "string")
```
21 – Số lượng lỗi gặp phải cho đến và bao gồm cả quá trình chuyển đổi này mà tại đó quy trình sẽ xảy ra lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

35 – ID danh mục của Danh mục dữ liệu đang được truy cập (ID tài khoản của Danh mục dữ liệu). Khi được đặt thành

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

16 (giá trị mặc định), nó sẽ sử dụng ID danh mục của tài khoản gọi điện

Ví dụ. Sử dụng giải quyếtChoice để xử lý một cột chứa nhiều loại

Ví dụ mã này sử dụng phương pháp

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

74 để chỉ định cách xử lý cột

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 chứa các giá trị thuộc nhiều loại. Ví dụ minh họa hai cách phổ biến để xử lý một cột với các loại khác nhau

Truyền cột thành một kiểu dữ liệu
Giữ lại tất cả các loại trong các cột riêng biệt

tập dữ liệu mẫu

Ví dụ sử dụng một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 được gọi là

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

40 với lược đồ sau

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

3

mã ví dụ

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

4

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

5

select_fields

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

41

Trả về một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới chứa các trường đã chọn

```
("`this.old.name`", "string", "thisNewName", "string")
```
26 – Danh sách các chuỗi. Mỗi chuỗi là một đường dẫn đến nút cấp cao nhất mà bạn muốn chọn
```
("`this.old.name`", "string", "thisNewName", "string")
```
18 – Một chuỗi duy nhất được sử dụng để xác định thông tin trạng thái (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
19 – Một chuỗi được liên kết với báo cáo lỗi cho chuyển đổi này (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
20 – Số lượng lỗi gặp phải trong quá trình chuyển đổi này mà quy trình sẽ loại bỏ lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi
```
("`this.old.name`", "string", "thisNewName", "string")
```
21 – Số lượng lỗi gặp phải cho đến và bao gồm cả quá trình chuyển đổi này mà tại đó quy trình sẽ xảy ra lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi

Ví dụ. Sử dụng select_fields để tạo một # Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame. # Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name. # Replace EXAMPLE-FRIENDS-DATA with your table name. from pyspark.context import SparkContext from awsglue.context import GlueContext # Create GlueContext sc = SparkContext.getOrCreate() glueContext = GlueContext(sc) # Create a DynamicFrame from Glue Data Catalog glue_source_database = "MY-EXAMPLE-DATABASE" glue_source_table = "EXAMPLE-FRIENDS-DATA" friends = glueContext.create_dynamic_frame.from_catalog( database=glue_source_database, table_name=glue_source_table ) print("Schema for friends DynamicFrame before calling drop_fields:") friends.printSchema() # Remove location.county, remove friends.age, remove age friends = friends.drop_fields(paths=["age", "location.county", "friends.age"]) print("Schema for friends DynamicFrame after removing age, county, and friend age:") friends.printSchema() 6 mới với các trường đã chọn

Ví dụ mã sau đây cho thấy cách sử dụng phương pháp

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

49 để tạo một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới với một danh sách các trường đã chọn từ một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 hiện có

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

6

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

7

cái vòi

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

52

Ghi các bản ghi mẫu vào một đích cụ thể để giúp bạn xác minh các chuyển đổi được thực hiện bởi công việc của bạn

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

53 – Đường dẫn của điểm đến để viết thư (bắt buộc)

# Example: Use join to combine data from three DynamicFrames

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Load DynamicFrames from Glue Data Catalog
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
memberships = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="memberships_json"
)
orgs = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="organizations_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()
print("Schema for the memberships DynamicFrame:")
memberships.printSchema()
print("Schema for the orgs DynamicFrame:")
orgs.printSchema()

# Join persons and memberships by ID
persons_memberships = persons.join(
    paths1=["id"], paths2=["person_id"], frame2=memberships
)

# Rename and drop fields from orgs
# to prevent field name collisions with persons_memberships
orgs = (
    orgs.drop_fields(["other_names", "identifiers"])
    .rename_field("id", "org_id")
    .rename_field("name", "org_name")
)

# Create final join of all three DynamicFrames
legislators_combined = orgs.join(
    paths1=["org_id"], paths2=["organization_id"], frame2=persons_memberships
).drop_fields(["person_id", "org_id"])

# Inspect the schema for the joined data
print("Schema for the new legislators_combined DynamicFrame:")
legislators_combined.printSchema()

1 – Các cặp khóa-giá trị chỉ định các tùy chọn (tùy chọn). Tùy chọn

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

55 xác định rằng bản ghi

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

56 đầu tiên phải được ghi. Tùy chọn

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

57 chỉ định xác suất (dưới dạng số thập phân) của việc chọn bất kỳ bản ghi đã cho nào. Bạn có thể sử dụng nó trong việc chọn bản ghi để viết

```
("`this.old.name`", "string", "thisNewName", "string")
```
18 – Một chuỗi duy nhất được sử dụng để xác định thông tin trạng thái (tùy chọn)

Ví dụ. Sử dụng đầu nối để ghi các trường mẫu từ # Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame. # Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name. # Replace EXAMPLE-FRIENDS-DATA with your table name. from pyspark.context import SparkContext from awsglue.context import GlueContext # Create GlueContext sc = SparkContext.getOrCreate() glueContext = GlueContext(sc) # Create a DynamicFrame from Glue Data Catalog glue_source_database = "MY-EXAMPLE-DATABASE" glue_source_table = "EXAMPLE-FRIENDS-DATA" friends = glueContext.create_dynamic_frame.from_catalog( database=glue_source_database, table_name=glue_source_table ) print("Schema for friends DynamicFrame before calling drop_fields:") friends.printSchema() # Remove location.county, remove friends.age, remove age friends = friends.drop_fields(paths=["age", "location.county", "friends.age"]) print("Schema for friends DynamicFrame after removing age, county, and friend age:") friends.printSchema() 6 lên Amazon S3

Ví dụ về mã này sử dụng phương thức

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

60 để ghi các bản ghi mẫu vào bộ chứa Amazon S3 sau khi áp dụng biến đổi

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

49

tập dữ liệu mẫu

Ví dụ sử dụng một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 được gọi là

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

63 với lược đồ sau

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

8

mã ví dụ

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

9

Sau đây là ví dụ về dữ liệu mà

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

60 ghi vào Amazon S3. Vì mã ví dụ đã chỉ định

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

65, dữ liệu mẫu chứa 10 bản ghi đầu tiên

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

0

split_fields

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

66

Trả về một

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

27 mới chứa hai

Schema for friends DynamicFrame before calling drop_fields:
root
|-- name: string
|-- age: int
|-- location: struct
|    |-- state: string
|    |-- county: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string
|    |    |-- age: int

Schema for friends DynamicFrame after removing age, county, and friend age:
root
|-- name: string
|-- location: struct
|    |-- state: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string

2.

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 đầu tiên chứa tất cả các nút đã được tách ra và nút thứ hai chứa các nút còn lại

("`this.old.name`", "string", "thisNewName", "string")

26 – Một danh sách các chuỗi, mỗi chuỗi là một đường dẫn đầy đủ đến một nút mà bạn muốn tách thành một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

72 – Một chuỗi tên cho

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 được tách ra

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

74 – Một chuỗi tên cho

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 còn lại sau khi các nút được chỉ định đã được tách ra

```
("`this.old.name`", "string", "thisNewName", "string")
```
18 – Một chuỗi duy nhất được sử dụng để xác định thông tin trạng thái (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
19 – Một chuỗi được liên kết với báo cáo lỗi cho chuyển đổi này (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
20 – Số lượng lỗi gặp phải trong quá trình chuyển đổi này mà quy trình sẽ loại bỏ lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi
```
("`this.old.name`", "string", "thisNewName", "string")
```
21 – Số lượng lỗi gặp phải cho đến và bao gồm cả quá trình chuyển đổi này mà tại đó quy trình sẽ xảy ra lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi

Ví dụ. Sử dụng split_fields để chia các trường đã chọn thành một # Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame. # Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name. # Replace EXAMPLE-FRIENDS-DATA with your table name. from pyspark.context import SparkContext from awsglue.context import GlueContext # Create GlueContext sc = SparkContext.getOrCreate() glueContext = GlueContext(sc) # Create a DynamicFrame from Glue Data Catalog glue_source_database = "MY-EXAMPLE-DATABASE" glue_source_table = "EXAMPLE-FRIENDS-DATA" friends = glueContext.create_dynamic_frame.from_catalog( database=glue_source_database, table_name=glue_source_table ) print("Schema for friends DynamicFrame before calling drop_fields:") friends.printSchema() # Remove location.county, remove friends.age, remove age friends = friends.drop_fields(paths=["age", "location.county", "friends.age"]) print("Schema for friends DynamicFrame after removing age, county, and friend age:") friends.printSchema() 6 riêng biệt

Ví dụ về mã này sử dụng phương pháp

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

81 để tách danh sách các trường được chỉ định thành một ___________6 riêng biệt

tập dữ liệu mẫu

Ví dụ sử dụng một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 được gọi là

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

84 từ một bộ sưu tập có tên là

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

85

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

84 có lược đồ và các mục sau

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

1

mã ví dụ

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

2

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

3

split_rows

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

87

Tách một hoặc nhiều hàng trong một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 thành một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới

Phương thức trả về một

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

27 mới chứa hai

Schema for friends DynamicFrame before calling drop_fields:
root
|-- name: string
|-- age: int
|-- location: struct
|    |-- state: string
|    |-- county: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string
|    |    |-- age: int

Schema for friends DynamicFrame after removing age, county, and friend age:
root
|-- name: string
|-- location: struct
|    |-- state: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string

2.

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 đầu tiên chứa tất cả các hàng đã được tách ra và cái thứ hai chứa các hàng còn lại

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

93 – Một từ điển trong đó khóa là đường dẫn đến một cột và giá trị là một từ điển khác để ánh xạ bộ so sánh tới các giá trị mà các giá trị cột được so sánh với. Ví dụ:

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

94 tách tất cả các hàng có giá trị trong cột tuổi lớn hơn 10 và nhỏ hơn 20

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

72 – Một chuỗi tên cho

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 được tách ra

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

74 – Một chuỗi tên cho

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 còn lại sau khi các nút được chỉ định đã được tách ra

```
("`this.old.name`", "string", "thisNewName", "string")
```
18 – Một chuỗi duy nhất được sử dụng để xác định thông tin trạng thái (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
19 – Một chuỗi được liên kết với báo cáo lỗi cho chuyển đổi này (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
20 – Số lượng lỗi gặp phải trong quá trình chuyển đổi này mà quy trình sẽ loại bỏ lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi
```
("`this.old.name`", "string", "thisNewName", "string")
```
21 – Số lượng lỗi gặp phải cho đến và bao gồm cả quá trình chuyển đổi này mà tại đó quy trình sẽ xảy ra lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi

Ví dụ. Sử dụng split_rows để chia các hàng trong # Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame. # Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name. # Replace EXAMPLE-FRIENDS-DATA with your table name. from pyspark.context import SparkContext from awsglue.context import GlueContext # Create GlueContext sc = SparkContext.getOrCreate() glueContext = GlueContext(sc) # Create a DynamicFrame from Glue Data Catalog glue_source_database = "MY-EXAMPLE-DATABASE" glue_source_table = "EXAMPLE-FRIENDS-DATA" friends = glueContext.create_dynamic_frame.from_catalog( database=glue_source_database, table_name=glue_source_table ) print("Schema for friends DynamicFrame before calling drop_fields:") friends.printSchema() # Remove location.county, remove friends.age, remove age friends = friends.drop_fields(paths=["age", "location.county", "friends.age"]) print("Schema for friends DynamicFrame after removing age, county, and friend age:") friends.printSchema() 6

Ví dụ về mã này sử dụng phương pháp

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

04 để phân chia các hàng trong một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 dựa trên giá trị trường

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

25

tập dữ liệu mẫu

Ví dụ sử dụng một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 có tên là

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

84 được chọn từ một bộ sưu tập có tên là

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

85

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

84 có lược đồ và các mục sau

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

4

mã ví dụ

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

5

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

6

mở hộp

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

11

Bỏ hộp (định dạng lại) một trường chuỗi trong một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 và trả về một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới có chứa

Unfiltered record count:  163065
Filtered record count:   564

5 đã được mở hộp

Một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

9 đại diện cho một bản ghi logic trong một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6. Nó tương tự như một hàng trong Apache Spark

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1, ngoại trừ việc nó tự mô tả và có thể được sử dụng cho dữ liệu không tuân theo một lược đồ cố định

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

53 – Đường dẫn đầy đủ đến nút chuỗi mà bạn muốn mở hộp

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

19 – Đặc tả định dạng (tùy chọn). Bạn sử dụng điều này cho kết nối Amazon S3 hoặc AWS Glue hỗ trợ nhiều định dạng. Để biết các định dạng được hỗ trợ, hãy xem Tùy chọn định dạng dữ liệu cho đầu vào và đầu ra trong AWS Glue

```
("`this.old.name`", "string", "thisNewName", "string")
```
18 – Một chuỗi duy nhất được sử dụng để xác định thông tin trạng thái (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
19 – Một chuỗi được liên kết với báo cáo lỗi cho chuyển đổi này (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
20 – Số lượng lỗi gặp phải trong quá trình chuyển đổi này mà quy trình sẽ loại bỏ lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi
```
("`this.old.name`", "string", "thisNewName", "string")
```
21 – Số lượng lỗi gặp phải cho đến và bao gồm cả quá trình chuyển đổi này mà tại đó quy trình sẽ xảy ra lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi

# Example: Use join to combine data from three DynamicFrames

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Load DynamicFrames from Glue Data Catalog
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
memberships = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="memberships_json"
)
orgs = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="organizations_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()
print("Schema for the memberships DynamicFrame:")
memberships.printSchema()
print("Schema for the orgs DynamicFrame:")
orgs.printSchema()

# Join persons and memberships by ID
persons_memberships = persons.join(
    paths1=["id"], paths2=["person_id"], frame2=memberships
)

# Rename and drop fields from orgs
# to prevent field name collisions with persons_memberships
orgs = (
    orgs.drop_fields(["other_names", "identifiers"])
    .rename_field("id", "org_id")
    .rename_field("name", "org_name")
)

# Create final join of all three DynamicFrames
legislators_combined = orgs.join(
    paths1=["org_id"], paths2=["organization_id"], frame2=persons_memberships
).drop_fields(["person_id", "org_id"])

# Inspect the schema for the joined data
print("Schema for the new legislators_combined DynamicFrame:")
legislators_combined.printSchema()

1 – Một hoặc nhiều điều sau đây

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

25 – Một chuỗi chứa ký tự ngăn cách

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

26 – Một chuỗi chứa ký tự thoát

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

27 – Một giá trị Boolean cho biết có nên bỏ qua phiên bản đầu tiên hay không

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

28 – Một chuỗi chứa lược đồ. Bạn phải gọi nó bằng cách sử dụng

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

29

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

30 – Giá trị Boolean cho biết liệu tiêu đề có được bao gồm hay không

Ví dụ. Sử dụng unbox để bỏ hộp một trường chuỗi thành một cấu trúc

Ví dụ mã này sử dụng phương pháp

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

31 để mở hộp hoặc định dạng lại trường chuỗi trong

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 thành trường kiểu cấu trúc

tập dữ liệu mẫu

Ví dụ sử dụng một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 được gọi là

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

34 với lược đồ và các mục nhập sau

Lưu ý trường có tên

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

35. Đây là trường mà ví dụ mở hộp thành một cấu trúc

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

7

mã ví dụ

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

8

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date

9

không hợp nhau

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

36

Bỏ lồng các đối tượng lồng nhau trong một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6, làm cho chúng trở thành các đối tượng cấp cao nhất và trả về một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới chưa được lồng

```
("`this.old.name`", "string", "thisNewName", "string")
```
18 – Một chuỗi duy nhất được sử dụng để xác định thông tin trạng thái (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
19 – Một chuỗi được liên kết với báo cáo lỗi cho chuyển đổi này (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
20 – Số lượng lỗi gặp phải trong quá trình chuyển đổi này mà quy trình sẽ loại bỏ lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi
```
("`this.old.name`", "string", "thisNewName", "string")
```
21 – Số lượng lỗi gặp phải cho đến và bao gồm cả quá trình chuyển đổi này mà tại đó quy trình sẽ xảy ra lỗi (tùy chọn). Giá trị mặc định là 0, điều này cho biết rằng quá trình sẽ không xảy ra lỗi

Ví dụ. Sử dụng unnest để biến các trường lồng nhau thành các trường cấp cao nhất

Ví dụ mã này sử dụng phương pháp

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

43 để làm phẳng tất cả các trường lồng nhau trong một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 thành các trường cấp cao nhất

tập dữ liệu mẫu

Ví dụ sử dụng một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 được gọi là

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

46 với lược đồ sau. Lưu ý rằng trường

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

47 là trường duy nhất chứa dữ liệu lồng nhau

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

0

mã ví dụ

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

1

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

2

unnest_ddb_json

Bỏ lồng các cột lồng nhau trong một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 cụ thể trong cấu trúc JSON của DynamoDB và trả về một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới chưa lồng. Các cột thuộc một mảng các kiểu cấu trúc sẽ không được bỏ lồng. Lưu ý rằng đây là một loại biến đổi hủy lồng cụ thể hoạt động khác với biến đổi

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

43 thông thường và yêu cầu dữ liệu phải có sẵn trong cấu trúc JSON của DynamoDB. Để biết thêm thông tin, xem

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

51

```
("`this.old.name`", "string", "thisNewName", "string")
```
18 – Một chuỗi duy nhất được sử dụng để xác định thông tin trạng thái (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
19 – Một chuỗi được liên kết với báo cáo lỗi cho chuyển đổi này (tùy chọn)
```
("`this.old.name`", "string", "thisNewName", "string")
```
20 – Số lượng lỗi gặp phải trong quá trình chuyển đổi này mà tại đó quy trình sẽ loại bỏ lỗi (tùy chọn. 0 theo mặc định, cho biết rằng quy trình sẽ không bị lỗi)
```
("`this.old.name`", "string", "thisNewName", "string")
```
21 – Số lượng lỗi gặp phải cho đến và bao gồm cả quá trình chuyển đổi này mà tại đó quy trình sẽ loại bỏ lỗi (tùy chọn. 0 theo mặc định, cho biết rằng quy trình sẽ không bị lỗi)

Ví dụ: lược đồ đọc một bản xuất có cấu trúc JSON của DynamoDB có thể giống như sau

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

3

Biến đổi

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

56 sẽ chuyển đổi cái này thành

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

4

Ví dụ mã sau đây cho biết cách sử dụng trình kết nối xuất AWS Glue DynamoDB, gọi một JSON DynamoDB không hợp lệ và in số lượng phân vùng

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

5

viết

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

57

Nhận một loại kết nối được chỉ định từ lớp GlueContext của

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 này và sử dụng nó để định dạng và viết nội dung của

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 này. Trả về

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 mới được định dạng và viết như đã chỉ định

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

61 – Loại kết nối sẽ sử dụng. Các giá trị hợp lệ bao gồm

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

62,

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

63,

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

64,

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

65,

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

66 và

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

67

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

68 – Tùy chọn kết nối để sử dụng (tùy chọn). Đối với một

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

61 của

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

62, một đường dẫn Amazon S3 được xác định

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

6

Đối với các kết nối JDBC, một số thuộc tính phải được xác định. Lưu ý rằng tên cơ sở dữ liệu phải là một phần của URL. Nó có thể tùy chọn được bao gồm trong các tùy chọn kết nối

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

7

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

19 – Đặc tả định dạng (tùy chọn). Điều này được sử dụng cho Dịch vụ lưu trữ đơn giản của Amazon (Amazon S3) hoặc kết nối AWS Glue hỗ trợ nhiều định dạng. Xem Tùy chọn định dạng dữ liệu cho đầu vào và đầu ra trong AWS Glue để biết các định dạng được hỗ trợ

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

72 – Tùy chọn định dạng cho định dạng được chỉ định. Xem Tùy chọn định dạng dữ liệu cho đầu vào và đầu ra trong AWS Glue để biết các định dạng được hỗ trợ

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

73 – Kích thước có thể tích lũy để sử dụng (tùy chọn)

— lỗi —

khẳng địnhErrorThreshold

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

74 – Khẳng định về lỗi trong các phép biến đổi đã tạo ra điều này

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6. Trả về một

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

76 từ cơ sở

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

1

lỗiAsDynamicKhung

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

78 – Trả về một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 có các bản ghi lỗi được lồng bên trong

Ví dụ. Sử dụng errorAsDynamicFrame để xem các bản ghi lỗi

Ví dụ mã sau đây cho thấy cách sử dụng phương thức

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

80 để xem bản ghi lỗi cho một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6

tập dữ liệu mẫu

Ví dụ sử dụng tập dữ liệu sau mà bạn có thể tải lên Amazon S3 dưới dạng JSON. Lưu ý rằng bản ghi thứ hai không đúng định dạng. Dữ liệu không đúng định dạng thường phá vỡ quá trình phân tích cú pháp tệp khi bạn sử dụng SparkSQL. Tuy nhiên,

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 nhận ra các vấn đề về dị tật và chuyển các dòng không đúng định dạng thành các bản ghi lỗi mà bạn có thể xử lý riêng lẻ

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

8

mã ví dụ

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

9

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

0

lỗiĐếm

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

83 – Trả về tổng số lỗi trong một

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6

giai đoạnLỗiĐếm

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

85 – Trả về số lượng lỗi đã xảy ra trong quá trình tạo

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

6 này

Làm cách nào để xóa các thẻ HTML trong SQL?

Chúng tôi đang sử dụng hàm CHARINDEX của SQL Server để lấy các vị trí của ' với khoảng trống sử dụng hàm STUFF . Chúng tôi đang sử dụng Vòng lặp WHILE sẽ chạy cho đến khi có '

Làm cách nào để xóa các thẻ HTML?

Có thể xóa các thẻ HTML khỏi một chuỗi đã cho bằng cách sử dụng phương thức replaceAll() của lớp String . Chúng tôi có thể xóa các thẻ HTML khỏi một chuỗi nhất định bằng cách sử dụng biểu thức chính quy. Sau khi xóa các thẻ HTML khỏi một chuỗi, nó sẽ trả về một chuỗi dưới dạng văn bản bình thường.

Làm cách nào để xóa thẻ HTML khỏi DataFrame của gấu trúc?

str. replace(' ', '') # Sử dụng regex để xóa thẻ html.

Làm cách nào để xóa tất cả các thẻ HTML khỏi chuỗi trong C#?

By using Regex: public static string RemoveHTMLTags(string html) { return Regex.Replace(html, "<.*?>", string.Empty); } .. .

Bằng cách sử dụng Regex đã biên dịch để có hiệu suất tốt hơn. .

Bằng cách sử dụng Char Array để có hiệu suất nhanh hơn cho một số tệp HTML

programming html

Spark sql xóa thẻ html

- sự thi công -

__trong đó__

fromDF

toDF

- thông tin -

đếm

lược đồ

in lược đồ

trình diễn

phân vùng lại

kết hợp lại

— biến đổi —

apply_mapping

Ví dụ. Sử dụng apply_mapping để đổi tên trường và thay đổi loại trường

drop_fields

lọc

Ví dụ. Sử dụng bộ lọc để có được lựa chọn các trường được lọc

tham gia

bản đồ

hợp nhấtKhung động

quan hệ hóa

đổi tên_field

giải quyếtLựa chọn

Ví dụ. Sử dụng giải quyếtChoice để xử lý một cột chứa nhiều loại

select_fields

cái vòi

split_fields

split_rows

mở hộp

Ví dụ. Sử dụng unbox để bỏ hộp một trường chuỗi thành một cấu trúc

không hợp nhau

Ví dụ. Sử dụng unnest để biến các trường lồng nhau thành các trường cấp cao nhất

unnest_ddb_json

viết

— lỗi —

khẳng địnhErrorThreshold

lỗiAsDynamicKhung

Ví dụ. Sử dụng errorAsDynamicFrame để xem các bản ghi lỗi

lỗiĐếm

giai đoạnLỗiĐếm

Làm cách nào để xóa các thẻ HTML trong SQL?

Làm cách nào để xóa các thẻ HTML?

Làm cách nào để xóa thẻ HTML khỏi DataFrame của gấu trúc?

Làm cách nào để xóa tất cả các thẻ HTML khỏi chuỗi trong C#?

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