What is index in mongodb schema?
for more details see this documentation . Don't be confused after reading this document because this document uses If you have no data in your database then it will work fine
but if you have some data in the database with duplicate
items then you should use But one thing you should know before use Like: IndexesIndexes are defined through DefinitionRegular indexes:
Sparse indexes:
Unique indexes:
Unique sparse indexes:
Compound indexes are defined on
the
Indexes are key to achieving high performance in MongoDB, as they allow the database to search through less documents to satisfy a query. Without an index MongoDB has to scan through all of the documents in a collection to fulfill the query. An index increases the amount of storage needed to represent a document and the time it takes to insert a document. Indexes trade off faster queries against storage space. One of the principal things to remember about indexes, is that they are tip
MongoDB has several types of indexes
It also supports a couple of variations of the above indexes
Single field indexesTake the following document
Figure: An example document Let’s look at some different ways we can apply a single field index
Example 1: A single field index This indexes the name field in ascending order.
Example 2: A single embedded field index This indexes the
Example 3: A embedded document field index This indexes the
Example 4: Embedded document match Compound indexesA compound index is an index that contains references to multiple fields within a document.
Example 5: Create compound index The compound indexes have some interesting properties. The index is usable if you have a query that includes nationality, age and name. But it’s also able to answer other queries using the index.
For compound indexes order matters as we match from left to right. For example if you reverse a query to start with name and age, it will not match the order of fields in the compound index, and MongoDB will not be able to use the index to speed up the query. tip
Always make sure the order of fields in a compound index matches the order of fields in the queries you want to execute against the collection. One thing to note about using a compound index is sorting. The ordering and direction of the fields in a compound index not only decide if it’s possible to use the index in the query, but are also used for sorting. Given the index
Example 6: Create compound index Sorting can only use the index if they match the order specified or the exact reverse order specified. Multi key indexesMulti key indexes lets MongoDB index arrays of values. Take the following example document.
Figure: Sample document Multi key indexes lets us search on the values in the
Example 7: Create Multikey fields The two indexes lets you do exact matches on values in the
Example 8: Query using Multikey indexes The first query will use the index on If the document changes structure over time, it would be better to create a specific compound index on the fields needed in the embedded documents. A better index would be:
Example 9: Create Embedded Array Compound Index To leverage the index, the second query can be written as follows.
Example 10: Query using the Compound Multikey index Geo-spatial indexesMongoDB offers several Geo-spatial index types. These indexes make it possible to perform efficient Geo-spatial queries. importantGEO JSON MongoDB supports GEO JSON (http://geojson.org) for performing GEO location queries. Specialized 2d Sphere indexThe 2d Geo-spatial Sphere index, allows applications to perform queries on an earth-like sphere, making for higher accuracy in matching locations. Take the following example document.
Figure: A Simple document with location information Create a 2dsphere index.
Example 11: Create 2dsphere compound index Query the index using a GEO JSON type.
Example 12: Query using 2dsphere index important
The 2d sphere index is a pure GeoSpatial index and is limited to the ranges for latitude (-90 to 90) and longitude (-180 to 180). It also only accepts General 2d indexThe 2d index is a flat index that does not take into consideration any projection. One of the benefits of the 2d index is that it allows for fixed lower and upper bounds for the coordinate system as well as the search resolution. This makes for a more general 2d index. Let’s add a sample document.
Figure: A Simple document with location information Notice that the Let’s create a 2d index.
Example 13: Create 2d index Now let’s look for all houses that fall inside the range of 2000 to 20000 in price and have 0 to 5 rooms.
Example 14: Query for matching houses tip
The Text indexesText search is integrated into the MongoDB query language from version 2.6 (In 2.4 it was available as beta command). It relies on an underlying text index. Let’s insert some sample documents.
Figure: Insert some sample documents Let’s create the text index.
Example 14: Create text index The example shows how weights can be used to control the weighing of fields in subsequent queries against the index. In
this case any search that matches We also passed a The The option Now let’s query for all the blog entries that contain the
Example 15: Query using text index The query matches all the documents, that contain the word
Example 16: Query using text index The query includes the score given to the individual matched documents and sorts them in descending order by the important
Text indexes can grow to be bigger than the actual stored documents, and can take a while to build if the collection is big. They also add additional overhead to writes, compared to simpler indexes. Sparse indexesSparse indexes are indexes, where no values are included for fields that do not exist. Look at the following two documents.
Figure: Sample documents A non-sparse index for the field To create a sparse index:
Example 17: Create Sparse Index Unique indexesAn unique index is different from a normal index in that it only allows a single document to exist for a field value.
Example 18: Create Unique Index Now let’s try to insert some documents
Figure: Sample documents The second insert fails, due to the existing document with the field Time To Live indexesTime to live indexes (TTL) are a special type of index that will remove documents that fail to meet the index condition. One use for TTL indexes is to model a cache where documents expire after a set amount of time, allowing old documents to be gradually removed by MongoDB. This avoids the need of performing bulk deletes of documents using an external application or script. Let’s insert some documents.
Figure: Sample documents Let’s define an a TTL index on
Example 19: Create TTL index The TTL will delete any documents where the important
The It’s important to note that Time to Live indexes only with Date based fields. Covered Index QueriesCovered index queries are queries that can be answered using only the information stored in the index. Basically MongoDB answers the query using the fields stored in an index, never actually materializing the document into memory. Let’s look at an example usage of covered queries.
Figure: Sample documents Let’s define the covered index first.
Example 20: Create Covered index Next perform a covered index query.
Example 21: Query using Covered Index Let’s retrieve the query plan.
Example 22: Query with Explain This returns a result with all the details about the query plan executed for this query.
Figure: Query plan example, field removed for brevity Notice how the query plan result includes important
Notice how What is an index schema?Index schema files define the attribute structure of entities in an index, including their name, data type, supported operations and synonyms. They play a critical part in the design of a MAKES API: They serve as the blueprint for defining entity structure in entity data files used to build an index.
How do I create an index in MongoDB schema?Following command can be used to create compound index for nested json: db. ACCOUNT_collection. createIndex({"account.id":1,"account. customerId":1},{unique:1}) Mongo json structure is like : {"_id":"648738" "account": { "id": "123", "customerId": 7879, "name": "test" .. .. } }
Where is index in MongoDB collection?You can find all the available indexes in a MongoDB collection by using the getIndexes method. This will return all the indexes in a specific collection. Result: The output contains the default _id index and the user-created index student name index.
How does MongoDB determine index?here are the best practices to help you.. Indexes in MongoDB. ... . Use Compound Indexes. ... . Follow the ESR rule. ... . Use Covered Queries When Possible. ... . Use Caution When Considering Indexes on Low-Cardinality Fields. ... . Eliminate Unnecessary Indexes. ... . Wildcard Indexes Are Not a Replacement for Workload-Based Index Planning.. |