Hướng dẫn dùng tableau extract trong PHP
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Extracts are saved subsets of data that you can use to improve performance or to take advantage of Tableau functionality not available or supported in your original data. When you create an extract of your data, you can reduce the total amount of data by using filters and configuring other limits. After you create an extract, you can refresh it with data from the original data. When refreshing the data, you have the option to either do a full refresh, which replaces all of the contents in the extract, or you can do an incremental refresh, which only adds rows that are new since the previous refresh. Show Extracts are advantageous for several reasons:
Beginning with version 2020.4, extracts are available in web authoring and content server. Now, you no longer have to use Tableau Desktop to extract your data sources. For more information, see Create Extracts on the Web. Logical and physical table extractsWith the introduction of logical tables and physical tables in the Tableau data model in version 2020.2, extract storage options have changed from Single Table and Multiple Tables, to Logical Tables and Physical Tables. These options better describe how extracts will be stored. For more information, see Decide how the extract data should be stored. Beginning with version 10.5, when you create a new extract it uses the .hyper format. Extracts in the .hyper format take advantage of the improved data engine, which supports faster analytical and query performance for larger data sets. Similarly, when an extract-related task is performed on a .tde extract using version 10.5 and later, the extract is upgraded to a .hyper extract. After a .tde extract is upgraded to a .hyper extract, it can't be reverted back to .tde extract. For more information, see Extract Upgrade to .hyper Format. Changes to values and marks in the viewTo improve extract efficiency and scalability, values in extracts can be computed differently in versions 10.5 and later compared to versions 10.4 and earlier. Changes to how the values are computed can affect the way marks in your view are populated. In some rare cases, the changes can cause your view to change shape or become blank. These changes can also apply to multi-connection data sources, data sources that use live connections to filed-based data, data sources that connect to Google Sheets data, cloud-based data sources, extract-only data sources, and WDC data sources. To get an idea of some of the differences you might see in your view using version 2022.2, see the sections below. Format of date and date time values In versions 10.5 and later, extracts are subject to more consistent and stricter rules around how date strings are interpreted through the DATE, DATETIME, and DATEPARSE functions. This affects how dates are parsed, or the date formats and patterns that are allowed for these functions. More specifically, the rules can be generalized as the following:
These new rules allow extracts to be more efficient and to produce results that are consistent with commercial databases. However, because of these rules, particularly in international scenarios where the workbook is created in a locale different from the locale that the workbook is opened in or the server that the workbook is published to, you might see that 1.) date and datetime values change to different date and datetime values or 2.) date and datetime values change to Null. When your date and datetime values change to different date and datetime values or become Null, it's often an indication that there are issues with the underlying data. Here are some common reasons why you might see changes to your date and datetime values in your extract data source using version 10.5 and later.
Suppose you have a workbook created in an English locale that uses .tde extract data source. The table below shows a column of string data contained in the extract data source.
Based on the particular English locale, the format of the date column was determined to follow the MDY (month, day, and year) format. The following tables show what Tableau displays based on this locale when the DATE function is used to convert string values into date values.
If the extract is opened in a German locale, you see the following:
However, after the extract is opened in a German locale using version 10.5 and later, the DMY (day, month, and year) format of the German locale is strictly enforced and causes a Null value because one of the values doesn't follow DMY format.
Suppose you have another workbook created in an English locale that uses a .tde extract data source. The table below shows a column of numeric date data contained in the extract data source.
Based on the particular English locale, the format of the date column was determined to follow the MDY (month, day, and year) format. The following tables show what Tableau displays based on this locale when the DATE function is used to convert the numeric values into date values.
Suppose you have a workbook that uses a .tde extract data source. The table below shows a column of string data contained in the extract data source.
Because the date uses the ISO format, the date column always follows the YYYY-MM-DD format. The following tables show what Tableau displays when the DATE function is used to convert string values into date values.
Note: In versions 10.4 (and earlier), ISO format and other date formats could have produced differing results depending on the locale of where the workbook was created. In an English locale for example, both 2018-12-10 and 2018/12/10 could produce December 12, 2o18. However, in a German locale 2018-12-10 could produce December 12, 2018 and 2018/12/10 could produce October 12, 2018. Sort order and case sensitivity Extracts have collation support and therefore can more appropriately sort string values that have accents or are cased differently. For example, suppose you have a table of string values. In terms of sort order, this means that a string value like Égypte is now appropriately listed after Estonie and before Fidji. About Excel data: With regard to casing, this means that how Tableau stores values have changed between version 10.4 (and earlier) and version 10.5 (and later). However, the rules for sorting and comparing values haven't. In version 10.4 (and earlier), string values like "House," "HOUSE," and "houSe" are treated the same and stored with one representative value. In version 10.5 (and later), the same string values are considered unique and therefore stored as individual values. For more information, see Changes to the way values are computed. Breaking ties in Top N queries When a Top N query in your extract produces duplicate values for a specific position in a rank, the position that breaks the tie can be different when using version 10.5 and later. For example, suppose you create a top 3 filter. Positions 3, 4, and 5 have the same values. When using version 10.4 and earlier, the top filter can return 1, 2, and 3 positions. However, when using version 10.5 and later, the top filter can return 1, 2, and 5 positions. Precision of floating-point values Extracts are better at taking advantage of the available hardware resources on a computer and therefore able to perform mathematical operations in a highly parallel way. Because of this, real numbers can be aggregated by .hyper extracts in different order. When numbers are aggregated in different order, you might see different values in your view after the decimal point each time the aggregation is computed. This is because floating-point addition and multiplication is not necessarily associative. That is, (a + b) + c is not necessarily the same as a + (b + c). Also, real numbers can be aggregated in different order because floating-point multiplication is not necessarily distributive. That is, (a x b) x c is not necessarily the same as a x b x c. This type of floating-point rounding behavior in .hyper extracts resemble that of floating-point rounding behavior in commercial databases. For example, suppose your workbook contains a slider filter on an aggregated field comprised of floating point values. Because the precision of floating-point values have changed, the filter might now exclude a mark that defines the upper or lower bound of the filter range. The absence of these numbers could cause a blank view. To resolve this issue, move the slider on the filter or remove and add the filter again. Accuracy of aggregations Extracts optimize for large data sets by taking better advantage of the available hardware resources on a computer and therefore able to compute aggregations in a highly parallel way. Because of this, aggregations performed by .hyper extracts can resemble the results from commercial databases more than the results from software that specializes in statistical computations. If you’re working with a small data set or need a higher level of accuracy, consider performing aggregations through reference lines, summary card statistics, or table calculation functions like variance, standard deviation, correlation, or covariance. If the Compute Calculations Now option was used in a .tde extract using an earlier version of Tableau Desktop, certain calculated fields were materialized and therefore computed in advance and stored in the extract. If you upgrade the extract from a .tde extract to a .hyper extract, the previously materialized calculations in your extract are not included. You must use the Compute Calculations Now option again to ensure that materialized calculations are a part of the extract after the extract upgrade. For more information, see Materialize Calculations in Your Extracts. You can use the Extract API 2.0 to create .hyper extracts. For tasks that you previously performed using the Tableau SDK, such as publishing extracts, you can use the Tableau Server REST API or the Tableau Server Client (Python) library. For refresh tasks, you can use the Tableau Server REST API as well. For more information, see Tableau Hyper API. Though there are several options in your Tableau workflow for creating an extract, the primary method is described below.
After you create an extract, the workbook begins to use the extract version of your data. However, the connection to the extract version of your data is not preserved until you save the workbook. This means if you close the workbook without saving the workbook first, the workbook will connect to the original data source the next time you open it. Toggle between sampled data and entire extractWhen you're working with a large extract, you might want to create an extract with a sample of the data so you can set up the view while avoiding long queries every time you place a field on a shelf on the sheet tab. You can then toggle between using the extract (with sample data) and using the entire data source by selecting a data source on the Data menu and then selecting Use Extract. Because extracts are saved to your file system, it is possible to connect directly to them with a new Tableau Desktop instance. This is not recommended for a few reasons:
You can remove an extract at anytime by selecting the extract data source on the Data menu and then selecting . When you remove an extract, you can choose to Remove the extract from the workbook only or Remove and delete the extract file. The latter option will delete the extract from your hard drive.
You can see when the extract was last updated and other details by selecting a data source on the Data menu and then selecting .
If you open a workbook that is saved with an extract and Tableau cannot locate the extract, select one of the following options in the Extract Not Found dialog box when prompted:
Tips for using the Physical Tables optionTableau generally recommends that you use the default data storage option, Logical Tables, when setting up and working with extracts. In many cases, some of the features you need for your extract, like extract filters, are only available to you if you use the Logical Tables option. The Physical Tables option should be used sparingly to help with specific situations such as when your data source meets the Conditions for using the Physical Tables option and the size of your extract is larger than expected. To determine if the extract is larger than it should be, the sum of rows in the extract using the Logical Tables option must be higher than the sum of rows of all the combined tables before the extract has been created. If you encounter this scenario, try using the Physical Tables option instead. Alternative filtering suggestions when using the Physical Tables optionWhen using the Physical Tables option, other options to help reduce the data in your extract, like extract filters, aggregation, Top N and Sampling are disabled. If you need to reduce the data in an extract that uses the Physical Tables option, consider filtering the data before it is brought into Tableau Desktop using one of the following suggestions:
If you want to secure extract data at the row level, using the Physical Tables option is the recommended way to achieve this scenario. For more information about row-level security in Tableau, see Restrict Access at the Data Row Level.
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