[SPARK-31319][SQL][DOCS] Document UDFs/UDAFs in SQL Reference
### What changes were proposed in this pull request? Document UDF in SQL Reference ### Why are the changes needed? To make SQL Reference complete. ### Does this PR introduce any user-facing change? Yes. Here are the new pages: <img width="1050" alt="Screen Shot 2020-04-09 at 5 06 42 PM" src="https://user-images.githubusercontent.com/13592258/78950977-585dc200-7a85-11ea-875c-ce14c3795e0f.png"> <img width="1049" alt="Screen Shot 2020-04-09 at 5 07 06 PM" src="https://user-images.githubusercontent.com/13592258/78950979-5b58b280-7a85-11ea-81f3-bd5d91bd07e3.png"> <img width="1049" alt="Screen Shot 2020-04-09 at 5 07 26 PM" src="https://user-images.githubusercontent.com/13592258/78950985-5e53a300-7a85-11ea-86be-f63152c1501b.png"> <img width="1051" alt="Screen Shot 2020-04-09 at 5 07 54 PM" src="https://user-images.githubusercontent.com/13592258/78950991-63185700-7a85-11ea-9379-8da46cfc434c.png"> <img width="1060" alt="Screen Shot 2020-04-09 at 5 08 17 PM" src="https://user-images.githubusercontent.com/13592258/78950994-657ab100-7a85-11ea-8b34-d2c87f94b03b.png"> <img width="1050" alt="Screen Shot 2020-04-09 at 5 09 27 PM" src="https://user-images.githubusercontent.com/13592258/78951001-6875a180-7a85-11ea-874e-8abd14a3d3d3.png"> <img width="1060" alt="Screen Shot 2020-04-09 at 5 10 00 PM" src="https://user-images.githubusercontent.com/13592258/78951005-6f041900-7a85-11ea-9e57-520eb8db59ec.png"> <img width="1049" alt="Screen Shot 2020-04-09 at 5 11 10 PM" src="https://user-images.githubusercontent.com/13592258/78951014-73303680-7a85-11ea-93ab-32d68d2e2d59.png"> <img width="1050" alt="Screen Shot 2020-04-09 at 5 11 41 PM" src="https://user-images.githubusercontent.com/13592258/78951019-75929080-7a85-11ea-9d3b-600e8e157c05.png"> <img width="1050" alt="Screen Shot 2020-04-09 at 5 16 22 PM" src="https://user-images.githubusercontent.com/13592258/78951137-dfab3580-7a85-11ea-8512-c6b660aa271e.png"> <img width="1050" alt="Screen Shot 2020-04-09 at 5 22 15 PM" src="https://user-images.githubusercontent.com/13592258/78951466-22214200-7a87-11ea-93dd-6e36492421f1.png"> <img width="1049" alt="Screen Shot 2020-04-09 at 5 22 46 PM" src="https://user-images.githubusercontent.com/13592258/78951469-24839c00-7a87-11ea-93a9-fe30d689adbd.png"> <img width="1050" alt="Screen Shot 2020-04-09 at 5 23 08 PM" src="https://user-images.githubusercontent.com/13592258/78951472-26e5f600-7a87-11ea-84db-087a3528aa53.png"> <img width="1050" alt="Screen Shot 2020-04-09 at 5 23 34 PM" src="https://user-images.githubusercontent.com/13592258/78951474-29e0e680-7a87-11ea-8be4-2a5be1bc3788.png"> <img width="1049" alt="Screen Shot 2020-04-09 at 5 23 57 PM" src="https://user-images.githubusercontent.com/13592258/78951481-2cdbd700-7a87-11ea-8894-0a39abf54a3b.png"> <img width="1050" alt="Screen Shot 2020-04-09 at 5 24 15 PM" src="https://user-images.githubusercontent.com/13592258/78951483-2f3e3100-7a87-11ea-8845-ffebf89d7898.png"> ### How was this patch tested? Manually build and check Closes #28087 from huaxingao/udf. Authored-by: Huaxin Gao <huaxing@us.ibm.com> Signed-off-by: Sean Owen <srowen@gmail.com>
This commit is contained in:
Huaxin Gao
Sean Owen
parent
d65f534c5a
commit
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@ -356,31 +356,8 @@ aggregations such as `count()`, `countDistinct()`, `avg()`, `max()`, `min()`, et
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While those functions are designed for DataFrames, Spark SQL also has type-safe versions for some of them in
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[Scala](api/scala/org/apache/spark/sql/expressions/scalalang/typed$.html) and
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[Java](api/java/org/apache/spark/sql/expressions/javalang/typed.html) to work with strongly typed Datasets.
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Moreover, users are not limited to the predefined aggregate functions and can create their own.
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Moreover, users are not limited to the predefined aggregate functions and can create their own. For more details
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about user defined aggregate functions, please refer to the documentation of
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[User Defined Aggregate Functions](sql-ref-functions-udf-aggregate.html).
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### Type-Safe User-Defined Aggregate Functions
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User-defined aggregations for strongly typed Datasets revolve around the [Aggregator](api/scala/org/apache/spark/sql/expressions/Aggregator.html) abstract class.
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For example, a type-safe user-defined average can look like:
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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{% include_example typed_custom_aggregation scala/org/apache/spark/examples/sql/UserDefinedTypedAggregation.scala%}
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</div>
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<div data-lang="java" markdown="1">
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{% include_example typed_custom_aggregation java/org/apache/spark/examples/sql/JavaUserDefinedTypedAggregation.java%}
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</div>
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</div>
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### Untyped User-Defined Aggregate Functions
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Typed aggregations, as described above, may also be registered as untyped aggregating UDFs for use with DataFrames.
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For example, a user-defined average for untyped DataFrames can look like:
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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{% include_example untyped_custom_aggregation scala/org/apache/spark/examples/sql/UserDefinedUntypedAggregation.scala%}
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</div>
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<div data-lang="java" markdown="1">
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{% include_example untyped_custom_aggregation java/org/apache/spark/examples/sql/JavaUserDefinedUntypedAggregation.java%}
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</div>
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</div>
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@ -1,7 +1,7 @@
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---
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layout: global
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title: User defined Aggregate Functions (UDAF)
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displayTitle: User defined Aggregate Functions (UDAF)
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title: User Defined Aggregate Functions (UDAFs)
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displayTitle: User Defined Aggregate Functions (UDAFs)
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license: |
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Licensed to the Apache Software Foundation (ASF) under one or more
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contributor license agreements. See the NOTICE file distributed with
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@ -19,4 +19,83 @@ license: |
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limitations under the License.
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---
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**This page is under construction**
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### Description
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User-Defined Aggregate Functions (UDAFs) are user-programmable routines that act on multiple rows at once and return a single aggregated value as a result. This documentation lists the classes that are required for creating and registering UDAFs. It also contains examples that demonstrate how to define and register UDAFs in Scala and invoke them in Spark SQL.
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### Aggregator[-IN, BUF, OUT]
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A base class for user-defined aggregations, which can be used in Dataset operations to take all of the elements of a group and reduce them to a single value.
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* IN - The input type for the aggregation.
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* BUF - The type of the intermediate value of the reduction.
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* OUT - The type of the final output result.
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<dl>
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<dt><code><em>bufferEncoder: Encoder[BUF]</em></code></dt>
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<dd>
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Specifies the Encoder for the intermediate value type.
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</dd>
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</dl>
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<dl>
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<dt><code><em>finish(reduction: BUF): OUT</em></code></dt>
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<dd>
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Transform the output of the reduction.
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</dd>
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</dl>
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<dl>
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<dt><code><em>merge(b1: BUF, b2: BUF): BUF</em></code></dt>
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<dd>
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Merge two intermediate values.
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</dd>
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</dl>
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<dl>
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<dt><code><em>outputEncoder: Encoder[OUT]</em></code></dt>
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<dd>
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Specifies the Encoder for the final output value type.
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</dd>
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</dl>
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<dl>
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<dt><code><em>reduce(b: BUF, a: IN): BUF</em></code></dt>
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<dd>
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Aggregate input value <code>a</code> into current intermediate value. For performance, the function may modify <code>b</code> and return it instead of constructing new object for <code>b</code>.
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</dd>
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</dl>
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<dl>
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<dt><code><em>zero: BUF</em></code></dt>
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<dd>
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The initial value of the intermediate result for this aggregation.
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</dd>
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</dl>
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### Examples
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#### Type-Safe User-Defined Aggregate Functions
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User-defined aggregations for strongly typed Datasets revolve around the [Aggregator](api/scala/org/apache/spark/sql/expressions/Aggregator.html) abstract class.
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For example, a type-safe user-defined average can look like:
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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{% include_example typed_custom_aggregation scala/org/apache/spark/examples/sql/UserDefinedTypedAggregation.scala%}
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</div>
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<div data-lang="java" markdown="1">
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{% include_example typed_custom_aggregation java/org/apache/spark/examples/sql/JavaUserDefinedTypedAggregation.java%}
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</div>
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</div>
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#### Untyped User-Defined Aggregate Functions
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Typed aggregations, as described above, may also be registered as untyped aggregating UDFs for use with DataFrames.
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For example, a user-defined average for untyped DataFrames can look like:
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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{% include_example untyped_custom_aggregation scala/org/apache/spark/examples/sql/UserDefinedUntypedAggregation.scala%}
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</div>
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<div data-lang="java" markdown="1">
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{% include_example untyped_custom_aggregation java/org/apache/spark/examples/sql/JavaUserDefinedUntypedAggregation.java%}
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</div>
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</div>
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### Related Statements
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* [Scalar User Defined Functions (UDFs)](sql-ref-functions-udf-scalar.html)
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* [Integration with Hive UDFs/UDAFs/UDTFs](sql-ref-functions-udf-hive.html)
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@ -1,7 +1,7 @@
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---
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layout: global
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title: User defined Scalar Functions (UDF)
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displayTitle: User defined Scalar Functions (UDF)
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title: Scalar User Defined Functions (UDFs)
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displayTitle: Scalar User Defined Functions (UDFs)
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license: |
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Licensed to the Apache Software Foundation (ASF) under one or more
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contributor license agreements. See the NOTICE file distributed with
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@ -19,4 +19,43 @@ license: |
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limitations under the License.
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---
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**This page is under construction**
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### Description
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User-Defined Functions (UDFs) are user-programmable routines that act on one row. This documentation lists the classes that are required for creating and registering UDFs. It also contains examples that demonstrate how to define and register UDFs and invoke them in Spark SQL.
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### UserDefinedFunction
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To define the properties of a user-defined function, the user can use some of the methods defined in this class.
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<dl>
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<dt><code><em>asNonNullable(): UserDefinedFunction</em></code></dt>
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<dd>
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Updates UserDefinedFunction to non-nullable.
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</dd>
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</dl>
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<dl>
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<dt><code><em>asNondeterministic(): UserDefinedFunction</em></code></dt>
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<dd>
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Updates UserDefinedFunction to nondeterministic.
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</dd>
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</dl>
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<dl>
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<dt><code><em>withName(name: String): UserDefinedFunction</em></code></dt>
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<dd>
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Updates UserDefinedFunction with a given name.
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</dd>
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</dl>
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### Examples
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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{% include_example udf_scalar scala/org/apache/spark/examples/sql/UserDefinedScalar.scala%}
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</div>
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<div data-lang="java" markdown="1">
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{% include_example udf_scalar java/org/apache/spark/examples/sql/JavaUserDefinedScalar.java%}
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</div>
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</div>
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### Related Statements
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* [User Defined Aggregate Functions (UDAFs)](sql-ref-functions-udf-aggregate.html)
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* [Integration with Hive UDFs/UDAFs/UDTFs](sql-ref-functions-udf-hive.html)
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@ -0,0 +1,98 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.spark.examples.sql;
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// $example on:udf_scalar$
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import org.apache.spark.sql.*;
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import org.apache.spark.sql.api.java.UDF1;
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import org.apache.spark.sql.expressions.UserDefinedFunction;
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import static org.apache.spark.sql.functions.udf;
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import org.apache.spark.sql.types.DataTypes;
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// $example off:udf_scalar$
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public class JavaUserDefinedScalar {
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public static void main(String[] args) {
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// $example on:udf_scalar$
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SparkSession spark = SparkSession
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.builder()
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.appName("Java Spark SQL UDF scalar example")
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.getOrCreate();
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// Define and register a zero-argument non-deterministic UDF
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// UDF is deterministic by default, i.e. produces the same result for the same input.
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UserDefinedFunction random = udf(
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() -> Math.random(), DataTypes.DoubleType
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);
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random.asNondeterministic();
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spark.udf().register("random", random);
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spark.sql("SELECT random()").show();
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// +-------+
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// |UDF() |
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// +-------+
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// |xxxxxxx|
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// +-------+
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// Define and register a one-argument UDF
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spark.udf().register("plusOne", new UDF1<Integer, Integer>() {
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@Override
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public Integer call(Integer x) {
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return x + 1;
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}
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}, DataTypes.IntegerType);
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spark.sql("SELECT plusOne(5)").show();
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// +----------+
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// |plusOne(5)|
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// +----------+
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// | 6|
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// +----------+
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// Define and register a two-argument UDF
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UserDefinedFunction strLen = udf(
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(String s, Integer x) -> s.length() + x, DataTypes.IntegerType
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);
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spark.udf().register("strLen", strLen);
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spark.sql("SELECT strLen('test', 1)").show();
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// +------------+
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// |UDF(test, 1)|
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// +------------+
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// | 5|
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// +------------+
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// UDF in a WHERE clause
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spark.udf().register("oneArgFilter", new UDF1<Long, Boolean>() {
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@Override
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public Boolean call(Long x) {
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return x > 5;
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}
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}, DataTypes.BooleanType);
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spark.range(1, 10).createOrReplaceTempView("test");
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spark.sql("SELECT * FROM test WHERE oneArgFilter(id)").show();
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// +---+
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// | id|
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// +---+
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// | 6|
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// | 7|
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// | 8|
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// | 9|
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// +---+
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// $example off:udf_scalar$
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spark.stop();
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}
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}
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@ -0,0 +1,80 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.spark.examples.sql
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// $example on:udf_scalar$
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import org.apache.spark.sql.SparkSession
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import org.apache.spark.sql.functions.udf
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// $example off:udf_scalar$
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object UserDefinedScalar {
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def main(args: Array[String]): Unit = {
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// $example on:udf_scalar$
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val spark = SparkSession
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.builder()
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.appName("Spark SQL UDF scalar example")
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.getOrCreate()
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// Define and register a zero-argument non-deterministic UDF
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// UDF is deterministic by default, i.e. produces the same result for the same input.
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val random = udf(() => Math.random())
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spark.udf.register("random", random.asNondeterministic())
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spark.sql("SELECT random()").show()
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// +-------+
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// |UDF() |
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// +-------+
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// |xxxxxxx|
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// +-------+
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// Define and register a one-argument UDF
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val plusOne = udf((x: Int) => x + 1)
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spark.udf.register("plusOne", plusOne)
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spark.sql("SELECT plusOne(5)").show()
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// +------+
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// |UDF(5)|
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// +------+
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// | 6|
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// +------+
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// Define a two-argument UDF and register it with Spark in one step
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spark.udf.register("strLenScala", (_: String).length + (_: Int))
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spark.sql("SELECT strLenScala('test', 1)").show()
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// +--------------------+
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// |strLenScala(test, 1)|
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// +--------------------+
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// | 5|
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// +--------------------+
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// UDF in a WHERE clause
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spark.udf.register("oneArgFilter", (n: Int) => { n > 5 })
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spark.range(1, 10).createOrReplaceTempView("test")
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spark.sql("SELECT * FROM test WHERE oneArgFilter(id)").show()
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// +---+
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// | id|
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// +---+
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// | 6|
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// | 7|
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// | 8|
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// | 9|
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// +---+
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// $example off:udf_scalar$
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spark.stop()
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}
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}
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Reference in a new issue