Functions

ksqlDB has many built-in functions that help with processing records in streaming data, like ABS and SUM. Functions are used within a SQL query to filter, transform, or aggregate data.

With the ksqlDB API, you can implement custom functions that go beyond the built-in functions. For example, you can create a custom function that applies a pre-trained machine learning model to a stream.

ksqlDB supports these kinds of functions:

Stateless scalar function (UDF)

A scalar function that takes one input row and returns one output value. No state is retained between function calls. When you implement a custom scalar function, it's called a User-Defined Function (UDF). For more information, see Scalar Function.

Stateful aggregate function (UDAF)

An aggregate function that takes N input rows and returns one output value. During the function call, state is retained for all input records, which enables aggregating results. When you implement a custom aggregate function, it's called a User-Defined Aggregate Function (UDAF). For more information, see Aggregate Function.

Table function (UDTF)

A table function that takes one input row and returns zero or more output rows. No state is retained between function calls. When you implement a custom table function, it's called a User-Defined Table Function (UDTF). For more information, see Table Function.

Implement a Custom Function

Follow these steps to create your custom functions:

1. Write your UDF, UDAF, or UDTF class in Java.

   - If your Java class is a UDF, mark it with the @UdfDescription and @Udf annotations. - If your class is a UDAF, mark it with the @UdafDescription and @UdafFactory annotations. - If your class is a UDTF, mark it with the @UdtfDescription and @UdtfFactory annotations.

   For more information, see Example UDF class and Example UDAF class.

2. Deploy the JAR file to the ksql extensions directory. For more information, see Deploying.

3. Use your function like any other ksqlDB function in your queries.

Tip

The SHOW FUNCTIONS statement lists the available functions in your ksqlDB Server, including your custom UDF and UDAF functions. Use the DESCRIBE FUNCTION statement to display details about your custom functions.

For a detailed walkthrough on creating a UDF, see the how-to guide for creating a user-defined function.

Creating UDFs, UDAFs, and UDTFs

ksqlDB supports creating User Defined Scalar Functions (UDFs), User Defined Aggregate Functions (UDAFs), and User Defined Table Functions (UDTFs) by using custom jars that are uploaded to the ext/ directory of the ksqlDB installation. At start up time, ksqlDB scans the jars in the directory looking for any classes that annotated with @UdfDescription (UDF), @UdafDescription (UDAF), or @UdtfDescription (UDTF).

• Classes annotated with @UdfDescription are scanned for any public methods that are annotated with @Udf.
• Classes annotated with @UdafDescription are scanned for any public static methods that are annotated with @UdafFactory.
• Classes annotated with @UdtfDescription are scanned for any public methods that are annotated with @Udtf.

Each function that is found is parsed and, if successful, loaded into ksqlDB.

Each function instance has its own child-first ClassLoader that is isolated from other functions. If you need to use any third-party libraries with your functions, they should also be part of your jar, which means that you should create an "uber-jar". The classes in your uber-jar are loaded in preference to any classes on the ksqlDB classpath, excluding anything vital to the running of ksqlDB, i.e., classes that are part of org.apache.kafka and io.confluent. Further, the ClassLoader can restrict access to other classes via a blacklist. The blacklist file is resource-blacklist.txt. You can add any classes or packages that you want blacklisted from UDF use. For example you may not want a UDF to be able to fork processes. Further details on how to blacklist are available below.

UDFs

To create a UDF you need to create a class that's annotated with @UdfDescription. Each method in the class that represents a UDF must be public and annotated with @Udf. The class you create represents a collection of UDFs all with the same name but may have different arguments and return types.

@UdfParameter annotations can be added to method parameters to provide users with richer information, including the parameter schema. This annotation is required if the SQL type can't be inferred from the Java type, for example, STRUCT.

Null Handling

If a UDF uses primitive types in its signature it is indicating that the parameter should never be null. Conversely, using boxed types indicates the function can accept null values for the parameter. It's up to the implementor of the UDF to chose which is the more appropriate. A common pattern is to return null if the input is null, though generally this is only for parameters that are expected to be supplied from the source row being processed.

For example, a substring(String str, int pos) UDF might return null if str is null, but a null value for the pos parameter would be treated as an error, and so should be a primitive. In fact, the built-in substring is more lenient and would return null if pos is null).

The return type of a UDF can also be a primitive or boxed type. A primitive return type indicates the function will never return null, whereas a boxed type indicates that it may return null.

The ksqlDB Server checks the value that's passed to each parameter and reports an error to the server log for any null values being passed to a primitive type. The associated column in the output row will be null.

Dynamic return type

UDFs support dynamic return types that are resolved at runtime. This is useful if you want to implement a UDF with a non-deterministic return type, like DECIMAL or STRUCT. For example, a UDF that returns BigDecimal, which maps to the SQL DECIMAL type, may vary the precision and scale of the output based on the input schema.

To use this functionality, you need to specify a method with signature public SqlType <your-method-name>(final List<SqlType> params) and annotate it with @UdfSchemaProvider. Also, you need to link it to the corresponding UDF by using the schemaProvider=<your-method-name> parameter of the @Udf annotation.

Generics in UDFs

A UDF declaration can utilize generics if they match the following conditions:

• Any generic in the return value of a method must appear in at least one of the method parameters
• The generic must not adhere to any interface. For example, <T extends Number> is not valid).
• The generic does not support type coercion or inheritance. For example, add(T a, T b) will accept BIGINT, BIGINT but not INT, BIGINT.

Example UDF class

The class below creates a UDF named multiply. The name of the UDF is provided in the name parameter of the UdfDescription annotation. This name is case-insensitive and is what can be used to call the UDF. As can be seen this UDF can be invoked in different ways:

• with two int parameters returning a long (BIGINT) result.
• with two long (BIGINT) parameters returning a long (BIGINT) result.
• with two nullable Long (BIGINT) parameters returning a nullable Long (BIGINT) result.
• with two double parameters returning a double result.
• with variadic double parameters returning a double result.

import io.confluent.ksql.function.udf.Udf;
import io.confluent.ksql.function.udf.UdfDescription;

@UdfDescription(name = "multiply", description = "multiplies 2 numbers")
public class Multiply {

  @Udf(description = "multiply two non-nullable INTs.")
  public long multiply(
    @UdfParameter(value = "V1", description = "the first value") final int v1,
    @UdfParameter(value = "V2", description = "the second value") final int v2) {
    return v1 * v2;
  }

  @Udf(description = "multiply two non-nullable BIGINTs.")
  public long multiply(
    @UdfParameter("V1") final long v1,
    @UdfParameter("V2") final long v2) {
    return v1 * v2;
  }

  @Udf(description = "multiply two nullable BIGINTs. If either param is null, null is returned.")
  public Long multiply(final Long v1, final Long v2) {
    return v1 == null || v2 == null ? null : v1 * v2;
  }

  @Udf(description = "multiply two non-nullable DOUBLEs.")
  public double multiply(final double v1, final double v2) {
    return v1 * v2;
  }

  @Udf(description = "multiply N non-nullable DOUBLEs.")
  public double multiply(final double... values) {
    return Arrays.stream(values).reduce((a, b) -> a * b);
  }
}

If you're using Gradle to build your UDF or UDAF, specify the ksqldb-udf dependency:

compile 'io.confluent.ksql:ksqldb-udf:6.0.1'

To compile with the latest version of ksqldb-udf:

compile 'io.confluent.ksql:ksqldb-udf:+'

If you're using Maven to build your UDF or UDAF, specify the ksqldb-udf dependency in your POM file:

<!-- Specify the repository for Confluent dependencies -->
    <repositories>
        <repository>
            <id>confluent</id>
            <url>http://packages.confluent.io/maven/</url>
        </repository>
    </repositories>

<!-- Specify the ksqldb-udf dependency -->
<dependencies>
    <dependency>
        <groupId>io.confluent.ksql</groupId>
        <artifactId>ksqldb-udf</artifactId>
        <version>6.0.1</version>
    </dependency>
</dependencies>

UdfDescription Annotation

The @UdfDescription annotation is applied at the class level and has four fields, two of which are required. The information provided here is used by the SHOW FUNCTIONS and DESCRIBE FUNCTION <function> commands.

Field	Description	Required
name	The case-insensitive name of the UDF(s) represented by this class.	Yes
description	A string describing generally what the function(s) in this class do.	Yes
category	For grouping similar functions in the output of SHOW FUNCTIONS.	No
author	The author of the UDF.	No
version	The version of the UDF.	No

Udf Annotation

The @Udf annotation is applied to public methods of a class annotated with @UdfDescription. Each annotated method will become an invocable function in SQL. This annotation supports the following fields:

Field	Description	Required
description	A string describing generally what a particular version of the UDF does (see the following example).	No
schema	The ksqlDB schema for the return type of this UDF.	For complex types such as STRUCT if schemaProvider is not passed in.
schemaProvider	A reference to a method that computes the return schema of this UDF. For more info, see Dynamic return type.	For complex types, like STRUCT, if schema is not provided.

@Udf(description = "Returns a substring of str that starts at pos"
  + " and continues to the end of the string")
public String substring(final String str, final int pos)

@Udf(description = "Returns a substring of str that starts at pos and is of length len")
public String substring(final String str, final int pos, final int len)

UdfParameter Annotation

The @UdfParameter annotation is applied to parameters of methods annotated with @Udf. ksqlDB uses the additional information in the @UdfParameter annotation to specify the parameter schema (if it can't be inferred from the Java type) or to provide users with richer information about the method when, for example, they execute DESCRIBE FUNCTION on the method.

Field	Description	Required
value	The case-insensitive name of the parameter	Required if the UDF JAR was not compiled with the -parameters javac argument.
description	A string describing generally what the parameter represents	No
schema	The ksqlDB schema for the parameter.	For complex types, like STRUCT

Note

If schema is supplied in the @UdfParameter annotation for a STRUCT it is considered "strict" - any inputs must match exactly, including order and names of the fields.

@Udf
public String substring(
   @UdfParameter("str") final String str,
   @UdfParameter(value = "pos", description = "Starting position of the substring") final int pos)

@Udf
public boolean livesInRegion(
   @UdfParameter(value = "zipcode", description = "a US postal code") final String zipcode,
   @UdfParameter(schema = "STRUCT<ZIP STRING, NAME STRING>") final Struct employee)

If your Java8 class is compiled with the -parameters compiler flag, the name of the parameter will be inferred from the method declaration.

Configurable UDF

If the UDF class needs access to the ksqlDB Server configuration it can implement org.apache.kafka.common.Configurable, for example:

@UdfDescription(name = "MyFirstUDF", description = "multiplies 2 numbers")
public class SomeConfigurableUdf implements Configurable {
  private String someSetting = "a.default.value";

  @Override
  public void configure(final Map<String, ?> map) {
    this.someSetting = (String)map.get("ksql.functions.myfirstudf.some.setting");
  }

  ...
}

For security reasons, only settings whose name is prefixed with ksql.functions.<lowercase-udfname>. or ksql.functions._global_. are propagated to the UDF.

UDAFs

To create a UDAF you need to create a class that's annotated with @UdafDescription. Each method in the class that's used as a factory for creating an aggregation must be public static, be annotated with @UdafFactory, and must return either Udaf or TableUdaf. The class you create represents a collection of UDAFs all with the same name but may have different arguments and return types.

Both Udaf and TableUdaf are parameterized by three types: I is the input type of the UDAF. A is the data type of the intermediate storage used to keep track of the state of the UDAF. O is the data type of the return value. Decoupling the data types of the state and return value enables you to define UDAFs like average, as shown in the following example.

When you create a UDAF, use the map method to provide the logic that transforms an intermediate aggregate value to the returned value.

The merge method is only called when merging sessions when session windowing is used.

Example UDAF class

The following class creates a UDAF named my_average. The name of the UDAF is provided in the name parameter of the UdafDescription annotation. This name is case-insensitive and is what can be used to call the UDAF.

The class provides three factories that return a TableUdaf, one for each of the input types Long, Integer, and Double. Moreover, it provides a factory that returns a Udaf that doesn't support undo. Each method defines a different type for the intermediate state based on the input type (I), which in this case is a STRUCT consisting of two fields, the SUM, of type I, and the COUNT, of type Long. To get the result of the UDAF, each method implements a map function that returns the Double division of the accumulated SUM and COUNT.

The UDAF can be invoked in four ways:

• With a Long (BIGINT) column, returning the aggregated value as Double. Defines the schema for intermediate state type using the annotation parameter parameterSchema. The return type is TableUdaf and therefore supports the undo operation.
• With an Integer column returning the aggregated value as Double. Likewise defines the schema of the Struct and supports undo.
• With a Double column, returning the aggregated value as Double. Likewise defines the schema of the Struct and supports undo.
• With a String (VARCHAR) column and an initializer that is a String (VARCHAR), returning the average String (VARCHAR) length as a Double.

@UdafDescription(name = "my_average", description = "Computes the average.")
public class AverageUdaf {

  private static final String COUNT = "COUNT";
  private static final String SUM = "SUM";

  @UdafFactory(description = "Compute average of column with type Long.",
      aggregateSchema = "STRUCT<SUM bigint, COUNT bigint>")
  // Can be used with table aggregations
  public static TableUdaf<Long, Struct, Double> averageLong() {

    final Schema STRUCT_LONG = SchemaBuilder.struct().optional()
          .field(SUM, Schema.OPTIONAL_INT64_SCHEMA)
          .field(COUNT, Schema.OPTIONAL_INT64_SCHEMA)
          .build();

    return new TableUdaf<Long, Struct, Double>() {

      @Override
      public Struct initialize() {
        return new Struct(STRUCT_LONG).put(SUM, 0L).put(COUNT, 0L);
      }

      @Override
      public Struct aggregate(final Long newValue,
                              final Struct aggregate) {

        if (newValue == null) {
          return aggregate;
        }
        return new Struct(STRUCT_LONG)
            .put(SUM, aggregate.getInt64(SUM) + newValue)
            .put(COUNT, aggregate.getInt64(COUNT) + 1);
      }

      @Override
      public Double map(final Struct aggregate) {
        final long count = aggregate.getInt64(COUNT);
        if (count == 0) {
          return 0.0;
        }
        return aggregate.getInt64(SUM) / ((double)count);
      }

      @Override
      public Struct merge(final Struct agg1,
                          final Struct agg2) {

        return new Struct(STRUCT_LONG)
            .put(SUM, agg1.getInt64(SUM) + agg2.getInt64(SUM))
            .put(COUNT, agg1.getInt64(COUNT) + agg2.getInt64(COUNT));
      }

      @Override
      public Struct undo(final Long valueToUndo,
                         final Struct aggregate) {

        return new Struct(STRUCT_LONG)
            .put(SUM, aggregate.getInt64(SUM) - valueToUndo)
            .put(COUNT, aggregate.getInt64(COUNT) - 1);
      }
    };
  }

  @UdafFactory(description = "Compute average of column with type Integer.",
      aggregateSchema = "STRUCT<SUM integer, COUNT bigint>")
  public static TableUdaf<Integer, Struct, Double> averageInt() {

    final Schema STRUCT_INT = SchemaBuilder.struct().optional()
          .field(SUM, Schema.OPTIONAL_INT32_SCHEMA)
          .field(COUNT, Schema.OPTIONAL_INT64_SCHEMA)
          .build();

    return new TableUdaf<Integer, Struct, Double>() {

      @Override
      public Struct initialize() {
        return new Struct(STRUCT_INT).put(SUM, 0).put(COUNT, 0L);
      }

      @Override
      public Struct aggregate(final Integer newValue,
                              final Struct aggregate) {

        if (newValue == null) {
          return aggregate;
        }
        return new Struct(STRUCT_INT)
            .put(SUM, aggregate.getInt32(SUM) + newValue)
            .put(COUNT, aggregate.getInt64(COUNT) + 1);

      }

      @Override
      public Double map(final Struct aggregate) {
        final long count = aggregate.getInt64(COUNT);
        if (count == 0) {
          return 0.0;
        }
        return aggregate.getInt64(SUM) / ((double)count);
      }

      @Override
      public Struct merge(final Struct agg1,
                          final Struct agg2) {

        return new Struct(STRUCT_INT)
            .put(SUM, agg1.getInt32(SUM) + agg2.getInt64(SUM))
            .put(COUNT, agg1.getInt64(COUNT) + agg2.getInt64(COUNT));
      }

      @Override
      public Struct undo(final Integer valueToUndo,
                         final Struct aggregate) {

        return new Struct(STRUCT_INT)
            .put(SUM, aggregate.getInt32(SUM) - valueToUndo)
            .put(COUNT, aggregate.getInt64(COUNT) - 1);
      }
    };
  }

  @UdafFactory(description = "Compute average of column with type Double.",
      aggregateSchema = "STRUCT<SUM double, COUNT bigint>")
  public static TableUdaf<Double, Struct, Double> averageDouble() {

    final Schema STRUCT_DOUBLE = SchemaBuilder.struct().optional()
        .field(SUM, Schema.OPTIONAL_FLOAT64_SCHEMA)
        .field(COUNT, Schema.OPTIONAL_INT64_SCHEMA)
        .build();

    return new TableUdaf<Double, Struct, Double>() {

      @Override
      public Struct initialize() {
        return new Struct(STRUCT_DOUBLE).put(SUM, 0.0).put(COUNT, 0L);
      }

      @Override
      public Struct aggregate(final Double newValue,
                              final Struct aggregate) {

        if (newValue == null) {
          return aggregate;
        }
        return new Struct(STRUCT_DOUBLE)
            .put(SUM, aggregate.getFloat64(SUM) + newValue)
            .put(COUNT, aggregate.getInt64(COUNT) + 1);

      }

      @Override
      public Double map(final Struct aggregate) {
        final long count = aggregate.getInt64(COUNT);
        if (count == 0) {
          return 0.0;
        }
        return aggregate.getFloat64(SUM) / ((double)count);
      }

      @Override
      public Struct merge(final Struct agg1,
                          final Struct agg2) {

        return new Struct(STRUCT_DOUBLE)
            .put(SUM, agg1.getFloat64(SUM) + agg2.getFloat64(SUM))
            .put(COUNT, agg1.getInt64(COUNT) + agg2.getInt64(COUNT));
      }

      @Override
      public Struct undo(final Double valueToUndo,
                         final Struct aggregate) {

        return new Struct(STRUCT_DOUBLE)
            .put(SUM, aggregate.getFloat64(SUM) - valueToUndo)
            .put(COUNT, aggregate.getInt64(COUNT) - 1);
      }
    };
  }

  // This method shows providing an initial value to an aggregated, i.e., it would be called
  // with my_average(col1, 'some_initial_value')
  @UdafFactory(description = "Compute average of length of strings",
      aggregateSchema = "STRUCT<SUM bigint, COUNT bigint>")
  public static Udaf<String, Struct, Double> averageStringLength(final String initialString) {

    final Schema STRUCT_LONG = SchemaBuilder.struct().optional()
          .field(SUM, Schema.OPTIONAL_INT64_SCHEMA)
          .field(COUNT, Schema.OPTIONAL_INT64_SCHEMA)
          .build();

    return new Udaf<String, Struct, Double>() {

      @Override
      public Struct initialize() {
        return new Struct(STRUCT_LONG).put(SUM, (long) initialString.length()).put(COUNT, 1L);
      }

      @Override
      public Struct aggregate(final String newValue,
                              final Struct aggregate) {

        if (newValue == null) {
          return aggregate;
        }
        return new Struct(STRUCT_LONG)
            .put(SUM, aggregate.getInt64(SUM) + newValue.length())
            .put(COUNT, aggregate.getInt64(COUNT) + 1);
      }

      @Override
      public Double map(final Struct aggregate) {
        final long count = aggregate.getInt64(COUNT);
        if (count == 0) {
          return 0.0;
        }
        return aggregate.getInt64(SUM) / ((double)count);
      }

      @Override
      public Struct merge(final Struct agg1,
                          final Struct agg2) {

        return new Struct(STRUCT_LONG)
            .put(SUM, agg1.getInt64(SUM) + agg2.getInt64(SUM))
            .put(COUNT, agg1.getInt64(COUNT) + agg2.getInt64(COUNT));
      }
    };
  }
}

UdafDescription Annotation

The @UdafDescription annotation is applied at the class level and has four fields, two of which are required. The information provided here is used by the SHOW FUNCTIONS and DESCRIBE FUNCTION <function> commands.

Field	Description	Required
name	The case-insensitive name of the UDAF(s) represented by this class.	Yes
description	A string describing generally what the function(s) in this class do.	Yes
author	The author of the UDF.	No
version	The version of the UDF.	No

UdafFactory Annotation

The @UdafFactory annotation is applied to public static methods of a class annotated with @UdafDescription. The method must return either Udaf, or, if it supports table aggregations, TableUdaf. Each annotated method is a factory for an invocable aggregate function in SQL. The annotation supports the following fields:

Field	Description	Required
description	A string describing generally what the function(s) in this class do.	Yes
paramSchema	The ksqlDB schema for the input parameter.	For complex types, like STRUCT
aggregateSchema	The ksqlDB schema for the intermediate state.	For complex types, like STRUCT
returnSchema	The ksqlDB schema for the return value.	For complex types, like STRUCT

Note

If paramSchema , aggregateSchema or returnSchema is supplied in the @UdfParameter annotation for a STRUCT, it's considered "strict" - any inputs must match exactly, including order and names of the fields.

You can use this to better describe what a particular version of the UDAF does, for example:

@UdafFactory(description = "Compute average of column with type Long.",
          aggregateSchema = "STRUCT<SUM bigint, COUNT bigint>")
public static TableUdaf<Long, Struct, Double> averageLong(){...}

@@UdafFactory(description = "Compute average of length of strings",
           aggregateSchema = "STRUCT<SUM bigint, COUNT bigint>")
public static Udaf<String, Struct, Double> averageStringLength(final String initialString){...}

UDTFs

To create a UDTF you need to create a class that is annotated with @UdtfDescription. Each method in the class that represents a UDTF must be public and annotated with @Udtf. The class you create represents a collection of UDTFs all with the same name but may have different arguments and return types.

@UdfParameter annotations can be added to method parameters to provide users with richer information, including the parameter schema. This annotation is required if the SQL type can't be inferred from the Java type, like STRUCT.

For an example UDTF implementation, see Data Enrichment in ksqlDB Using UDTFs.

Null Handling

If a UDTF uses primitive types in its signature, this indicates that the parameter should never be null. Conversely, using boxed types indicates that the function can accept null values for the parameter. It's up to the implementer of the UDTF to chose which is the most appropriate. A common pattern is to return null if the input is null, though generally this is only for parameters that are expected to be supplied from the source row being processed.

For example, a substring(String str, int pos) UDF might return null if str is null, but a null pos parameter would be treated as an error and so should be a primitive. The built-in substring function is more lenient and return null if pos is null.

The return type of a UDTF can also be a primitive or boxed type. A primitive return type indicates that the function never returns null, and a boxed type indicates that it may return null.

ksqlDB server checks the value being passed to each parameter and reports an error to the server log for any null values being passed to a primitive type. The associated column in the output row will be null.

Dynamic return type

UDTFs support dynamic return types that are resolved at runtime. This is useful if you want to implement a UDTF with a non-deterministic return type. For example, a UDTF that returns a BigDecimal may vary the precision and scale of the output based on the input schema.

To use this functionality, specify a method with signature public SqlType <your-method-name>(final List<SqlType> params) and annotate it with @UdfSchemaProvider. Also, you need to link it to the corresponding UDF by using the schemaProvider=<your-method-name> parameter of the @Udtf annotation.

If your UDTF method returns a value of type List<T>, the type referred to by the schema provider method is the type T, not the type List<T>.

Example UDTF class

The following class creates a UDTF named split_string. The name of the UDTF is provided in the name parameter of the UdtfDescription annotation. This name is case-insensitive, and you can use it to call the UDTF.

UDTF methods must return a value of type List<T>, where T is any of the supported SQL Java types.

You can invoke this UDTF in two different ways:

• with a single string containing the string to split;
• with a string containing the string to split and a regex to define the delimiter.

import io.confluent.ksql.function.udf.Udtf;
import io.confluent.ksql.function.udf.UdtfDescription;

@UdtfDescription(name = "split_string", description = "splits a string into words")
public class SplitString {

  @Udtf(description="Splits a string into words")
  public List<String> split(String input) {
    return Arrays.asList(String.split("\\s+"));
  }

  @Udtf(description="Splits a string into words")
  public List<String> split(String input, String delimRegex) {
    return Arrays.asList(String.split(delimRegex));
  }
}

If you're using Gradle to build your UDF or UDAF, specify the ksqldb-udf dependency:

compile 'io.confluent.ksql:ksqldb-udf:6.0.1'

To compile with the latest version of ksqldb-udf:

compile 'io.confluent.ksql:ksqldb-udf:+'

If you're using Maven to build your function, specify the ksqldb-udf dependency in your POM file:

<!-- Specify the repository for Confluent dependencies -->
    <repositories>
        <repository>
            <id>confluent</id>
            <url>http://packages.confluent.io/maven/</url>
        </repository>
    </repositories>

<!-- Specify the ksqldb-udf dependency -->
<dependencies>
    <dependency>
        <groupId>io.confluent.ksql</groupId>
        <artifactId>ksqldb-udf</artifactId>
        <version>6.0.1</version>
    </dependency>
</dependencies>

UdtfDescription Annotation

The @UdtfDescription annotation is applied at the class level and has four fields, two of which are required. The information provided here is used by the SHOW FUNCTIONS and DESCRIBE FUNCTION <function> commands.

Field	Description	Required
name	The case-insensitive name of the UDTF(s) represented by this class.	Yes
description	A string describing generally what the function(s) in this class do.	Yes
author	The author of the UDTF.	No
version	The version of the UDTF.	No

Udtf Annotation

The @Udtf annotation is applied to public methods of a class annotated with @UdtfDescription. Each annotated method becomes an invocable function in SQL. This annotation supports the following fields:

Field	Description	Required
description	A string describing generally what a particular version of the UDTF does (see example).	No
schema	The ksqlDB schema for the return type of this UDTF.	For complex types such as STRUCT if schemaProvider is not passed in.
schemaProvider	A reference to a method that computes the return schema of this UDTF. (See Dynamic Return Types for more info).	For complex types such as STRUCT if schema is not passed in.

Annotating UDTF Parameters

You can use the @UdfParameter annotation to provide extra information for UDTF parameters. This is the same annotation as used for UDFs. Please see the earlier documentation on this for further information.

Supported Types

ksqlDB supports the following Java types for UDFs, UDAFs, and UDTFs.

Java Type	SQL Type
int	INTEGER
Integer	INTEGER
boolean	BOOLEAN
Boolean	BOOLEAN
long	BIGINT
Long	BIGINT
double	DOUBLE
Double	DOUBLE
String	VARCHAR
List	ARRAY
Map	MAP
Struct	STRUCT
BigDecimal	DECIMAL

Note

Using Struct or BigDecimal in your functions requires specifying the schema by using paramSchema, returnSchema, aggregateSchema, or a schema provider.

Deploying

To deploy your user defined functions, you create a jar containing all of the classes required by the functions. If you depend on third-party libraries, this should be an uber-jar containing these libraries. Once the jar is created, deploy it to each ksqlDB server instance. Copy the jar to the ext/ directory that's part of the ksqlDB distribution. The ext/ directory can be configured via the ksql.extension.dir.

The jars in the ext/ directory are scanned only at start-up, so you must restart your ksqlDB Server instances to pick up new and updated UD(A)Fs.

It s important to ensure that you deploy the custom jars to each server instance. Failure to do so results in errors when processing any statements that try to use these functions. The errors may go unnoticed in the ksqlDB CLI if the ksqlDB Server instance it is connected to has the jar installed, but one or more other ksqlDB servers don't have it installed. In these cases, the errors will appear in the ksqlDB Server log (ksql.log) . The error would look something like:

[2018-07-04 12:37:28,602] ERROR Failed to handle: Command{statement='create stream pageviews_ts as select tostring(viewtime) from pageviews;', overwriteProperties={}} (io.confluent.ksql.rest.server.computation.InteractiveStatementExecutor:218)
io.confluent.ksql.util.KsqlException: Can't find any functions with the name 'TOSTRING'

The servers that don't have the jars don't process any queries using the custom UD(A)Fs. Processing will continue, but it's restricted to only the servers with the correct jars installed.

Usage

Once your functions are deployed, you can call them in the same way you would invoke any of the ksqlDB built-in functions. The function names are case-insensitive. For example, using the multiply example:

CREATE STREAM number_stream (int1 INT, int2 INT, long1 BIGINT, long2 BIGINT)
  WITH (VALUE_FORMAT = 'JSON', KAFKA_TOPIC = 'numbers');

SELECT multiply(int1, int2), MULTIPLY(long1, long2) FROM number_stream EMIT CHANGES;

ksqlDB Custom Functions and Security

Blacklisting

In some deployment environments, it may be necessary to restrict the classes that UD(A)Fs have access to, as they may represent a security risk. To reduce the attack surface of ksqlDB user defined functions you can optionally blacklist classes and packages so that they can't be used from a UD(A)F. An example blacklist is in a file named resource-blacklist.txt in the ext/ directory. All of the entries in the default version of the file are commented out, but it shows how you can use the blacklist.

This file contains one entry per line, where each line is a class or package that should be blacklisted. The matching of the names is based on a regular expression, so if you have an entry, java.lang.Process like this:

java.lang.Process

This matches any paths that begin with java.lang.Process, like java.lang.Process, java.lang.ProcessBuilder, etc.

If you want to blacklist a single class, for example, java.lang.Compiler, then you would add:

java.lang.Compiler$

Any blank lines or lines beginning with # are ignored. If the file is not present, or is empty, then no classes are blacklisted.

Security Manager

By default, ksqlDB installs a simple Java security manager for executing user defined functions. The security manager blocks attempts by any functions to fork processes from the ksqlDB Server. It also prevents them from calling System.exit(..).

You can disable the security manager by setting ksql.udf.enable.security.manager to false.

Disabling ksqlDB Custom Functions

You can disable the loading of all UDFs in the ext/ directory by setting ksql.udfs.enabled to false. By default, they are enabled.

Metric Collection

Metric collection can be enabled by setting the config ksql.udf.collect.metrics to true. This defaults to false and is generally not recommended for production usage, as metrics are collected on each invocation and introduce some overhead to processing time. See more details in the UDF metrics reference section.

Suggested Reading

• Data Enrichment in ksqlDB Using UDTFs
• ksqlDB UDFs and UDAFs Made Easy
• How to Build a UDF and/or UDAF in ksqlDB 5.0

---

Last update: 2020-10-19