KSQL Custom Function Reference (UDF, UDAF and UDTF)

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

With the KSQL 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.

KSQL 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).
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).
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).

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 KSQL function in your queries.

Tip

The SHOW FUNCTIONS statement lists the available functions in your KSQL 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 Implement a User-defined Function (UDF, UDAF or UDTF).

Creating UDFs, UDAFs and UDTFs

KSQL supports creating User Defined Scalar Functions (UDFs), User Defined Aggregate Functions (UDAFs) and User Defined Table Functions (UDTFs) via custom jars that are uploaded to the ext/ directory of the KSQL installation. At start up time KSQL 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 KSQL.

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 UDFs then they should also be part of your jar, i.e., you should create an “uber-jar”. The classes in your uber-jar will be loaded in preference to any classes on the KSQL classpath excluding anything vital to the running of KSQL, 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 is 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 KSQL type cannot be inferred from the Java type (e.g. 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 is up to the implementor of the UDF 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 hence should be a primitive. (In actual fact, the in-built substring is more lenient and would return null if pos was 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, where as a boxed type indicates it may return null.

The KSQL server will check the value being passed to each parameter and report 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 such as 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 @SchemaProvider. 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 ksql-udf dependency:

compile 'io.confluent.ksql:ksql-udf:5.4.1'

To compile with the latest version of ksql-udf:

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

If you’re using Maven to build your UDF or UDAF, specify the ksql-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 ksql-udf dependency -->
<dependencies>
    <dependency>
        <groupId>io.confluent.ksql</groupId>
        <artifactId>ksql-udf</artifactId>
        <version>5.4.1</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
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 KSQL. The annotation only has a single field description that is optional. You can use this to better describe what a particular version of the UDF does, for example:

@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. KSQL will use the additional information in the @UdfParameter annotation to specify the parameter schema (if it cannot 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 KSQL schema for the parameter. For complex types such as 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 KSQL server configuration it can implement org.apache.kafka.common.Configurable, e.g.

@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_. will be propagated to the Udf.

UDAFs

To create a UDAF you need to create a class that is annotated with @UdafDescription. Each method in the class that is 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 allows you to define UDAFs like average as we show in the example below. When creating a UDAF, use the map method to provide the logic that transforms an intermediate aggregate value to the returned value.

Example UDAF class

The class below 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 does not 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 KSQL. 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 KSQL schema for the input parameter. For complex types such as STRUCT
aggregateSchema The KSQL schema for the intermediate state. For complex types such as STRUCT
returnSchema The KSQL schema for the return value. For complex types such as STRUCT

Note

If paramSchema , aggregateSchema or returnSchema 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.

You can use this to better describe what a particular version of the UDF 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 KSQL type cannot be inferred from the Java type (e.g. STRUCT).

Null Handling

If a UDTF 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 is up to the implementor 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 hence should be a primitive. (In actual fact, the in-built substring is more lenient and would return null if pos was null).

The return type of a UDTF can also be a primitive or boxed type. A primitive return type indicates the function will never return null, where as a boxed type indicates it may return null.

The KSQL server will check the value being passed to each parameter and report 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. A UDTF which returns BigDecimal, for example, 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 @SchemaProvider. 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 class below 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 KSQL 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;

@UdfDescription(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 ksql-udf dependency:

compile 'io.confluent.ksql:ksql-udf:5.4.1'

To compile with the latest version of ksql-udf:

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

If you’re using Maven to build your function, specify the ksql-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 ksql-udf dependency -->
<dependencies>
    <dependency>
        <groupId>io.confluent.ksql</groupId>
        <artifactId>ksql-udf</artifactId>
        <version>5.4.1</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 will become an invocable function in KSQL. 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 KSQL 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

The types supported by UDFs/UDAFs/UDTFs are currently limited to:

Java Type KSQL Type
int INTEGER
Integer INTEGER
boolean BOOLEAN
Boolean BOOLEAN
long BIGINT
Long BIGINT
double DOUBLE
Double DOUBLE
String VARCHAR
List ARRAY
Map MAP
Struct STRUCT1
BigDecimal DECIMAL1

1. Using Struct or BigDecimal in UDFs requires specifying the schema using paramSchema, returnSchema, aggregateSchema or a schema provider.

Deploying

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

The jars in the ext/ directory are only scanned at start-up, so you will need to restart your KSQL server instances to pick up new UD(A)Fs.

It is important to ensure that you deploy the custom jars to each server instance. Failure to do so will result in errors when processing any statements that try to use these functions. The errors may go unnoticed in the KSQL CLI if the KSQL server instance it is connected to has the jar installed, but one or more other KSQL servers don’t have it installed. In these cases the errors will appear in the KSQL 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.StatementExecutor:210)
io.confluent.ksql.util.KsqlException: Can't find any functions with the name 'TOSTRING'

The servers that don’t have the jars will not process any queries using the custom UD(A)Fs. Processing will continue, but it will be 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 KSQL built-in functions. The function names are case-insensitive. For example, using the multiply example above:

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;

KSQL 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 KSQL UD(A)Fs you can optionally blacklist classes and packages such that they can’t be used from a UD(A)F. There is an example blacklist that is found in the file resource-blacklist.txt that is in the ext/ directory. All the entries in it are commented out, but it demonstrates how you can use the blacklist.

This file contains an 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

java.lang.Process

This would match any paths that begin with java.lang.Process, i.e., java.lang.Process, java.lang.ProcessBuilder etc.

If you want to blacklist a single class, i.e., 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 KSQL installs a simple java security manager for UD(A)F execution. The security manager blocks attempts by any functions to fork processes from the KSQL server. It also prevents them from calling System.exit(..).

The security manager can be disabled by setting ksql.udf.enable.security.manager to false.

Disabling KSQL 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 will be collected on each invocation and will introduce some overhead to processing time.