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KLIP-1: Improve UDF Interfaces

Author: agavra | Release Target: 5.3 | Status: In Discussion | Discussion: link

tl;dr: Address the most common feature requests related to UDF/UDAFs including struct support, generic types, variable arguments and complex aggregation

Motivation and background

There have been many requests (#2163) to improve the UDF/UDAF interface in KSQL. Of these features, four stand out:

  • Custom Struct Support - UDFs can only operate on standard Java types (Map, List, String, etc...) and do not accept structured types. This dramatically limits what KSQL can handle, as much of the data flowing through kafka is structured Avro/JSON.
  • Generics Type Support - Today, there is no way to implement a single UDF that supports data structures with implicit types. For example, implementing a generic list_union operator would require different method signatures for each supported type. This quickly becomes unmanageable when there are multiple arguments.
  • Variable Argument Support - Varargs are required to create UDFs that accept an arbitrary number of inputs. Creating a simple concat UDF that could sum multiple columns would require creating method signatures for as many input combinations as desired.
  • Complex Aggregation Models - Today, KSQL only supports tracking a single variable for aggregation and it must mirror the output type. To implement an avg UDAF, it is necessary to store both a count and a running_total.

Scope

All changes will be made for the new annotation-based UDF interfaces and will not be backported to the legacy implementation. The bullet points mentioned above are all in scope, but the following will not be included in this KLIP:

  • UDTFs - multiple output operations are more complicated and will require a KLIP of their own
  • Generics/Varargs for UDAFs - the support for Generics/Varargs will only extend to UDFs.

Value

Basic UDF operations that are supported in SQL languages will now be unblocked for KSQL. Some of these are outlined below:

Improvement Value
Generics support Unblock ARRAY_LENGTH, ARRAY_SLICE, ARRAY_TO_STRING, etc...
Varargs support CONCAT, MAX, MIN etc...
Complex Aggregation AVG, PERCENTILE, etc...
Structured UDFs Arbitrary data handling without intermediary transforms

Public APIS

Some public APIs will be changed, though all will be changed in a data backwards compatible fashion:

Improvement API change
Generics support UDF interface will accept inferred generics (e.g. List<T>)
Varargs support UDF interface will accept ... in the method declarations
Complex Aggregation UDAFs may include VR export(A agg) to convert aggregated to output
Structured UDFs UDF/UDAF interfaces will accept the Kafka Connect Struct type

Design

Each of these improvements is relatively self-contained and can be worked on independently and in parallel. Below are detailed designs for each:

Structured UDFs

After refactoring done in #2411, the type coercion ensures that the validation does not unnecessarily prevent usage of Struct. To complete support for sturcutred UDFS, we also need to have some mechanism to create the output schema. To address this, we can resolve the schema as part of the UDF specification, requiring users to specify the struct schema inline (which will be parsed using the same ANTLR grammar).

@UdfSchema
public static final Schema SCHEMA = SchemaBuilder.struct()...build();

@Udf(schema="STRUCT<val VARCHAR, length INT>")
public Struct generate(
    @UdfParam(schema="STRUCT<val VARCHAR>") final Struct from
  ) {
  return new Struct(...);
}

UdfFactory could then load the correct UDF given the schema by matching the schema of the type against various different methods. It will be possible to support multiple methods with different struct schemas if the name of the udf method is changed (note that the java method name is ignored from actual evaluation).

Generics

Today, we eagerly convert UDF output types to corresponding Connect schema types at the time that we compile the UDFs and not the time that we use them in a SELECT clause. Since there is no corresponding "unresolved" type in Connect, we fail this conversion. To solve this problem, we can infer the type from the source schema and compile the code to work with any component type (e.g. omit the generics from the compiled code).

@Udf("returns a sublist of 'list'")
public <T> List<T> sublist(
    @UdfParameter final List<T> list, 
    @UdfParameter final int start, 
    @UdfParameter final int end) {
  return list.subList(start, end);
}

We need to ensure that the output type can be inferred. This means that we need to fail if the signature is something like <T> List<T> convert(List<String> list). This can be done during compilation by following a simple rule: any generic type used in the output must be present in at least one of the parameters. We can access this information via reflection.

The DESCRIBE FUNCTION call to describe such generic values will display a SQL type that is not supported in DDL statements (e.g. ARRAY<ANY<T>>) (See documentation section).

NOTE: Supporting a wildcard output type is not covered by this design since we would not be able to generate the output schema for select statements, however supporting wildcards in the input types (e.g. Long length(List<?> list)) should be possible.

Varargs

Varargs boils down to supporting native Java arrays as parameters and ensuring that component types are resolved properly. Anytime a method is registered with variable args (e.g. String... args) we will register a corresponding function with the wrapping array type (e.g. String[]).

We will need to resolve methods such as foo(Int, String...) and foo(String, String...) as well as accept empty arguments to foo(String...). If any parameter is null, it will be considered valid for any vararg declaration.

@Udf("sums all arguments in 'args'")
public int sum(@UdfParameter final int... args) {
  return Arrays.stream(args).sum();
}

Complex Aggregation

Complex aggregation for UDAFs requires the ability to aggregate on a value that is later reduced to a value of a different type. To support this, we will change the Udaf interface in a backwards incompatible way (code built using the old Udaf interface will not compile, but migration is straightforward):

/**
* ...
* @param <V> value type (the value that is accepted)
* @param <A> aggregate type
* @param <VR> reduce type (the value to return)
*/
public interface Udaf<V, A, VR> {
  
  ...
 
  /**
   * Redcues an aggregate into the return type.
   * 
   * @param aggregate the running aggregate
   * @return the final return value
   */ 
  VR reduce(A aggregate);
  
}

reduce will be taken as the behavioral parameter to a mapValues task that will be applied after the aggregate task in the generated KStreams topology.

Test plan

Each of these improvements will come with some built-in UDF/UDAF examples. These examples will then be comprehensively covered by both unit tests as well as corresponding Query Translation Tests. Specifically, the UDFs described in the Value section will all be implemented alongside this KLIP. For the new UDAF complex aggregation, we will ensure that the generated topologies do not include any unexpected steps such as a repartition.

Documentation Updates

Varargs

  • The Example UDF Class in udf.rst will be extended to showcase the new features. Namely, we will replace the multiply double method to include varargs:
     @Udf(description = "multiply N non-nullable DOUBLEs.")
     public double multiply(final double... values) {
       return Arrays.stream(values).reduce((a, b) -> a * b);;
     }
  • The DESCRIBE FUNCTION for vararg functions will need to be updated to use the ... syntax:
Name        : ARRAYCONTAINS
Author      : Confluent
Type        : scalar
Jar         : internal
Variations  :

	Variation   : ARRAYCONTAINS(VARCHAR... )
	Returns     : BOOLEAN

Struct Support for UDF

  • The Supported Types section in udf.rst will include Struct and have an updated note at the bottom which reads:

Note: Complex types other than List, Map and Struct are not currently supported. Generic types for List and Map are supported, but component elements must be of one of the supported types. If the types are generic, the output type must be able to be inferred from one or more of the input parameters.

  • Function descriptions as part of DESCRIBE FUNCTION should also show the schema for any Struct that is used:
Name        : FOO
Author      : Confluent
Type        : scalar
Jar         : internal
Variations  :

      Variation   : FOO(STRUCT<'VAL' BIGINT, 'OTHER' VARCHAR>)
      Returns     : STRUCT<'VAL' BIGINT, 'ANOTHER' LIST<VARCHAR>)

Generics

  • The DESCRIBE FUNCTION should indicate when a function accepts generics using ANY<?> where ? is a letter that begins with T1 and is incremented for each inferred type.
Name        : FOO
Author      : Confluent
Type        : scalar
Jar         : internal
Variations  :

      Variation   : FOO(ARRAY<ANY<T1>>, ANY<T2>)
      Returns     : ANY<T1>

Complex Aggregation

  • The UDAF section in udf.rst will update the example from Sum to Average and include non-trivial usage of the reduce API:

The class below creates a UDAF named my_average...

.. code:: java

@UdafDescription(name="my_average", description="averages")
public class AverageUdaf {
 
 private static final Schema SCHEMA = SchemaBuilder.struct()
                                        .field("sum", Schema.INT64_SCHEMA)
                                        .field("count", Schema.INT32_SCHEMA)
                                        .build();

 @UdafFactory(description = "averages longs")
 public static TableUdaf<Long, Struct, Double> createAverageLong() {
   return new TableUdaf<Long, Struct, Double>() {
     @Override
     public Struct initialize() { 
       return new Struct(SCHEMA).put("sum", 0L).put("count", 0); 
     }
     
     @Override
     public Struct aggregate(final Long value, final Struct aggregate) {
       return new Struct(SCHEMA)
                 .put("sum", aggregate.get("sum") + value)
                 .put("count", aggregate.get("count") + 1);
     }
     
     @Override
     public Struct undo(final Long valueToUndo, final Struct aggregate) {
       return new Struct(SCHEMA)
                 .put("sum", aggregate.get("sum") - valueToUndo)
                 .put("count", aggregate.get("count") - 1);
     }
     
     @Override
     public Struct merge(final Struct aggOne, final Struct aggTwo) {
       return new Struct(SCHEMA)
                 .put("sum", aggOne.get("sum") + aggTwo.get("sum"))
                 .put("count", aggOne.get("count") + aggTwo.get("count"));
     }
     
     @Override
     public Double reduce(final Struct aggregate) {
       return ((Double) aggregate.getInt64("sum")) / aggregate.getInt32("count");
     }
   };
 }
}
  • For exportable UDAFs, we will also need to generate updated documentation for DESCRIBE FUNCTION calls to make sure that the output value is not the intermediate value, but rather the final exported value. For example:
ksql> DESCRIBE FUNCTION AVERAGE;

Name        : AVERAGE
Author      : Confluent
Overview    : Returns the average value for a window.
Type        : aggregate
Jar         : internal
Variations  :

  Variation   : AVERAGE(BIGINT)
  Returns     : DOUBLE
// Note that the signature for this is actually Udaf<Long, Struct, DOUBLE> as seen above
  • Syntax reference needs to be updated to reflect any new UDF/UDAFs that are implemented as part of this KLIP.

Compatibility implications

  • Custom UDAF implementations will fail to compile. Upgrade notes will include a section detailing the following upgrade semantics:

To update to 5.3, any custom UDAF implementations must make the following changes in order to compile:

  1. Add a third type parameter to your class signature that is equivalent to the second (e.g. MyUdaf<String, Long> -> MyUdaf<String, Long, Long>)
  2. Implement the VR reduce(A aggregate) method. Since old implementations of UDAf always had VR == A, this can simply be return aggregate;.

There will be no changes to the functionality of your custom UDAF.

Performance implications

N/A

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