User-defined Functions

User-defined functions (UDFs) in Apache Beam Java allow you to apply custom logic to your data as part of your data processing pipelines. UDFs can be used to transform, filter, aggregate, or perform any other operations on your input data.

To define a UDF in Java, you need to create a new class that implements the SerializableFunction interface from the Apache Beam SDK. This interface has a single method apply() which takes an input element and returns the transformed output.

Here’s an example of a simple UDF that converts a string to uppercase:

import org.apache.beam.sdk.transforms.SerializableFunction;

public class UppercaseFn implements SerializableFunction<String, String> {
  @Override
  public String apply(String input) {
    return input.toUpperCase();
  }
}

Once you have defined your UDF, you can use it in your Beam pipeline by applying the ParDo transform, which is used to apply transformations to individual elements of your input data. Here’s an example:

PCollection<String> input = ... // your input PCollection
PCollection<String> output = input.apply(ParDo.of(new UppercaseFn()));

Data Transformations

Apache Beam Java provides a wide range of built-in transformations that you can use to process and manipulate your data. These transformations are available as methods on the PCollection class, allowing you to chain them together to create complex data processing pipelines.

Some common data transformations in Apache Beam Java include MapElements, Filter, FlatMap, GroupByKey, and Combine. Each transformation has specific inputs and outputs and can be applied to one or more PCollections.

Here’s an example that demonstrates the use of MapElements transformation to transform a PCollection of integers into a PCollection of strings:

PCollection<Integer> input = ... // your input PCollection
PCollection<String> output = input.apply(MapElements.into(TypeDescriptors.strings()).via(Object::toString));

In this example, the MapElements transform takes a simple lambda function Object::toString to convert each integer into a string representation.

By combining these user-defined functions and built-in transformations, you can create powerful and flexible data processing pipelines in Apache Beam Java.

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