Project: Beam (App Definition and Development / Streaming & Messaging)
File: 44_batching_for_external_calls.md

Prompt: How to reduce the number of external calls when calling external services from my Apache Beam pipeline? Response: To reduce the number of external calls when calling external services from your Apache Beam pipeline, you can employ batching techniques by using the `GroupIntoBatches` transform. Batching involves aggregating multiple elements into a single payload, reducing the number of requests sent to the external service and minimizing overhead. Under the hood, the `GroupIntoBatches` transform utilizes state and timers to grant users precise control over batch size and buffering duration parameters, while abstracting away the implementation details. Key parameters include: `maxBufferDuration`: controls the maximum waiting time for a batch to be emitted. `batchSize`: determines the maximum number of elements in each batch. Elements are buffered until the specified number is reached, then emitted as a batch. `batchSizeBytes` (Java only): limits the byte size of a single batch, determined by the input coder. `elementByteSize` (Java only): sets the byte size of a single batch using a user-defined function. Additionally, you can enhance parallelism by implementing the `withShardedKey()` function, which distributes a single key across multiple threads. The following sample code snippets illustrate the use of `GroupIntoBatches` to batch elements in an Apache Beam pipeline in Java and Python. Java: ```java // Define your input PCollection PCollection<KV<String, String>> input = ...; // Specify the batch size long batchSize = 100L; // Apply GroupIntoBatches to group elements into batches of the specified size PCollection<KV<String, Iterable<String>>> batched = input .apply(GroupIntoBatches.<String, String>ofSize(batchSize)) // Set the coder for the batched PCollection .setCoder(KvCoder.of(StringUtf8Coder.of(), IterableCoder.of(StringUtf8Coder.of()))) // Apply ParDo to process each batch element .apply(ParDo.of(new DoFn<KV<String, Iterable<String>>, KV<String, String>>() { @ProcessElement public void processElement(@Element KV<String, Iterable<String>> element, OutputReceiver<KV<String, String>> r) { // Call the web service with the batch values and output the result r.output(KV.of(element.getKey(), callWebService(element.getValue()))); } })); ``` Python: ```python input_data = ... # Replace ... with your input PCollection def callwebservice(batch): pass # Implement your web service call logic here batch_size = 100 batched = ( input_data | "GroupIntoBatches" >> beam.GroupIntoBatches(batch_size) | "ProcessBatch" >> beam.Map(lambda element: (element[0], callwebservice(element[1]))) ) ``` The provided examples demonstrate the usage of `GroupIntoBatches` to organize elements into batches of a specified size. Subsequently, the code invokes the designated web service with each batch of values and produces the resulting key-value pairs. Utilizing `GroupIntoBatches` allows for the efficient processing of elements in bulk, reducing the overhead in your pipeline when making external API calls. When grouping elements into batches, keep in mind the following considerations: Batches exclusively contain elements with the same key. Batches retain elements from the same window, preserving windowing logic. A batch might encompass elements from more than one bundle. For more examples, refer to the Apache Beam documentation for the Python and Java `GroupIntoBatches` transforms.