When it comes to processing large amounts of data, two popular approaches are streaming and batch processing. Apache Beam is a unified programming model that allows you to write code that can run on both batch and streaming processing engines. In this blog post, we will explore the differences between streaming and batch processing using the Apache Beam Java SDK.

Streaming Processing

Streaming processing refers to the continuous and real-time processing of data as it becomes available. This approach is commonly used for applications that require near real-time analytics or monitoring. Streaming data is processed in small increments called “windows” or “micro-batches”.

Apache Beam provides abstractions for handling streaming data through its windowing and triggering capabilities. Windowing allows you to group data into logical windows based on time or other specified criteria. Triggers define when to emit results based on the data that arrives within a particular window.

Here’s an example of streaming processing using Apache Beam Java SDK:

PipelineOptions options = PipelineOptionsFactory.create();
Pipeline pipeline = Pipeline.create(options);

pipeline
    .apply("ReadFromPubSub", PubsubIO.readStrings().fromTopic("topic"))
    .apply(Window.into(FixedWindows.of(Duration.standardMinutes(1))))
    .apply(Count.perElement())
    .apply("WriteToBigQuery", BigQueryIO.write().to("table").withSchema(schema));

pipeline.run().waitUntilFinish();

In this example, we read data from a Pub/Sub topic, window it into fixed windows of 1 minute, count the occurrences of each element within the window, and then write the results to BigQuery.

Batch Processing

Batch processing, on the other hand, refers to processing data in large, finite sets. This approach is commonly used for offline data processing or when real-time processing is not required. Batch processing allows you to process large volumes of data in a cost-effective manner.

Apache Beam simplifies batch processing by providing a programming model that treats batch processing as a special case of streaming processing. You can write your code using the same programming model and APIs used for streaming processing, with minor adjustments to handle finite input data.

Here’s an example of batch processing using Apache Beam Java SDK:

PipelineOptions options = PipelineOptionsFactory.create();
Pipeline pipeline = Pipeline.create(options);

pipeline
    .apply("ReadFromText", TextIO.read().from("input.txt"))
    .apply(Count.perElement())
    .apply("WriteToText", TextIO.write().to("output.txt"));

pipeline.run().waitUntilFinish();

In this example, we read data from a text file, count the occurrences of each element, and then write the results back to a text file.

Conclusion

Both streaming and batch processing have their own strengths and use cases. Streaming processing is ideal for real-time analytics and monitoring, while batch processing is suitable for offline data processing. With the Apache Beam Java SDK, you can write code that is portable across different processing engines and easily switch between streaming and batch processing modes.

#bigdata #apachbeam