Batch Analytics with Apache Flink: The New Challenger

Previous: Structured Streaming: The Modern Way to Handle Data Streams

We’ve spent a lot of time on Spark, and for good reason - it’s amazing. But if you’re serious about big data, you need to know about Apache Flink. In Chapter 8, Sridhar Alla introduces us to the technology that many experts consider the “true” successor to MapReduce for real-time processing.

What Makes Flink Different?

While Spark was built for batch processing and added streaming later, Flink was built from the ground up for streaming. To Flink, batch processing is just a special case of streaming where the data has a beginning and an end.

This architectural difference gives Flink some serious advantages:

• Low Latency: Flink can process events one-by-one with very little delay.
• Accurate Results: It’s incredibly good at handling out-of-order data.
• Fault Tolerance: Flink can recover from failures while maintaining “exactly-once” state, making it extremely reliable.

Bounded vs. Unbounded Data

The book clarifies two important terms:

• Unbounded Datasets: These are infinite (like a Twitter feed). You never “finish” processing them.
• Bounded Datasets: These are finite (like a CSV file from last month).

Flink uses the DataSet API for bounded data and the DataStream API for unbounded data. In this post, we’re focusing on the DataSet API for batch analytics.

Getting Started with Flink

Setting up Flink is pretty simple. You download the binaries, extract them, and run a single script: ./bin/start-local.sh.

This gives you access to the Flink Dashboard at http://localhost:8081. It’s a really slick web UI where you can monitor your jobs, see how they’re being executed, and debug any issues.

The Scala Shell

Just like Spark, Flink has a Scala shell that lets you experiment with data interactively. Loading a file into a Flink DataSet looks very similar to Spark:

val dataSet = benv.readTextFile("OnlineRetail.csv")
dataSet.count()

You can then perform transformations like map, filter, and groupBy. For example, you can split a string and grab a specific column in just a few lines of code.

Flink might not have the same level of name recognition as Spark yet, but its architecture is incredibly elegant. In the next post, we’ll dive deeper into the DataSet API and see how to perform complex joins and aggregations.

Next: Flink DataSet API: Transformations, Joins, and Aggregations