Category: Spark

Bill Chambers and Jules Damji look at a couple of stateful scenarios within Spark Streaming:

No streaming events are free of duplicate entries. Dropping duplicate entries in record-at-a-time systems is imperative—and often a cumbersome operation for a couple of reasons. First, you’ll have to process small or large batches of records at time to discard them. Second, some events, because of network high latencies, may arrive out-of-order or late, which may force you to reiterate or repeat the process. How do you account for that?

Structured Streaming, which ensures exactly once-semantics, can drop duplicate messages as they come in based on arbitrary keys. To deduplicate data, Spark will maintain a number of user-specified keys and ensure that duplicates, when encountered, are discarded.

Just as other stateful processing APIs in Structured Streaming are bounded by declaring watermarking for late data semantics, so is dropping duplicates. Without watermarking, the maintained state can grow infinitely over the course of your stream.

In this scenario, you would still want some sort of de-duplication code at the far end of your process if you can never have duplicates come in across the lifetime of the application.  This sounds like it’s more about preventing bursty duplicates from sensors.

Burak Yavuz shares a benchmark of Spark Streaming versus Flink and Kafka Streams:

At Databricks, we used Databricks Notebooks and cluster management to set up a reproducible benchmarking harness that compares the performance of Apache Spark’s Structured Streaming, running on Databricks Unified Analytics Platform, against other open source streaming systems such as Apache Kafka Streams and Apache Flink. In particular, we used the following systems and versions in our benchmarks:

• Databricks Runtime 3.1
• Apache Flink 1.2.1
• Kafka Streams 0.10.2.1

The Yahoo Streaming Benchmark is a well-known benchmark used in industry to evaluate streaming systems. When setting up our benchmark, we wanted to push each streaming system to its absolute limits, yet keep the business logic the same as in the Yahoo Streaming Benchmark. We shared some of the results we achieved from these benchmarks during Spark Summit West 2017 keynote showing that Spark can reach 5x or higher throughput over other popular streaming systems. In this blog, we discuss in more detail about how we performed this benchmark, and how you can reproduce the results yourselves.

Standard vendor-based metric warnings aside, you can get the benchmark details at their GitHub repo.

Artem Rukavytsia shows us how you can easily integrate Akka into a solution with Kafka and Spark Streaming:

Akka gives you the opportunity to make logic for producing/consuming messages from Kafka with the Actor model. It’s very convenient if actors are widely used in your code and it significantly simplifies making data pipelines with actors. For example, you have your Akka Cluster, one part of which allows you to crawl of web pages and the other part of which makes it possible to index and send indexed data to Kafka. The consumer can aggregate this logic. Producing data to Kafka looks as follows:

The Actor model, which Akka implements, is something I kind of understand, but have never spent much time trying to implement.  I can see how it’d make perfect sense communicating with Kafka, though, given the scale and independence of consumers within a consumer group that Kafka provides.

Ayush Tiwari has an introductory tutorial on using Spark Streaming on top of Kafka:

The Spark Streaming integration for Kafka 0.10 is similar in design to the 0.8 Direct Stream approach. It provides simple parallelism, 1:1 correspondence between Kafka partitions and Spark partitions, and access to offsets and metadata. However, because the newer integration uses the new Kafka consumer API instead of the simple API, there are notable differences in usage. This version of the integration is marked as experimental, so the API is potentially subject to change.

In this blog, I am going to implement the basic example on Spark Structured Streaming & Kafka Integration.

This is a code-heavy tutorial, so check it out.

Jules Damji has a nice compendium of links and additional resources for people wanting to learn more about Apache Spark’s Structured Streaming:

Structured Streaming In Apache Spark: A new high-level API for streaming

Databricks’ engineers and Apache Spark committers Matei Zaharia, Tathagata Das, Michael Armbrust and Reynold Xin expound on why streaming applications are difficult to write, and how Structured Streaming addresses all the underlying complexities.

There’s quite a bit of reading material on the other side.