Data processing articles

Looking for something else? Check the categories of Data processing:

Apache Beam Apache Flink Apache Spark Apache Spark GraphFrames Apache Spark GraphX Apache Spark SQL Apache Spark Streaming Apache Spark Structured Streaming PySpark

If not, below you can find all articles belonging to Data processing.

Apache Flink best practices - Flink Forward lessons learned

I won't hide it, I'm still a fresher in the Apache Flink world and despite my past streaming experiences with Apache Spark Structured Streaming and GCP Dataflow, I need to learn. And to learn a new tool or concept, there is nothing better than watching some conference talks!

Continue Reading →

_spark_metadata in Apache Spark Structured Streaming issue is no more!

There are probably not that many people working today on the flat files with Structured Streaming than 5 years ago thanks to the table file formats. However, if you are in this group and are still generating CSVs or JSONs with the streaming sink, brace yourself, the memory problems are coming if you don't take action!

Continue Reading →

The first state in Apache Spark Structured Streaming arbitrary stateful processing

When you wrote your first arbitrary stateful processing pipelines, the state expiration is maybe the first tricky point you had to deal with. Why is that? After all, it's just about setting the timeout, doesn't it? Most of the time, yes, but there is an exception.

Continue Reading →

State expiration in stream-to-stream joins with event time range condition

You certainly know it, the watermark (aka GC Watermark) is responsible for cleaning state store in Apache Spark Structured Streaming. But you may not know that it's not the single time-based condition. There is a different one involved in the stream-to-stream joins.

Continue Reading →

How to initialize state in Apache Spark Structured Streaming stateful jobs?

Starting from Apache Spark 3.2.0 is now possible to load an initial state of the arbitrary stateful pipelines. Even though the feature is easy to implement, it hides some interesting implementation details!

Continue Reading →

Multiple queries running in Apache Spark Structured Streaming

That's often a dilemma, whether we should put multiple sinks working on the same data source in the same or in different Apache Spark Structured Streaming applications? Both solutions may be valid depending on your use case but let's focus here on the former one including multiple sinks together.

Continue Reading →

Yes, I'm learning Apache Flink - beginner's problems

Surprised? You shouldn't. I've always been eager to learn, including 5 years ago when for the first time, I left my Apache Spark comfort zone to explore Apache Beam. Since then I had a chance to write some Dataflow streaming pipelines to fully appreciate this technology and work on AWS, GCP, and Azure. But there is some excitement for learning-from scratch I miss.

Continue Reading →

What's new in Apache Spark 3.4.0 - shuffle changes

Shuffle is a permanent point in the What's new in Apache Spark series. Why? It's often one the most time consuming part of the jobs and knowing the improvement simply helps writing better pipelines.

Continue Reading →

What's new in Apache Spark 3.4.0 - Spark Connect

Spark Connect is probably the most expected feature in Apache Spark 3.4.0. It was announced in the Data+AI Summit 2022 keynotes and has a lot of coverage in social media right now. I'll try to add my small contribution to this by showing some implementation details.

Continue Reading →

What's new in Apache Spark 3.4.0 - Structured Streaming

The asynchronous progress tracking and correctness issue fixes presented in the previous blog posts are not the single new feature in Apache Spark Structured Streaming 3.4.0. There are many others but to keep the blog post readable, I'll focus here only on 3 of them.

Continue Reading →

What's new in Apache Spark 3.4.0 - Structured Streaming and correctness issue

Apache Spark is infamous for its correctness issue for chained stateful operations. Fortunately things get improved in each release. The most recent one, the 3.4.0, also got some important changes on that field!

Continue Reading →

What's new in Apache Spark 3.4.0 - Async progress tracking for Structured Streaming

Finally, the time has come to start the analysis of the new features in Apache Spark. The first of them that grabbed my attention was the Async progress tracking from Structured Streaming.

Continue Reading →

Spark SQL checkpoints

In my long - but not long enough! - journey with Apache Spark I've met the "checkpointing" world in the context of Structured Streaming mostly. But this term also applies to other modules including Apache Spark SQL, so batch processing!

Continue Reading →

Introduction to Apache Spark History

If you need to go back in time and analyze your past Apache Spark applications, you can use the native Apache Spark History server. However, it can also be an infrastructure problem because of the continuously increasing historical logs for streaming applications. In this blog post we'll try to understand this component and to see different configuration options.

Continue Reading →

Filtering rules accumulator

Data can have various quality issues, from missing to badly formatted values. However, there is another issue less people talk about, the erroneous filtering logic.

Continue Reading →

Apache Spark as you don't know it

It's difficult to see all the use cases of a framework. Back in time, when I was a backend engineer, I never succeeded to see all applications of Spring framework. Now, when I'm a data engineer, I feel the same for Apache Spark. Fortunately, the community is there to show me some outstanding features!

Continue Reading →

Shuffle in PySpark

Shuffle is for me a never-ending story. Last year I spent long weeks analyzing the readers and writers and was hoping for some rest in 2022. However, it didn't happen. My recent PySpark investigation led me to the shuffle.py file and my first reaction was "Oh, so PySpark has its own shuffle mechanism?". Let's check this out!

Continue Reading →

Serializers in PySpark

We've learned in the previous PySpark blog posts about the serialization overhead between the Python application and JVM. An intrinsic actor of this overhead are Python serializers that will be the topic of this article and hopefully, will provide a more complete overview of the Python <=> JVM serialization.

Continue Reading →

Generated method too long to be JIT compiled

There are days like that. You inherit a code and it doesn't really work as expected. While digging into issues you find usual weird warnings but also several new things. For me one of these things was the "Generated method too long to be JIT compiled..." info message.

Continue Reading →

Apache Spark listeners

Message bus is a common architectural design in the Enterprise Design Patterns. But it's also present at a lower level to enable the event-driven behavior. Apache Spark is not an exception. It uses a publish/subscribe approach in various places.

Continue Reading →