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.
If you are a newcomer in the distributed world, someone certainly told you that shuffle is bad and will slow down your processing. But what does it mean? What happens when this infamous shuffle exists in your code? In this article you should find some answers for the shuffle in Apache Spark.
Apache Spark has this great capacity to optimize joins of bucketed tables but does it work on partitions as well? No, and to understand why, I invite you to read the following sections of this blog post ?
Another stateful operation requiring the state store is drop duplicates. You can use it to deduplicate your streaming data before pushing it to the sink.
Have you ever wondered what is the relationship between drop and select operations in Apache Spark SQL? If not, I will shed some light on them in this short blog post.
It's the second follow-up Data+AI Summit post but the first one focusing on the stateful operations and their interaction with the state store.
The main Apache Spark component enabling stateful processing is StateStoreRDD. It creates a partition-based state store instance but also triggers state-based computation.
I believe Kubernetes is the next big step in the framework after proposing Catalyst Optimizer, modernizing streaming processing with Structured Streaming, and introducing Adaptive Query Execution. Especially that Apache Spark 3 brings a lot of changes in this part!
Some time ago @ArunJijo36 mentioned me on Twitter with a question about broadcasting in Structured Streaming. If, like me at this time, you don't know what happens, I think that this article will be good for you 👊
GPU-awareness was one of the topics I postponed the most in my Apache Spark 3.0 exploration. But its time has come and in this blog post you will discover what changed in the version 3 of the framework regarding the GPU-based computation.
Few months ago, before the Apache Spark 3.0 features series, you probably noticed a short series about files processing in Structured Streaming. If you enjoyed it, here is a complementary note presenting the file data source :)
Apache Kafka changes in Apache Spark 3.0 was one of the first topics covered in the "what's new" series. Even though there were a lot of changes related to the Kafka source and sink, they're not the single ones in Structured Streaming.
Apart from data processing-related changes, Apache Spark 3.0 also brings some changes at the UI level. The interface is supposed to be more intuitive and should help you understand processing logic better!
There are stories like this, the stories that remain in the backlog for a very long time, and finally, they get implemented. That's exactly what happened with the Dynamic Partition Pruning feature added, after almost 4 years in the backlog, to Apache Spark 3.
The Local shuffle reader presented in one of the previous posts might have introduced some doubt in the way how the broadcast join is working. If it's the case, this blog post should shed some light on it. If not, it can give you more in-depth details than the ones introducing this type of join a few years ago.
If you noticed that some filter expressions weren't pushed down to your Apache Parquet files, the situation should change in Apache Spark 3.0. The new release supports this feature called nested data predicate pushdown.
All the operations from the title are natively available in relational databases but doing them with distributed data processing systems is not obvious. Starting from 3.0, Apache Spark gives a possibility to implement them in the data sources.
It's the last part of the series about the Adaptive Query Execution in Apache Spark SQL. So far you learned about the physical plan optimizations. But they're not alone and you will see that in this blog post.
So far you learned about skew optimization and coalesce shuffle partition optimizations made by the Adaptive Query Execution engine. But they're not the single ones and the next one you will discover is also related to the shuffle.
Apart from big and complex changes in the Adaptive Query Execution like skews or partitions coalescing, there are also some others, less complex. Although their smaller complexity, it doesn't mean they are not important. Especially when one of these changes offers a reuse of the subqueries.
Shuffle partitions coalesce is not the single optimization introduced with the Adaptive Query Execution. Another one, addressing maybe one of the most disliked issues in data processing, is joins skew optimization that you will discover in this blog post.
