A 3-day bug hunt on a 3-person team costs up to β¬7,200 in lost engineering time. This workshop teaches you to prevent that β unit tests, data tests, and integration tests for PySpark and Databricks Lakeflow, including Spark Declarative Pipelines.
Recently we discovered the concept of state stores used to deal with stateful aggregations in Structured Streaming. But at that moment we didn't spend the time on these aggregations. As promised, they'll be described now.
Streaming stateful processing in Apache Spark evolved a lot from the first versions of the framework. At the beginning was updateStateByKey but some time after, judged inefficient, it was replaced by mapWithState. With the arrival of Structured Streaming the last method was replaced in its turn by mapGroupsWithState.
During my last Spark exploration of the RPC implementation one class caught my attention. It was StateStoreCoordinator used by the state store that is an important place in Structured Streaming pipelines.
Structured Streaming introduced a lot of new concepts regarding to the DStream-based streaming. One of them is the output mode.
The idea of watermark was firstly presented in the occasion of discovering the Apache Beam project. However it's also implemented in Apache Spark to respond to the same problem - the problem of late data.
Some last weeks I was focused on Apache Beam project. After some readings, I discovered a lot of similar concepts between Beam and Spark Structured Streaming (or inversely?). One of this similarities are triggers.
The error quoted in the title of this post is quite common when you want to copy conception logic from Spark DStream/RDD to Spark structured streaming. This post makes some insight on it.
Spark 2.2.0 brought the change of structured streaming state. Between 2.0 and 2.2.0 it was marked as "alpha". But the last version changed this status to General Availability. It's so a good moment to start to play with this new feature - even if some basics have already been covered in the post about structured streaming. This time we'll go deeper and analyze the integration with Apache Kafka that will be helpful to
Project Tungsten, explained in one of previous posts, brought a lot of optimizations - especially in terms of memory use. Until now it was essentially used by Spark SQL and Spark MLib projects. However, since 2.0.0, some work was done to integrate DataFrame/Dataset in streaming processing (Spark Streaming).
