Spark 2.3.1 Structured Streaming 状态存储内部工作

Question

I have been going through the documentation of spark 2.3.1 on structured streaming, but could not find details of how stateful operation works internally with the the state store. More specifically what i would like to know is, (1) is the state store distributed? (2) if so then how, per worker or core ?

It seems like in previous version of Spark it was per worker but no idea for now. I know that it is backed by HDFS, but nothing explained how the in-memory store actually works.

Indeed is it a distributed in-memory store ? I am particularly interested in de-duplication, if data are stream from let say a large data set, then this need to be planned as the all "Distinct" DataSet will be ultimately held in memory as the end of the processing of that data set. Hence one need to plan the size of the worker or master depending on how that state store work.

Answer 1

There is only one implementation of State Store in Structured Streaming which is backed by In-memory HashMap and HDFS. While In-Memory HashMap is for data storage, HDFS is for fault rolerance. The HashMap occupies executor memory on the worker and each HashMap represents a versioned key-value data of aggregated partition (generated after aggregator operator like deduplication, groupByy, etc)

But this does not explain how the HDFSBackedStateStore actually work. i don't see it in the documentation

You are correct that there is no such documentation available. I had to understand the code (2.3.1) , wrote an article on how State Store works internally in Structured Streaming. You might like to have a look : https://www.linkedin.com/pulse/state-management-spark-structured-streaming-chandan-prakash/