[SPARK-30462] Structured Streaming _spark_metadata fills up Spark Driver memory when having lots of objects

• Type: Bug
• Status: Resolved
• Priority: Major
• Resolution: Fixed
• Affects Version/s: 2.4.3, 2.4.4, 3.0.0
• Fix Version/s: 3.1.0
• Component/s: Structured Streaming
• Assignee: Jungtaek Lim
• Reporter: Vladimir Yankov
• Created: 08/Jan/20 14:57
• Updated: 20/Aug/20 09:31
• Resolved: 20/Aug/20 09:31

Description

Hi,

With the current implementation of the Spark Structured Streaming it does not seem to be possible to have a constantly running stream, writing millions of files, without increasing the spark driver's memory to dozens of GB's.

In our scenario we are using Spark structured streaming to consume messages from a Kafka cluster, transform them, and write them as compressed Parquet files in an S3 Objectstore Service.
Each 30 seconds a new batch of the spark-streaming is writing hundreds of objects, which respectively results within time to millions of objects in S3.
As all written objects are recorded in the _spark_metadata, the size of the compact files there grows to GB's that eventually fill up the Spark Driver's memory and lead to OOM errors.

We need the functionality to configure the spark structured streaming to run without loading all the historically accumulated metadata in its memory.
Regularly resetting the _spark_metadata and the checkpoint folders is not an option in our use-case, as we are using the information from the _spark_metadata to have a register of the objects for faster querying and search of the written objects.

Issue Links

• relates to: SPARK-24295 Purge Structured streaming FileStreamSinkLog metadata compact file data.
• relates to: SPARK-29995 Structured Streaming file-sink log grow indefinitely
• relates to: SPARK-27188 FileStreamSink: provide a new option to have retention on output files
• links to: [Github] Pull Request #28904 (HeartSaVioR)
• links to: [Github] Pull Request #28904 (HeartSaVioR)