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From "Ahn, Daniel" <daniel....@optum.com.INVALID>
Subject [Structured Streaming] Checkpoint file compact file grows big
Date Thu, 16 Apr 2020 00:19:24 GMT
Are Spark Structured Streaming checkpoint files expected to grow over time indefinitely? Is
there a recommended way to safely age-off old checkpoint data?

Currently we have a Spark Structured Streaming process reading from Kafka and writing to an
HDFS sink, with checkpointing enabled and writing to a location on HDFS. This streaming application
has been running for 4 months and over time we have noticed that with every 10th job within
the application there is about a 5 minute delay between when a job finishes and the next job
starts which we have attributed to the checkpoint compaction process. At this point the .compact
file that is written is about 2GB in size and the contents of the file show it keeps track
of files it processed at the very origin of the streaming application.

This issue can be reproduced with any Spark Structured Streaming process that writes checkpoint
files.

Is the best approach for handling the growth of these files to simply delete the latest .compact
file within the checkpoint directory, and are there any associated risks with doing so?


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