Details
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Improvement
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Status: Resolved
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Major
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Resolution: Fixed
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None
Description
When writing data, a gap was observed between spark stages. By tracking down where the time was spent on the spark driver, it's get-small-files operation for partitions.
When creating the UpsertPartitioner and trying to assign insert records, it uses a normal for-loop for get the list of small files for all partitions that the load is going to load data to, and the process is very slow when there are a lot of partitions to go through. While the operation is running on spark driver process, all other worker nodes are sitting idle waiting for tasks.
For all those partitions, they don't affect each other, so the get-small-files operations can be parallelized. The change I made is to pass the JavaSparkContext to the UpsertPartitioner, and create RDD for the partitions and eventually send the get small files operations to multiple tasks.
screenshot attached for
the gap without the improvement
the spark stage with the improvement (no gap)
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