jto opened a new pull request, #28045:
URL: https://github.com/apache/beam/pull/28045
In the current implementation of file sources, the Flink runner will try to
create a number of input splits equal to `source estimated size / parallelism`.
Most of the time, the resulting desired number of splits will be smaller than
the total number of files in the source. In that case each individual file
become a full split. This creates an issue when the size of individual files
varies a lot. At Spotify we commonly read datasets stored on GCS where
individual files range from a few Mb to 1 or 2 Gb.
Because of this, work distribution is sub-optimal because some workers will
be consuming larges splits while other worker consume smaller splits.
This PR limits the maximum size of each split to 128Mb (configurable), which
is a decent tradeoff between balancing work and limiting the overhead of
consuming splits. I believe Dataflow uses the same strategy.
In our test on real workflows, we measured an **improvement in performance
of up to 20%**. Of course the overall improvement varies a lot from one
workflow to another, but we never encountered a case where performances where
noticeably degraded.
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