[GitHub] spark issue #17276: [WIP][SPARK-19937] Collect metrics of block sizes when s...
Github user jinxing64 commented on the issue: https://github.com/apache/spark/pull/17276 @mridulm @squito Thanks a lot for taking time review this pr. I will close it for now and make another one if there is progress. --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] spark issue #17276: [WIP][SPARK-19937] Collect metrics of block sizes when s...
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[GitHub] spark issue #17276: [WIP][SPARK-19937] Collect metrics of block sizes when s...
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[GitHub] spark issue #17276: [WIP][SPARK-19937] Collect metrics of block sizes when s...
Github user jinxing64 commented on the issue: https://github.com/apache/spark/pull/17276 @squito Thanks a lot for taking time looking into this pr. I updated the pr. Currently just add two metrics: a) the total size of underestimated blocks size, b) the size of blocks shuffled to memory. For a), executor use `maxBytesInFlight` to control the speed of shuffle-read. I agree with your comment `another metric that may be nice to capture here is maximum underestimate`. But think about this scenario: the maximum is small, but thousands of blocks are underestimated, thus `maxBytesInFlight` cannot help avoid the OOM during shuffle-read. That's why I proposed to track the metrics of total size of underestimated blocks size; For b), currently all data are shuffled-read to memory. If we add the feature of shuffling to disk when memory shortage, we need to evaluate the performance. I think another two metrics need to be taken into account: the size of blocks shuffled to disk(to be added in another pr) and task's running time(already exist). The more data shuffled to memory, the better performance; The shorter time cost, the better performance. I also added some log for debug in `ShuffleWriter`, including the num of underestimated blocks and the size distribution. --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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