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  1. Spark
  2. SPARK-21571

Spark history server leaves incomplete or unreadable history files around forever.

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    Details

    • Type: Bug
    • Status: Resolved
    • Priority: Minor
    • Resolution: Fixed
    • Affects Version/s: 2.2.0
    • Fix Version/s: 2.3.0
    • Component/s: Scheduler
    • Labels:
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      Description

      We have noticed that history server logs are sometimes never cleaned up. The current history server logic ONLY cleans up history files if they are completed since in general it doesn't make sense to clean up inprogress history files (after all, the job is presumably still running?) Note that inprogress history files would generally not be targeted for clean up any way assuming they regularly flush logs and the file system accurately updates the history log last modified time/size, while this is likely it is not guaranteed behavior.

      As a consequence of the current clean up logic and a combination of unclean shutdowns, various file system bugs, earlier spark bugs, etc. we have accumulated thousands of these dead history files associated with long since gone jobs.

      For example (with spark.history.fs.cleaner.maxAge=14d):

      rw-rw--- 3 xxxxxx ooooooo 14382 2016-09-13 15:40 /user/hadoop/xxxxxxxxxxxxxx/spark/logs/qqqqqq1974_ppppppppppp-8812_110586000000195_dev4384_jjjjjjjjjjjj-53982.zstandard
      rw-rw--- 3 xxxx ooooooo 5933 2016-11-01 20:16 /user/hadoop/xxxxxxxxxxxxxx/spark/logs/qqqqqq2016_ppppppppppp-8812_126507000000673_dev5365_jjjjjjjjjjjj-65313.lz4
      rw-rw--- 3 yyy ooooooo 0 2017-01-19 11:59 /user/hadoop/xxxxxxxxxxxxxx/spark/logs/yyyyyyyyyyyyyyyy0057_zzzz326_mmmmmmmmm-57863.lz4.inprogress
      rw-rw--- 3 xxxxxxxxx ooooooo 0 2017-01-19 14:17 /user/hadoop/xxxxxxxxxxxxxx/spark/logs/yyyyyyyyyyyyyyyy0063_zzzz688_mmmmmmmmm-33246.lz4.inprogress
      rw-rw--- 3 yyy ooooooo 0 2017-01-20 10:56 /user/hadoop/xxxxxxxxxxxxxx/spark/logs/yyyyyyyyyyyyyyyy1030_zzzz326_mmmmmmmmm-45195.lz4.inprogress
      rw-rw--- 3 xxxxxxxxxxxx ooooooo 11955 2017-01-20 17:55 /user/hadoop/xxxxxxxxxxxxxx/spark/logs/yyyyyyyyyyyyyyyy1314_wwww54_kkkkkkkkkkkkkk-64671.lz4.inprogress
      rw-rw--- 3 xxxxxxxxxxxx ooooooo 11958 2017-01-20 17:55 /user/hadoop/xxxxxxxxxxxxxx/spark/logs/yyyyyyyyyyyyyyyy1315_wwww1667_kkkkkkkkkkkkkk-58968.lz4.inprogress
      rw-rw--- 3 xxxxxxxxxxxx ooooooo 11960 2017-01-20 17:55 /user/hadoop/xxxxxxxxxxxxxx/spark/logs/yyyyyyyyyyyyyyyy1316_wwww54_kkkkkkkkkkkkkk-48058.lz4.inprogress

      Based on the current logic, clean up candidates are skipped in several cases:
      1. if a file has 0 bytes, it is completely ignored
      2. if a file is in progress and not paresable/can't extract appID, is it completely ignored
      3. if a file is complete and but not parseable/can't extract appID, it is completely ignored.

      To address this edge case and provide a way to clean out orphaned history files I propose a new configuration option:

      spark.history.fs.cleaner.aggressive=

      {true, false}

      , default is false.

      If true, the history server will more aggressively garbage collect history files in cases (1), (2) and (3). Since the default is false, existing customers won't be affected unless they explicitly opt-in. If customers have similar leaking garbage over time they have the option of aggressively cleaning up in such cases. Also note that aggressive clean up may not be appropriate for some customers if they have long running jobs that exceed the cleaner.maxAge time frame and/or have buggy file systems.

      Would like to get feedback on if this seems like a reasonable solution.

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            • Assignee:
              ericvandenbergfb Eric Vandenberg
              Reporter:
              ericvandenbergfb Eric Vandenberg
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                Updated:
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