[HADOOP-1027] Fix the RAM FileSystem/Merge problems (reported in HADOOP-1014)

• Type: Bug
• Status: Closed
• Priority: Major
• Resolution: Fixed
• Fix Version/s: 0.12.0
• Assignee: Devaraj Das
• Reporter: Devaraj Das
• Created: 20/Feb/07 11:38
• Updated: 08/Jul/09 16:52
• Resolved: 26/Feb/07 20:36

Description

1) Merge algorithm implementation does not delete empty segments (sequence files with no key/val data) in cases where single level merges don't happen on those segments (due to the check "numberOfSegmentsRemaining <= factor" returning true). This affected the in-mem merge in a subtle way :-
For the in-mem merge, the merge-spill-file is given the same name as the name of the 0th entry file in the ramfs. If this file was an empty file, then it would not get deleted from the ramfs, and if the subsequent merge on ramfs chose the same name for the merge-spill-file, it would overwrite the previously created spill. This led to the inconsistent output sizes.

2) The InMemoryFileSystem has a "close" method which is not protected (only method where pathToFileAttribs map is modified without first locking the InMemoryFileSystem instance) and that quite likely leads to ConcurrentModificationException if some thread calls InMemoryFileSystem.close (due to some exception) and some other thread is in the process of doing InMemoryFileSystem.getFiles(). However, this problem will not affect the correctness of the merge process (anyway the task is going to fail) and the more important thing is that some other exception happened (like insufficient disk space and so map outputs could not be written) which may not be related to the merge process at all.

3) The number of outputs that is merged at once in RAM should be limited. This is to prevent OutOfMemory errors. Consider a case where there are 10s of thousands of maps and all maps generate empty outputs. Given the default size of the RAM FS as 75 MB, we can possibly accomodate lots of map outputs in RAM without doing any merge but it also results in the various other data structures exploding in size. We have to do a trade off here especially because the inmem-merging is done in the TaskTracker process which already is under a good amount of memory pressure.

Issue Links

• incorporates: HADOOP-1012 OutOfMemoryError in reduce
• is related to: HADOOP-1014 map/reduce is corrupting data between map and reduce