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From "Jonathan Gray (JIRA)" <j...@apache.org>
Subject [jira] Created: (HBASE-2375) Make decision to split based on aggregate size of all StoreFiles and revisit related config params
Date Thu, 25 Mar 2010 15:23:27 GMT
Make decision to split based on aggregate size of all StoreFiles and revisit related config
params
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                 Key: HBASE-2375
                 URL: https://issues.apache.org/jira/browse/HBASE-2375
             Project: Hadoop HBase
          Issue Type: Improvement
          Components: regionserver
    Affects Versions: 0.20.3
            Reporter: Jonathan Gray
            Priority: Critical
             Fix For: 0.20.4, 0.21.0


Currently we will make the decision to split a region when a single StoreFile in a single
family exceeds the maximum region size.  This issue is about changing the decision to split
to be based on the aggregate size of all StoreFiles in a single family (but still not aggregating
across families).  This would move a check to split after flushes rather than after compactions.
 This issue should also deal with revisiting our default values for some related configuration
parameters.

The motivating factor for this change comes from watching the behavior of RegionServers during
heavy write scenarios.

Today the default behavior goes like this:
- We fill up regions, and as long as you are not under global RS heap pressure, you will write
out 64MB (hbase.hregion.memstore.flush.size) StoreFiles.
- After we get 3 StoreFiles (hbase.hstore.compactionThreshold) we trigger a compaction on
this region.
- Compaction queues notwithstanding, this will create a 192MB file, not triggering a split
based on max region size (hbase.hregion.max.filesize).
- You'll then flush two more 64MB MemStores and hit the compactionThreshold and trigger a
compaction.
- You end up with 192 + 64 + 64 in a single compaction.  This will create a single 320MB and
will trigger a split.
- While you are performing the compaction (which now writes out 64MB more than the split size,
so is about 5X slower than the time it takes to do a single flush), you are still taking on
additional writes into MemStore.
- Compaction finishes, decision to split is made, region is closed.  The region now has to
flush whichever edits made it to MemStore while the compaction ran.  This flushing, in our
tests, is by far the dominating factor in how long data is unavailable during a split.  We
measured about 1 second to do the region closing, master assignment, reopening.  Flushing
could take 5-6 seconds, during which time the region is unavailable.
- The daughter regions re-open on the same RS.  Immediately when the StoreFiles are opened,
a compaction is triggered across all of their StoreFiles because they contain references.
 Since we cannot currently split a split, we need to not hang on to these references for long.

This described behavior is really bad because of how often we have to rewrite data onto HDFS.
 Imports are usually just IO bound as the RS waits to flush and compact.  In the above example,
the first cell to be inserted into this region ends up being written to HDFS 4 times (initial
flush, first compaction w/ no split decision, second compaction w/ split decision, third compaction
on daughter region).  In addition, we leave a large window where we take on edits (during
the second compaction of 320MB) and then must make the region unavailable as we flush it.


If we increased the compactionThreshold to be 5 and determined splits based on aggregate size,
the behavior becomes:
- We fill up regions, and as long as you are not under global RS heap pressure, you will write
out 64MB (hbase.hregion.memstore.flush.size) StoreFiles.
- After each MemStore flush, we calculate the aggregate size of all StoreFiles.  We can also
check the compactionThreshold.  For the first three flushes, both would not hit the limit.
 On the fourth flush, we would see total aggregate size = 256MB and determine to make a split.
- Decision to split is made, region is closed.  This time, the region just has to flush out
whichever edits made it to the MemStore during the snapshot/flush of the previous MemStore.
 So this time window has shrunk by more than 75% as it was the time to write 64MB from memory
not 320MB from aggregating 5 hdfs files.  This will greatly reduce the time data is unavailable
during splits.
- The daughter regions re-open on the same RS.  Immediately when the StoreFiles are opened,
a compaction is triggered across all of their StoreFiles because they contain references.
 This would stay the same.

In this example, we only write a given cell twice (instead of 4 times) while drastically reducing
data unavailability during splits.  On the original flush, and post-split to remove references.
 The other benefit of post-split compaction (which doesn't change) is that we then get good
data locality as the resulting StoreFile will be written to the local DataNode.  In another
jira, we should deal with opening up one of the daughter regions on a different RS to distribute
load better, but that's outside the scope of this one.

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