OOME in master splitting logs
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Key: HBASE-3323
URL: https://issues.apache.org/jira/browse/HBASE-3323
Project: HBase
Issue Type: Bug
Components: master
Affects Versions: 0.90.0
Reporter: Todd Lipcon
Priority: Blocker
Fix For: 0.90.0
In testing a RS failure under heavy increment workload I ran into an OOME when
the master was splitting the logs.
In this test case, I have exactly 136 bytes per log entry in all the logs, and
the logs are all around 66-74MB). With a batch size of 3 logs, this means the
master is loading about 500K-600K edits per log file. Each edit ends up
creating 3 byte[] objects, the references for which are each 8 bytes of RAM, so
we have 160 (136+8*3) bytes per edit used by the byte[]. For each edit we also
allocate a bunch of other objects: one HLog$Entry, one WALEdit, one ArrayList,
one LinkedList$Entry, one HLogKey, and one KeyValue. Overall this works out to
400 bytes of overhead per edit. So, with the default settings on this fairly
average workload, the 1.5M log entries takes about 770MB of RAM. Since I had a
few log files that were a bit larger (around 90MB) it exceeded 1GB of RAM and I
got an OOME.
For one, the 400 bytes per edit overhead is pretty bad, and we could probably
be a lot more efficient. For two, we should actually account this rather than
simply having a configurable "batch size" in the master.
I think this is a blocker because I'm running with fairly default configs here
and just killing one RS made the cluster fall over due to master OOME.
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