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From Chris Goffinet <>
Subject Re: Cassandra cluster runs into OOM when bulk loading data
Date Mon, 26 Apr 2010 19:32:25 GMT
Upgrade to b20 of Sun's version of JVM. This OOM might be related to
LinkedBlockQueue issues that were fixed.


2010/4/26 Roland Hänel <>

> Cassandra Version 0.6.1
> OpenJDK Server VM (build 14.0-b16, mixed mode)
> Import speed is about 10MB/s for the full cluster; if a compaction is going
> on the individual node is I/O limited
> tpstats: caught me, didn't know this. I will set up a test and try to catch
> a node during the critical time.
> Thanks,
> Roland
> 2010/4/26 Chris Goffinet <>
>  Which version of Cassandra?
>> Which version of Java JVM are you using?
>> What do your I/O stats look like when bulk importing?
>> When you run `nodeprobe -host XXXX tpstats` is any thread pool backing up
>> during the import?
>> -Chris
>> 2010/4/26 Roland Hänel <>
>> I have a cluster of 5 machines building a Cassandra datastore, and I load
>>> bulk data into this using the Java Thrift API. The first ~250GB runs fine,
>>> then, one of the nodes starts to throw OutOfMemory exceptions. I'm not using
>>> and row or index caches, and since I only have 5 CF's and some 2,5 GB of RAM
>>> allocated to the JVM (-Xmx2500M), in theory, that should happen. All inserts
>>> are done with consistency level ALL.
>>> I hope with this I have avoided all the 'usual dummy errors' that lead to
>>> OOM's. I have begun to troubleshoot the issue with JMX, however, it's
>>> difficult to catch the JVM in the right moment because it runs well for
>>> several hours before this thing happens.
>>> One thing gets to my mind, maybe one of the experts could confirm or
>>> reject this idea for me: is it possible that when one machine slows down a
>>> little bit (for example because a big compaction is going on), the memtables
>>> don't get flushed to disk as fast as they are building up under the
>>> continuing bulk import? That would result in a downward spiral, the system
>>> gets slower and slower on disk I/O, but since more and more data arrives
>>> over Thrift, finally OOM.
>>> I'm using the "periodic" commit log sync, maybe also this could create a
>>> situation where the commit log writer is too slow to catch up with the data
>>> intake, resulting in ever growing memory usage?
>>> Maybe these thoughts are just bullshit. Let me now if so... ;-)

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