Thomas Steinmaurer created CASSANDRA-15400:
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Summary: Cassandra 3.0.18 went OOM several hours after joining a
cluster
Key: CASSANDRA-15400
URL: https://issues.apache.org/jira/browse/CASSANDRA-15400
Project: Cassandra
Issue Type: Bug
Reporter: Thomas Steinmaurer
Attachments: cassandra_hprof_bigtablereader_statsmetadata.png,
cassandra_hprof_dominator_classes.png, cassandra_jvm_metrics.png,
cassandra_operationcount.png, cassandra_sstables_pending_compactions.png
We have been moving from Cassandra 2.1.18 to Cassandra 3.0.18 and have been
facing an OOM two times with 3.0.18 on newly added nodes joining an existing
cluster after several hours being successfully bootstrapped.
Running in AWS:
* m5.2xlarge, EBS SSD (gp2)
* Xms/Xmx12G, Xmn3G, CMS GC
* 4 compaction threads, throttling set to 32 MB/s
What we see is a steady increase in the OLD gen over many hours.
!cassandra_jvm_metrics.png!
* The node started to join / auto-bootstrap the cluster on Oct 30 ~ 12:00
* It basically finished joining the cluster (UJ => UN) ~ 19hrs later on Oct 31
~ 07:00 also starting to be a member of serving client read requests
!cassandra_operationcount.png!
Memory-wise (on-heap) it didn't look that bad at that time, but old gen usage
constantly increased.
We see a correlation in increased number of SSTables and pending compactions.
!cassandra_sstables_pending_compactions.png!
Until we reached the OOM somewhere in Nov 1 in the night. After a Cassandra
startup (metric gap in the chart above), number of SSTables + pending
compactions is still high, but without facing memory troubles since then.
This correlation is confirmed by the auto-generated heap dump with e.g. ~ 5K
BigTableReader instances with ~ 8.7GByte retained hype in total.
!cassandra_hprof_dominator_classes.png!
Having a closer look on a single object instance, seems like each instance is ~
2MByte in size.
!cassandra_hprof_bigtablereader_statsmetadata.png!
With 2 pre-allocated byte buffers (highlighted in the screen above) at 1 MByte
each
We have been running with 2.1.18 for > 3 years and I can't remember dealing
with such OOM in the context of extending a cluster.
While the MAT screens above are from our production cluster, we partly can
reproduce this behavior in our loadtest environment (although not going full
OOM there), thus I might be able to share a hprof from this non-prod
environment if needed.
Thanks a lot.
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