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https://issues.apache.org/jira/browse/CASSANDRA-10937?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15105495#comment-15105495
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Jack Krupansky edited comment on CASSANDRA-10937 at 1/18/16 5:16 PM:
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I still don't see any reason to believe that there is a bug here and that the
primary issue is that you are overloading the cluster. Sure, Cassandra should
do a better job of shedding/failing excessive incoming requests, and there is
an open Jira ticket to add just such a freature, but even with that new
feature, the net effect will be the same - it will still be up to the
application and operations to properly size the cluster and throttle
application load before it gets to Cassandra.
OOM is not typically an indication of a software bug. Sure, sometimes code has
memory leaks, but with a highly dynamic system such as Cassandra, it typically
means either a misconfigured JVM or just very heavy load. Sometimes OOM simply
means that there is a lot of background processing going on (like compactions
or hinted handoff) that is having trouble keeping up with incoming requests.
Sometimes OOM occurs because you have too large a heap which defers GC but then
GC takes too long and further incoming requests simply generate more pressure
on the heap faster than that massive GC can deal with it.
It is indeed tricky to make sure the JVM has enough heap but not too much. DSE
typically runs with a larger heap by default. You can try increasing your heap
to 10 or 12G. But if you make the heap too big, the big GC can bite you as
described above. In that case, the heap needs to be reduced. Typically you
don't need a heap smaller than 8 GB. If OOM occurs with a 8 GB heap it
typically means the load on that node is simply too heavy.
Be sure to review the recommendations in this blog post on reasonable
recommendations:
http://www.datastax.com/dev/blog/how-not-to-benchmark-cassandra
A few questions that will help us better understand what you are really trying
to do:
1. How much reading are you doing and when relative to writes?
2. Are you doing any updates or deletes? (Cause compaction, which can fall
behind your write/update load.)
3. How much data is on the cluster (rows)?
4. How many tables?
5. What RF? RF=3 would be the recommendation, but if you have a heavy read load
you may need RF=5, although heavy load usually means you just need a lot more
nodes so that the fraction of incoming requests going to a particular node are
dramatically reduced. RF>3 is only needed if there is high load for each
particular row or partition.
6. Have you tested using cassandra-stress? That's the gold standard around here.
7. Are your clients using token-aware routing? (Otherwise a write must be
bounced from the coordinating node to the node owning the token for the
partition key.)
8. Are you using batches for your writes? If, so, do all the writes in one
batch have the same partition key? (If not, adds more network hops.)
9. What expectations did you have as to how many writes/reads a given number of
nodes should be able to handle?
was (Author: jkrupan):
I still don't see any reason to believe that there is a bug here and that the
primary issue is that you are overloading the cluster. Sure, Cassandra should
do a better job of shedding/failing excessive incoming requests, and there is
an open Jira ticket to add just such a freature, but even with that new
feature, the net effect will be the same - it will still be up to the
application and operations to properly size the cluster and throttle
application load before it gets to Cassandra.
OOM is not typically an indication of a software bug. Sure, sometimes code has
memory leaks, but with a highly dynamic system such as Cassandra, it typically
means either a misconfigured JVM or just very heavy load. Sometimes OOM simply
means that there is a lot of background processing going on (like compactions
or hinted handoff) that is having trouble keeping up with incoming requests.
Sometimes OOM occurs because you have too large a heap which defers GC but then
GC takes too long and further incoming requests simply generate more pressure
on the heap faster than that massive GC can deal with it.
It is indeed tricky to make sure the JVM has enough heap but not too much. DSE
typically runs with a larger heap by default. You can try increasing your heap
to 10 or 12G. But if you make the heap too big, the big GC can bite you as
described above. In that case, the heap needs to be reduced. Typically you
don't need a heap smaller than 8 GB. If OOM occurs with a 8 GB heap it
typically means the load on that node is simply too heavy.
Be sure to review the recommendations in this blog post on reasonable
recommendations:
http://www.datastax.com/dev/blog/how-not-to-benchmark-cassandra
A few questions that will help us better understand what you are really trying
to do:
1. How much reading are you doing and when relative to writes?
2. Are you doing any updates or deletes? (Cause compaction, which can fall
behind your write/update load.)
3. How much data is on the cluster (rows)?
4. How many tables?
5. What RF? RF=3 would be the recommendation, but if you have a heavy read load
you may need RF=5.
6. Have you tested using cassandra-stress? That's the gold standard around here.
7. Are your clients using token-aware routing? (Otherwise a write must be
bounced from the coordinating node to the node owning the token for the
partition key.)
8. Are you using batches for your writes? If, so, do all the writes in one
batch have the same partition key? (If not, adds more network hops.)
> OOM on multiple nodes on write load (v. 3.0.0), problem also present on
> DSE-4.8.3, but there it survives more time
> ------------------------------------------------------------------------------------------------------------------
>
> Key: CASSANDRA-10937
> URL: https://issues.apache.org/jira/browse/CASSANDRA-10937
> Project: Cassandra
> Issue Type: Bug
> Environment: Cassandra : 3.0.0
> Installed as open archive, no connection to any OS specific installer.
> Java:
> Java(TM) SE Runtime Environment (build 1.8.0_65-b17)
> OS :
> Linux version 2.6.32-431.el6.x86_64
> ([email protected]) (gcc version 4.4.7 20120313 (Red
> Hat 4.4.7-4) (GCC) ) #1 SMP Sun Nov 10 22:19:54 EST 2013
> We have:
> 8 guests ( Linux OS as above) on 2 (VMWare managed) physical hosts. Each
> physical host keeps 4 guests.
> Physical host parameters(shared by all 4 guests):
> Model: HP ProLiant DL380 Gen9
> Intel(R) Xeon(R) CPU E5-2690 v3 @ 2.60GHz
> 46 logical processors.
> Hyperthreading - enabled
> Each guest assigned to have:
> 1 disk 300 Gb for seq. log (NOT SSD)
> 1 disk 4T for data (NOT SSD)
> 11 CPU cores
> Disks are local, not shared.
> Memory on each host - 24 Gb total.
> 8 (or 6, tested both) Gb - cassandra heap
> (lshw and cpuinfo attached in file test2.rar)
> Reporter: Peter Kovgan
> Priority: Critical
> Attachments: gc-stat.txt, more-logs.rar, some-heap-stats.rar,
> test2.rar, test3.rar, test4.rar, test5.rar, test_2.1.rar,
> test_2.1_logs_older.rar, test_2.1_restart_attempt_log.rar
>
>
> 8 cassandra nodes.
> Load test started with 4 clients(different and not equal machines), each
> running 1000 threads.
> Each thread assigned in round-robin way to run one of 4 different inserts.
> Consistency->ONE.
> I attach the full CQL schema of tables and the query of insert.
> Replication factor - 2:
> create keyspace OBLREPOSITORY_NY with replication =
> {'class':'NetworkTopologyStrategy','NY':2};
> Initiall throughput is:
> 215.000 inserts /sec
> or
> 54Mb/sec, considering single insert size a bit larger than 256byte.
> Data:
> all fields(5-6) are short strings, except one is BLOB of 256 bytes.
> After about a 2-3 hours of work, I was forced to increase timeout from 2000
> to 5000ms, for some requests failed for short timeout.
> Later on(after aprox. 12 hous of work) OOM happens on multiple nodes.
> (all failed nodes logs attached)
> I attach also java load client and instructions how set-up and use
> it.(test2.rar)
> Update:
> Later on test repeated with lesser load (100000 mes/sec) with more relaxed
> CPU (idle 25%), with only 2 test clients, but anyway test failed.
> Update:
> DSE-4.8.3 also failed on OOM (3 nodes from 8), but here it survived 48 hours,
> not 10-12.
> Attachments:
> test2.rar -contains most of material
> more-logs.rar - contains additional nodes logs
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