A lot of the answers for performance and capacity are "it depends".
You'll get *much* better performance from Oracle java, 1.7u55 is current
recommended. Given you're "experimenting" with GCG1 (which isn't current
best practise, hence experimenting), you might even want to try Java 1.8.

If you want to drop memory use you can disable bloom filtering.

Regards,
Mark Walkom

Infrastructure Engineer
Campaign Monitor
email: ma...@campaignmonitor.com
web: www.campaignmonitor.com


On 31 July 2014 19:16, Tim Stoop <tim.st...@gmail.com> wrote:

> Hi all,
>
> We've been running an ElasticSearch cluster of three nodes since last
> December. We're running them on Debian Wheezy. Due to the size of our
> network, we're getting about 600 messages/s (800 at peak times). Using
> logstash and daily indices, we're currently at about 3TB of data spread
> over 158 indices. We use 2 replicas, so all machines have all data.
>
> I'm struggling to understand what expected working limits are for
> ElasticSearch in a centralized log situation and would really appreciate
> input from others. What we eventually try to do is provide about 13 months
> of searchable logs via Kibana, but we're mostly running into RAM
> constraints. Each node has 3TB of data, but the heap usage is almost
> constantly up at 27-29GB.
>
> We did have a problem with garbage collects earlier, which took so long
> that a node would drop from the cluster. To fix this, we switched to the G1
> gc, which seems very well suited for this operation. The machines we're
> using have 12 cores, so the added CPU overhead is negligible (general usage
> of the cores is less than 30%). But I'm not enough of a Java dev to judge
> if this switch could be the cause of the constant high heap usage. We're
> currently at about 1GB RAM per 100GB of disk usage.
>
> My questions:
>
> 1- Is 1GB RAM usage per 100GB disk usage an expected usage pattern for an
> index heavy cluster?
> 2- Aside from closing indices, are there other ways of lowering this?
> 2.5- Should I worry about it?
> 3- Are we approaching this the wrong way and should we change our setup?
> 4- Would upgrading to 1.3.1 change the usage significantly, due to the fix
> in issue 6856 or is it unrelated?
>
> The numbers:
>
> 3 nodes with the following config:
> - 92GB of RAM
> - 12 cores with HT
> - 6x 3.6TB spinning disks (we're contemplating adding CacheCade, since the
> controller supports it)
>   - We expose the disks to elasticsearch via 6 mount points
> - Debian Wheezy
> - Java 1.7.0_65 OpenJDK JRE with IcedTea 2.5.1 (Debian package with
> version 7u65-2.5.1-2~deb7u1)
> - ElasticSearch 1.2.1 from the ElasticSearch repositories
>
> Config snippets (leaving out ips and the like):
>
> bootstrap:
>  mlockall: true
> cluster:
>  name: logging
>  routing:
>   allocation:
>     node_concurrent_recoveries: 4
> discovery:
>  zen:
>   minimum_master_nodes: 2
>   ping:
>    unicast:
>     hosts:
> [snip]
> index:
>  number_of_replicas: 2
>  number_of_shards: 6
> indices:
>  memory:
>   index_buffer_size: 50%
>  recovery:
>   concurrent_streams: 5
>   max_bytes_per_sec: 100mb
> node:
>  concurrent_recoveries: 6
>  name: stor1-stor
> path:
>  data:
>    - /srv/elasticsearch/a
>    - /srv/elasticsearch/b
>    - /srv/elasticsearch/c
>    - /srv/elasticsearch/d
>    - /srv/elasticsearch/f
>    - /srv/elasticsearch/e
>
> Java options: -Xms30g -Xmx30g -Xss256k -Djava.awt.headless=true
> -XX:+UseG1GC -XX:+HeapDumpOnOutOfMemoryError
>
> Thanks for your help! Please let me know if you need more information
>
> --
> Kind regards,
> Tim Stoop
>
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