For Elasticsearch, try m3.xlarge and set ES_HEAP_SIZE to 7 or 8GB. You may
also want to have more than one node in your cluster.
You might also want to split Logstash off onto a separate instance. It is
CPU intensive but not particularly RAM intensive. Set the -w {n} flag in
the startup script to allow Logstash to run multiple threads across
multiple cores. You might start with a m3.large for this and use -w 2 and
see how it goes.
On Wednesday, August 13, 2014 9:38:10 AM UTC-6, AK wrote:
>
> Hi,
>
> I recently launched ELK and I'm receiving about 3,000,000 - 8,000,000 docs
> per day (~ 5GB)
> I'm running on AWS on a small server, and after a week of data collection
> the system becomes very very slow, mainly when I am looking for data older
> than 2 days.
> Do you have a recommendation for servers in points such as cpu, memory and
> iops and elstic settings like shards.
>
> Thanks
> AK
>
>
>
>
>
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