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 > > > > > -- You received this message because you are subscribed to the Google Groups "elasticsearch" group. To unsubscribe from this group and stop receiving emails from it, send an email to elasticsearch+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/elasticsearch/90398be0-4804-44d7-9f8e-e033daa7050b%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.