So you use Kibana? Then probably behind the scene you use facets (histogram panel, terms panel…)
Facets use a lot of memory.
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David Pilato | Technical Advocate | Elasticsearch.com
@dadoonet | @elasticsearchfr | @scrutmydocs
Le 24 oct. 2013 à 17:16, subin ks ksubins321@gmail.com a écrit :
David, this is the template that I use - @fields separated, date analyzed and 2 time-formats · GitHub. There is no faceting to transform my data. I use Kibana to search the logs and sorting, is done on @timestamp field. Now, could there by an clues or pointers, still? Because I see optimal usage when no searches are being done but the usage of Heap memory shoots up when searches are done and the events start getting queued up on REDIS. In normal cases, when REDIS's queues are cleared off the memory usage is like 4-5Mb. When searches are done on ES(from Kibana) it grows to 30Mb+.
Subin
On Tue, Oct 22, 2013 at 12:09 PM, David Pilato david@pilato.fr wrote:
It depends! 
Seriously, facets are memory intensive for example. Sorting as well.
So the answer is always: test it with your queries.
(I know: it does not really help 
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David 
Twitter : @dadoonet / @elasticsearchfr / @scrutmydocs
Le 22 oct. 2013 à 08:30, subin ksubins321@gmail.com a écrit :
Hello,
I've been pondering over this for some time. Obviously RAM will depend on the total amount of indexes to span over. But what else are the other factors to consider? Assuming that at any time, ES has to search through 30 days of indexes, amounting to about 250G, how could I take up calculations from here? let's say a single node with 1 shard/0 replica?
Thanks,
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