We're using the Java API. I assume that it is using a binary representation 
of some kind that is more compact than JSON. I just mentioned JSON to 
illustrate the size of the response. I'll certainly try to monitor 
disk/network activity.

Nils-H


On Friday, May 30, 2014 7:16:38 AM UTC+2, Otis Gospodnetic wrote:
>
> Massive JSON responses could indeed be a problem.  I think you can easily 
> see if CPU, Disk, or Network are the bottleneck using really any monitoring 
> tool.  Even dstat --cpu --mem --disk --net will give you an idea. :)
>
> Otis
> --
> Performance Monitoring * Log Analytics * Search Analytics
> Solr & Elasticsearch Support * http://sematext.com/
>
>
> On Tuesday, May 27, 2014 6:44:45 AM UTC-4, [email protected] wrote:
>>
>> Thank you for your reply.
>>
>> Here are some observations from a couple of days testing:
>>
>> - Setting up routing manually reduced the aggregation time about 40%!
>> - ... however, manual routing caused data to distribute unevenly. I 
>> assume we could have taken steps to improve the distribution, but we didn't 
>> investigate any further
>> - Upgrading from 1.1.0 to 1.2.0 didn't seem to improve speed nor memory 
>> usage, although we didn't do any accurate measurements of RAM usage
>> - Changing the compression value for percentiles did indeed have an effect
>> - Increasing number of nodes from 3 to 5 didn't seem to improve 
>> performance
>>
>> Since adding additional nodes didn't seem to improve the performance, 
>> there seem to be a bottleneck somewhere. The result of the aggregation is 
>> very large (as a JSON-result, it results in about a million lines of text), 
>> so maybe data transfer or constructing the result can be a bottleneck?
>>
>

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