Otis, Thanks for the reply.

On Tuesday, 23 September 2014 21:47:42 UTC+5:30, Otis Gospodnetic wrote:
>
> Hi,
>
> We aggregate outside of ES, in memory, and push in bulk.  We could still 
> roll up the data stored in ES later on if we wanted to, but reading from ES 
> could get expensive. 
>
> Otis
> --
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>
>
> On Monday, September 22, 2014 11:08:09 PM UTC-4, Srinath C wrote:
>>
>> Hi,
>>     We are currently utilizing ES for analyzing the last 24 hours of 
>> data. The arrival rate of data is of the order of a few hundreds per 10 
>> second interval with each document having a timestamp associated with it.
>>     We now need to be able to analyze data over a week and to reduce the 
>> amount of space required we plan to retain the 24 hr TTL on the documents 
>> but aggregate data into one document per minute to retrieve data that is 
>> dated beyond 24 hours and upto 7 days. All fields in the document need to 
>> be aggregated.
>>
>>     So,
>> 1. Is there any out of the box features that I can use to achieve this 
>> kind of roll ups?
>> 2. What is the best approach (preferably a time-tested one if someone has 
>> already done this)?
>>
>>     Some approaches we were contemplating:
>> 1. Aggregating the data in real time (outside ES) and store the 
>> aggregated data into ES
>> 2. Periodically (say once in 30 mins) run aggregation queries and write 
>> back responses to ES
>> 3. Periodically (say once in 30 mins) read new documents using time 
>> range, aggregate and store back aggregated data in bulk into ES. Maybe use 
>> streaming or paged read of documents to aggregate them....
>> 4. Maybe use a combination of 1 and (2 or 3) so that real time data gets 
>> aggregated and data that is delayed (may happen) due to some reason can be 
>> updated into the aggregated data using the Update API of ES?
>>
>> Thanks for all the advices,
>> Srinath.
>>
>>
>>

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