I use hazelcast on same jvm and run map/reduce in memory.It works really
well.For about 100000 blog datas and word count,es request with hz
map/reduce finish in less than 3 seconds.

On Tue, Feb 3, 2015 at 8:30 PM, chengtao cheng <chengtaot...@gmail.com>
wrote:

> I met the same problem with you !
>
> 在 2014年10月23日星期四 UTC+8上午9:17:18,Hajime Takase写道:
>
>> Hi,
>>
>> I have like billion records on 20 nodes and would like to run custom
>> map/reduce or "aggregation" (word count,sentiment analysis,etc) immediately
>> after the ES result set is determined.
>>
>> I came up with using Plugin system to customize "aggregation" like this:
>> https://github.com/algolia/elasticsearch-cardinality-
>> plugin/tree/1.0.X/src/main/java/org/alg/elasticsearch/
>> search/aggregations/cardinality
>>
>> but want to update the jar quite often which will eventually require ES
>> to be reload,I look up the scripted map/ reduce
>> http://www.elasticsearch.org/guide/en/elasticsearch/reference/1.4/search-
>> aggregations-metrics-scripted-metric-aggregation.html
>>
>> but was not sure about the memory usage or customization,I decide to run
>> hazelcast or Spark on the same node or jvm and use their map/reduce
>> framework.I use Filter phase to put the ES data like this:
>> https://github.com/medcl/elasticsearch-filter-redis/
>> blob/master/src/main/java/org/elasticsearch/index/query/
>> RedisFilterParser.java#L121
>>
>> but it just takes quite long time to put data on those in-memory
>> middleware...
>>
>> Is there any best practice to put ES data to in-memory middleware, just
>> to re-use the same data efficiently in subsequent program?
>> I don't think I can use the ES query result set (on each shard) which
>> seems to be on memory ,in my program,am I right?
>>
>> Thanks,
>>
>> Haji
>>
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