Hi Ajay,

As Nick pointed out, this query is going to match documents on a doc-by-doc
basis, which is going to be very slow.

However, each iteration is not supposed to increase memory usage. Memory
usage might jump on the first request because elasticsearch will need to
load the `date_value` field into field data and potentially your term
filter into the filter cache, but that should be it, and subsequent
executions of this request should not add 2GB of garbage.

Something that is uncommonly high in your query is the `size` parameter. Do
you have an idea of how large your documents are? It could be that part of
the reason why so much garbage is generated is due to the building and then
serialization of the search response.


On Fri, Nov 21, 2014 at 1:07 PM, Ajay Divakaran <[email protected]>
wrote:

> The term filter that is used:
>
> curl -XGET 'http://localhost:9200/my-index/my-doc-type/_search' -d '{
>     "filter": {
>        "term": {"void": false}
>     },
>     "fields": [["user_id1", "user_name", "date", "status", "q1",
>           "q1_unique_code", "q2", "q3"]],
>      "size": 50000, "sort": ["date_value"]}'
>
>
>    - The 'void' field is a boolean field.
>    - The index store size is 504mb.
>    - The elastic search setup consists of only a single node and the
>    index consists of only a single shard and 0 replicas. The version of
>    elasticsearch is 0.90.7
>    - The fields mentioned above is only the first 8 fields. The actual
>    term filter that we execute has 350 fields mentioned.
>
> *We noticed the memory spiking by about 2-3gb though the store size is
> only 504mb.*
>
> *Running the query multiple times seems to continuously increase the
> memory.*
>
> Could someone explain why this memory spike occurs?
>
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-- 
Adrien Grand

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