# Investigating query slowness

**URL:** <https://discuss.elastic.co/t/investigating-query-slowness/127366>\
**Category:** Elasticsearch\
**Created:** [April 9, 2018, 6:25pm UTC](https://discuss.elastic.co/t/investigating-query-slowness/127366 "2018-04-09T18:25:19Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![dimalini](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/dimalini/32/22448_2.png) [@dimalini](https://discuss.elastic.co/u/dimalini)\
**Post date:** [April 9, 2018, 6:25pm UTC](https://discuss.elastic.co/t/investigating-query-slowness/127366/1 "2018-04-09T18:25:19Z")

</div>

We have 2 main fields[F1, F2] in our mapping, which holds the same tokens.  
For instance -  
F1- Every token in F1 holds some payload information in addition to the terms and its offsets.  
F2 - This has mostly the same tokens (minus payload info)with a minor diff of some additional tokens present only in F2. And for these additional tokens the list of documents in which it appears is significantly high.

I am investigating some query slowness for the above configuration especially for phrase queries. I used **profile API** to collect some additional data.

Here are my observations and Queries:- --All in context of phrase queries--  
**(1)** In case of phrase queries against F2, the time to taken to do "matches", evaluate "next\_doc" is significantly higher. My suspicion is : the additional tokens with significantly high doc hits is contributing to the time taken. Is that right ?  
To confirm the same, is there a way we can see the size of inverted indices or any tool that could be used to see the term - doc mapping of the inverted index ? Or any way that is recommended to understand the inverted index info present in every segment?

**(1b)** In follow-up to (1) As per the profile API documentation "matches" is a two step process. How to know the overhead from the tokens with max doc hits contribute to this ?

**(2)** Queries if and when executed alternately against the fields [F1 or F2], is slow, I believe a cache miss causes eviction and adds to the time than when query is fired against the same field.  
Is there any way (tool/plugin/APIs) to track and understand such cache hits/misses, the memory usage pattern loading such different fields in memory ?

Thanks,  
Divya

---

<div class="post-metadata">

**Author:** ![system](https://us1.discourse-cdn.com/elastic/original/3X/1/a/1ac57faf039f6b580b3f104ef42a2a89e41014de.png) [@system](https://discuss.elastic.co/u/system)\
**Post date:** [May 7, 2018, 6:25pm UTC](https://discuss.elastic.co/t/investigating-query-slowness/127366/2 "2018-05-07T18:25:30Z")

</div>

This topic was automatically closed 28 days after the last reply. New replies are no longer allowed.