# Performance impact for searching high frequency word **URL:** \ **Category:** Elasticsearch\ **Created:** [May 1, 2019, 7:02pm UTC](https://discuss.elastic.co/t/performance-impact-for-searching-high-frequency-word/179240 "2019-05-01T19:02:20Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![Devon\_Yoo](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/devon_yoo/32/45284_2.png) [@Devon\_Yoo](https://discuss.elastic.co/u/Devon_Yoo)\ **Post date:** [May 1, 2019, 7:02pm UTC](https://discuss.elastic.co/t/performance-impact-for-searching-high-frequency-word/179240/1 "2019-05-01T19:02:20Z")
Hi, I have a question which is fundamental regarding the basics of ES. I could find lots of how to use it and what the queries look like but I can hardly find a document about the backend structures. Let's say I indexed **10 million email bodies** associated to email\_id for each body. Every word in the bodies will be split and indexed into the ES in a certain way. And, **9 million emails** of them contain one or more of word " **ABC**" and **only 100 emails** of them contain one or more of word " **DEF**". And I want to query to search the top 10 emails that have the most number of the input word in desc order. In such case, the processing time between searching "ABC" and "DEF" will be different? If it's different, what should I do for better performance. If it's same, why is that? Thanks, ---
**Author:** ![polyfractal](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/polyfractal/32/48162_2.png) [@polyfractal](https://discuss.elastic.co/u/polyfractal)\ **Post date:** [May 6, 2019, 5:46pm UTC](https://discuss.elastic.co/t/performance-impact-for-searching-high-frequency-word/179240/2 "2019-05-06T17:46:15Z")
Generally, yes. The more "exclusive" a query is, the faster. It also depends a bit if you are scoring or just filtering. Elasticsearch uses an inverted index. This means a term dictionary is created, which maps the individual tokens to the documents that contain the tokens. So our term dictionary here is: ```auto Term Documents ----------------------------------- ABC | 2,3,4,5,6,7,8,9,10,11,12,13,14,15..... DEF | 1,128202 ``` So when we search for "DEF", we go to the "DEF" part of the term dictionary and extract the list of matching documents: `1` and `128202`. We then score those documents with the query. For "ABC", we do the same thing but the list is 10 million long, which means we have many more documents to score. It also depends on how exclusive the _entire_ query is, not just individual queries. Elasticsearch will try to execute the least expensive portions of the query first (e.g. the parts that exclude the largest amount of documents). So even if you are searching for "ABC", if another part of the query is exclusive (like a time filter) it will cut the list of matching docs way down. But at the end of the day, more docs matched == more docs that have to be loaded and scored and processed, so they will be slower than queries that only match a handful of documents. Hope that helps! You can read a bit more about the inverted index stuff here: [https://www.elastic.co/guide/en/elasticsearch/guide/current/inverted-index.html](https://www.elastic.co/guide/en/elasticsearch/guide/current/inverted-index.html) ---
**Author:** ![system](https://us1.discourse-cdn.com/elastic/original/3X/1/a/1ac57faf039f6b580b3f104ef42a2a89e41014de.png) [@system](https://discuss.elastic.co/u/system)\ **Post date:** [June 3, 2019, 5:46pm UTC](https://discuss.elastic.co/t/performance-impact-for-searching-high-frequency-word/179240/3 "2019-06-03T17:46:16Z")
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