# Elasticsearch disk occupancy

**URL:** <https://discuss.elastic.co/t/elasticsearch-disk-occupancy/126194>\
**Category:** Elasticsearch\
**Created:** [March 30, 2018, 6:45am UTC](https://discuss.elastic.co/t/elasticsearch-disk-occupancy/126194 "2018-03-30T06:45:51Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![sebastienf](https://avatars.discourse-cdn.com/v4/letter/s/f07891/32.png) [@sebastienf](https://discuss.elastic.co/u/sebastienf)\
**Post date:** [March 30, 2018, 6:45am UTC](https://discuss.elastic.co/t/elasticsearch-disk-occupancy/126194/1 "2018-03-30T06:45:51Z")

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Hi,

I am kind of new in the ES word, i work with ES V5 and I have some questions...  
Thanks in advance to lightening me 🙂

I need to know the total real occupancy of my datas through my ES cluster.  
To do that, I use those commands : \_cat/shard and \_cat/indices

A very simple and short document (only one field, just a letter as a value...) takes 7kb while this document weighs barely 500b...  
I am half surprised : in a way, I understand that this document will be stored and/or indexed and then, it cost more than the original payload. But, normally, with compression, I was expecting a little bit more efficient...

-So, first question : is it "normal" to observe that thing ?

-Is the use of \_cat/shards and \_cat/indices give the true size (uncompressed) of datas or the compressed size of datas ?

-If a "Double" value costs 64bits (8octets), how many cost a "Text" (String) value ?

-Will a property name in a document ("property\_name":"value") will cost like a "Text" value ?

Again, thanks a lot 🙂

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**Author:** ![Christian\_Dahlqvist](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/christian_dahlqvist/32/4617_2.png) [@Christian\_Dahlqvist](https://discuss.elastic.co/u/Christian_Dahlqvist)\
**Post date:** [March 30, 2018, 6:57am UTC](https://discuss.elastic.co/t/elasticsearch-disk-occupancy/126194/2 "2018-03-30T06:57:31Z")

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To determine how large space your data will take up on disk, you need to index a good amount of data, ideally at least a few tens of GB. Indexing a very small number of documents does not allow compression to be very efficient, so will not allow you to draw any conclusions. You can then reduce the amount of space your data takes up by optimising the mappings you use.

Have a look at the following resources:

[https://www.elastic.co/elasticon/conf/2016/sf/quantitative-cluster-sizing](https://www.elastic.co/elasticon/conf/2016/sf/quantitative-cluster-sizing)

> **[Filebeat modules, access logs and Elasticsearch storage requirements
	  	 |...](https://www.elastic.co/blog/filebeat-modiles-access-logs-and-elasticsearch-storage-requirements)**
>
> Elastic recently introduced Filebeat Modules, which are designed to make it extremely easy to ingest and gain insights from common log formats. These follow the principle that

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**Author:** ![system](https://us1.discourse-cdn.com/elastic/original/3X/1/a/1ac57faf039f6b580b3f104ef42a2a89e41014de.png) [@system](https://discuss.elastic.co/u/system)\
**Post date:** [April 27, 2018, 6:57am UTC](https://discuss.elastic.co/t/elasticsearch-disk-occupancy/126194/3 "2018-04-27T06:57:34Z")

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