161 lines
4.3 KiB
Plaintext
161 lines
4.3 KiB
Plaintext
[[tune-for-disk-usage]]
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== Tune for disk usage
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[float]
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=== Disable the features you do not need
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By default elasticsearch indexes and adds doc values to most fields so that they
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can be searched and aggregated out of the box. For instance if you have a numeric
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field called `foo` that you need to run histograms on but that you never need to
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filter on, you can safely disable indexing on this field in your
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<<mappings,mappings>>:
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[source,js]
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--------------------------------------------------
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PUT index
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{
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"mappings": {
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"type": {
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"properties": {
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"foo": {
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"type": "integer",
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"index": false
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}
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}
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}
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}
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}
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--------------------------------------------------
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// CONSOLE
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<<text,`text`>> fields store normalization factors in the index in order to be
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able to score documents. If you only need matching capabilities on a `text`
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field but do not care about the produced scores, you can configure elasticsearch
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to not write norms to the index:
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[source,js]
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--------------------------------------------------
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PUT index
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{
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"mappings": {
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"type": {
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"properties": {
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"foo": {
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"type": "text",
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"norms": false
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}
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}
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}
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}
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}
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--------------------------------------------------
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// CONSOLE
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<<text,`text`>> fields also store frequencies and positions in the index by
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default. Frequencies are used to compute scores and positions are used to run
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phrase queries. If you do not need to run phrase queries, you can tell
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elasticsearch to not index positions:
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[source,js]
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--------------------------------------------------
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PUT index
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{
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"mappings": {
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"type": {
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"properties": {
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"foo": {
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"type": "text",
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"index_options": "freqs"
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}
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}
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}
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}
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}
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--------------------------------------------------
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// CONSOLE
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Furthermore if you do not care about scoring either, you can configure
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elasticsearch to just index matching documents for every term. You will
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still be able to search on this field, but phrase queries will raise errors
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and scoring will assume that terms appear only once in every document.
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[source,js]
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--------------------------------------------------
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PUT index
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{
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"mappings": {
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"type": {
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"properties": {
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"foo": {
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"type": "text",
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"norms": false,
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"index_options": "freqs"
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}
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}
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}
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}
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}
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--------------------------------------------------
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// CONSOLE
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[float]
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=== Don't use default dynamic string mappings
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The default <<dynamic-mapping,dynamic string mappings>> will index string fields
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both as <<text,`text`>> and <<keyword,`keyword`>>. This is wasteful if you only
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need one of them. Typically an `id` field will only need to be indexed as a
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`keyword` while a `body` field will only need to be indexed as a `text` field.
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This can be disabled by either configuring explicit mappings on string fields
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or setting up dynamic templates that will map string fields as either `text`
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or `keyword`.
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For instance, here is a template that can be used in order to only map string
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fields as `keyword`:
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[source,js]
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--------------------------------------------------
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PUT index
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{
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"mappings": {
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"type": {
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"dynamic_templates": [
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{
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"strings": {
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"match_mapping_type": "string",
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"mapping": {
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"type": "keyword"
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}
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}
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}
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]
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}
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}
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}
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--------------------------------------------------
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// CONSOLE
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[float]
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=== Disable `_all`
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The <<mapping-all-field,`_all`>> field indexes the value of all fields of a
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document and can use significant space. If you never need to search against all
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fields at the same time, it can be disabled.
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[float]
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=== Use `best_compression`
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The `_source` and stored fields can easily take a non negligible amount of disk
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space. They can be compressed more aggressively by using the `best_compression`
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<<index-codec,codec>>.
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[float]
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=== Use the smallest numeric type that is sufficient
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The type that you pick for <<number,numeric data>> can have a significant impact
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on disk usage. In particular, integers should be stored using an integer type
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(`byte`, `short`, `integer` or `long`) and floating points should either be
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stored in a `scaled_float` if appropriate or in the smallest type that fits the
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use-case: using `float` over `double`, or `half_float` over `float` will help
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save storage.
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