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36a3b84fc9
* Default include_type_name to false for get and put mappings. * Default include_type_name to false for get field mappings. * Add a constant for the default include_type_name value. * Default include_type_name to false for get and put index templates. * Default include_type_name to false for create index. * Update create index calls in REST documentation to use include_type_name=true. * Some minor clean-ups around the get index API. * In REST tests, use include_type_name=true by default for index creation. * Make sure to use 'expression == false'. * Clarify the different IndexTemplateMetaData toXContent methods. * Fix FullClusterRestartIT#testSnapshotRestore. * Fix the ml_anomalies_default_mappings test. * Fix GetFieldMappingsResponseTests and GetIndexTemplateResponseTests. We make sure to specify include_type_name=true during xContent parsing, so we continue to test the legacy typed responses. XContent generation for the typeless responses is currently only covered by REST tests, but we will be adding unit test coverage for these as we implement each typeless API in the Java HLRC. This commit also refactors GetMappingsResponse to follow the same appraoch as the other mappings-related responses, where we read include_type_name out of the xContent params, instead of creating a second toXContent method. This gives better consistency in the response parsing code. * Fix more REST tests. * Improve some wording in the create index documentation. * Add a note about types removal in the create index docs. * Fix SmokeTestMonitoringWithSecurityIT#testHTTPExporterWithSSL. * Make sure to mention include_type_name in the REST docs for affected APIs. * Make sure to use 'expression == false' in FullClusterRestartIT. * Mention include_type_name in the REST templates docs.
199 lines
6.6 KiB
Plaintext
199 lines
6.6 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?include_type_name=true
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{
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"mappings": {
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"_doc": {
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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?include_type_name=true
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{
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"mappings": {
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"_doc": {
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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?include_type_name=true
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{
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"mappings": {
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"_doc": {
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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?include_type_name=true
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{
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"mappings": {
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"_doc": {
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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?include_type_name=true
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{
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"mappings": {
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"_doc": {
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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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=== Watch your shard size
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Larger shards are going to be more efficient at storing data. To increase the size of your shards, you can decrease the number of primary shards in an index by <<indices-create-index,creating indices>> with less primary shards, creating less indices (e.g. by leveraging the <<indices-rollover-index,Rollover API>>), or modifying an existing index using the <<indices-shrink-index,Shrink API>>.
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Keep in mind that large shard sizes come with drawbacks, such as long full recovery times.
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[float]
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=== Disable `_source`
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The <<mapping-source-field,`_source`>> field stores the original JSON body of the document. If you don’t need access to it you can disable it. However, APIs that needs access to `_source` such as update and reindex won’t work.
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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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=== Force Merge
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Indices in Elasticsearch are stored in one or more shards. Each shard is a Lucene index and made up of one or more segments - the actual files on disk. Larger segments are more efficient for storing data.
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The <<indices-forcemerge,`_forcemerge` API>> can be used to reduce the number of segments per shard. In many cases, the number of segments can be reduced to one per shard by setting `max_num_segments=1`.
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[float]
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=== Shrink Index
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The <<indices-shrink-index,Shrink API>> allows you to reduce the number of shards in an index. Together with the Force Merge API above, this can significantly reduce the number of shards and segments of an index.
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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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[float]
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=== Use index sorting to colocate similar documents
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When Elasticsearch stores `_source`, it compresses multiple documents at once
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in order to improve the overall compression ratio. For instance it is very
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common that documents share the same field names, and quite common that they
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share some field values, especially on fields that have a low cardinality or
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a https://en.wikipedia.org/wiki/Zipf%27s_law[zipfian] distribution.
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By default documents are compressed together in the order that they are added
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to the index. If you enabled <<index-modules-index-sorting,index sorting>>
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then instead they are compressed in sorted order. Sorting documents with similar
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structure, fields, and values together should improve the compression ratio.
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[float]
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=== Put fields in the same order in documents
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Due to the fact that multiple documents are compressed together into blocks,
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it is more likely to find longer duplicate strings in those `_source` documents
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if fields always occur in the same order.
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