109 lines
4.6 KiB
Plaintext
109 lines
4.6 KiB
Plaintext
[[eager-global-ordinals]]
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=== `eager_global_ordinals`
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==== What are global ordinals?
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To support aggregations and other operations that require looking up field
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values on a per-document basis, Elasticsearch uses a data structure called
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<<doc-values, doc values>>. Term-based field types such as `keyword` store
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their doc values using an ordinal mapping for a more compact representation.
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This mapping works by assigning each term an incremental integer or 'ordinal'
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based on its lexicographic order. The field's doc values store only the
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ordinals for each document instead of the original terms, with a separate
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lookup structure to convert between ordinals and terms.
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When used during aggregations, ordinals can greatly improve performance. As an
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example, the `terms` aggregation relies only on ordinals to collect documents
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into buckets at the shard-level, then converts the ordinals back to their
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original term values when combining results across shards.
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Each index segment defines its own ordinal mapping, but aggregations collect
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data across an entire shard. So to be able to use ordinals for shard-level
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operations like aggregations, Elasticsearch creates a unified mapping called
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'global ordinals'. The global ordinal mapping is built on top of segment
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ordinals, and works by maintaining a map from global ordinal to the local
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ordinal for each segment.
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Global ordinals are used if a search contains any of the following components:
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* Certain bucket aggregations on `keyword`, `ip`, and `flattened` fields. This
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includes `terms` aggregations as mentioned above, as well as `composite`,
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`diversified_sampler`, and `significant_terms`.
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* Bucket aggregations on `text` fields that require <<fielddata, `fielddata`>>
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to be enabled.
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* Operations on parent and child documents from a `join` field, including
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`has_child` queries and `parent` aggregations.
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NOTE: The global ordinal mapping uses heap memory as part of the
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<<modules-fielddata, field data cache>>. Aggregations on high cardinality fields
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can use a lot of memory and trigger the <<fielddata-circuit-breaker, field data
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circuit breaker>>.
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==== Loading global ordinals
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The global ordinal mapping must be built before ordinals can be used during a
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search. By default, the mapping is loaded during search on the first time that
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global ordinals are needed. This is the right approach if you are optimizing
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for indexing speed, but if search performance is a priority, it's recommended
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to eagerly load global ordinals on fields that will be used in aggregations:
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[source,console]
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------------
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PUT my-index-000001/_mapping
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{
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"properties": {
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"tags": {
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"type": "keyword",
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"eager_global_ordinals": true
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}
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}
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}
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------------
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// TEST[s/^/PUT my-index-000001\n/]
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When `eager_global_ordinals` is enabled, global ordinals are built when a shard
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is <<indices-refresh, refreshed>> -- Elasticsearch always loads them before
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exposing changes to the content of the index. This shifts the cost of building
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global ordinals from search to index-time. Elasticsearch will also eagerly
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build global ordinals when creating a new copy of a shard, as can occur when
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increasing the number of replicas or relocating a shard onto a new node.
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Eager loading can be disabled at any time by updating the `eager_global_ordinals` setting:
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[source,console]
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------------
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PUT my-index-000001/_mapping
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{
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"properties": {
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"tags": {
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"type": "keyword",
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"eager_global_ordinals": false
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}
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}
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}
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------------
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// TEST[continued]
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==== Avoiding global ordinal loading
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Usually, global ordinals do not present a large overhead in terms of their
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loading time and memory usage. However, loading global ordinals can be
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expensive on indices with large shards, or if the fields contain a large
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number of unique term values. Because global ordinals provide a unified mapping
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for all segments on the shard, they also need to be rebuilt entirely when a new
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segment becomes visible.
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In some cases it is possible to avoid global ordinal loading altogether:
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* The `terms`, `sampler`, and `significant_terms` aggregations support a
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parameter
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<<search-aggregations-bucket-terms-aggregation-execution-hint, `execution_hint`>>
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that helps control how buckets are collected. It defaults to `global_ordinals`,
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but can be set to `map` to instead use the term values directly.
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* If a shard has been <<indices-forcemerge,force-merged>> down to a single
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segment, then its segment ordinals are already 'global' to the shard. In this
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case, Elasticsearch does not need to build a global ordinal mapping and there
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is no additional overhead from using global ordinals. Note that for performance
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reasons you should only force-merge an index to which you will never write to
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again.
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