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eager_global_ordinals
editeager_global_ordinals
editWhat are global ordinals?
editTo support aggregations and other operations that require looking up field
values on a per-document basis, Elasticsearch uses a data structure called
doc values. Term-based field types such as keyword
store
their doc values using an ordinal mapping for a more compact representation.
This mapping works by assigning each term an incremental integer or ordinal
based on its lexicographic order. The field’s doc values store only the
ordinals for each document instead of the original terms, with a separate
lookup structure to convert between ordinals and terms.
When used during aggregations, ordinals can greatly improve performance. As an
example, the terms
aggregation relies only on ordinals to collect documents
into buckets at the shard-level, then converts the ordinals back to their
original term values when combining results across shards.
Each index segment defines its own ordinal mapping, but aggregations collect data across an entire shard. So to be able to use ordinals for shard-level operations like aggregations, Elasticsearch creates a unified mapping called global ordinals. The global ordinal mapping is built on top of segment ordinals, and works by maintaining a map from global ordinal to the local ordinal for each segment.
Global ordinals are used if a search contains any of the following components:
-
Certain bucket aggregations on
keyword
,ip
, andflattened
fields. This includesterms
aggregations as mentioned above, as well ascomposite
,diversified_sampler
, andsignificant_terms
. -
Bucket aggregations on
text
fields that requirefielddata
to be enabled. -
Operations on parent and child documents from a
join
field, includinghas_child
queries andparent
aggregations.
The global ordinal mapping is an on-heap data structure. When measuring
memory usage, Elasticsearch counts the memory from global ordinals as
fielddata. Global ordinals memory is included in the
fielddata circuit breaker, and is returned
under fielddata
in the node stats response.
Loading global ordinals
editThe global ordinal mapping must be built before ordinals can be used during a search. By default, the mapping is loaded during search on the first time that global ordinals are needed. This is is the right approach if you are optimizing for indexing speed, but if search performance is a priority, it’s recommended to eagerly load global ordinals eagerly on fields that will be used in aggregations:
PUT my_index/_mapping/_doc { "properties": { "tags": { "type": "keyword", "eager_global_ordinals": true } } }
When eager_global_ordinals
is enabled, global ordinals are built when a shard
is refreshed — Elasticsearch always loads them before
exposing changes to the content of the index. This shifts the cost of building
global ordinals from search to index-time. Elasticsearch will also eagerly
build global ordinals when creating a new copy of a shard, as can occur when
increasing the number of replicas or relocating a shard onto a new node.
Eager loading can be disabled at any time by updating the eager_global_ordinals
setting:
PUT my_index/_mapping/_doc { "properties": { "tags": { "type": "keyword", "eager_global_ordinals": false } } }
On a frozen index, global ordinals are discarded
after each search and rebuilt again when they’re requested. This means that
eager_global_ordinals
should not be used on frozen indices: it would
cause global ordinals to be reloaded on every search. Instead, the index should
be force-merged to a single segment before being frozen. This avoids building
global ordinals altogether (more details can be found in the next section).
Avoiding global ordinal loading
editUsually, global ordinals do not present a large overhead in terms of their loading time and memory usage. However, loading global ordinals can be expensive on indices with large shards, or if the fields contain a large number of unique term values. Because global ordinals provide a unified mapping for all segments on the shard, they also need to be rebuilt entirely when a new segment becomes visible.
In some cases it is possible to avoid global ordinal loading altogether:
-
The
terms
,sampler
, andsignificant_terms
aggregations support a parameterexecution_hint
that helps control how buckets are collected. It defaults toglobal_ordinals
, but can be set tomap
to instead use the term values directly. - If a shard has been force-merged down to a single segment, then its segment ordinals are already global to the shard. In this case, Elasticsearch does not need to build a global ordinal mapping and there is no additional overhead from using global ordinals. Note that for performance reasons you should only force-merge an index to which you will never write to again.