OpenSearch/docs/reference/glossary.asciidoc

[glossary]
[[glossary]]
= Glossary of terms

[glossary]
[[glossary-analysis]] analysis ::

Analysis is the process of converting <<glossary-text,full text>> to
<<glossary-term,terms>>. Depending on which analyzer is used, these phrases:
`FOO BAR`, `Foo-Bar`, `foo,bar` will probably all result in the
terms `foo` and `bar`. These terms are what is actually stored in
the index.
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A full text query (not a <<glossary-term,term>> query) for `FoO:bAR` will
also be analyzed to the terms `foo`,`bar` and will thus match the
terms stored in the index.
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It is this process of analysis (both at index time and at search time)
that allows Elasticsearch to perform full text queries.
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Also see <<glossary-text,text>> and <<glossary-term,term>>.

[[glossary-cluster]] cluster ::

A cluster consists of one or more <<glossary-node,nodes>> which share the
same cluster name. Each cluster has a single master node which is
chosen automatically by the cluster and which can be replaced if the
current master node fails.

[[glossary-ccr]] {ccr} (CCR)::

The {ccr} feature enables you to replicate indices in remote clusters to your
local cluster. For more information, see 
{stack-ov}/xpack-ccr.html[{ccr-cap}].  
  
[[glossary-ccs]] {ccs} (CCS)::

The {ccs} feature enables any node to act as a federated client across
multiple clusters. See <<modules-cross-cluster-search>>.    

[[glossary-document]] document ::

A document is a JSON document which is stored in Elasticsearch. It is
like a row in a table in a relational database. Each document is
stored in an <<glossary-index,index>> and has a <<glossary-type,type>> and an
<<glossary-id,id>>.
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A document is a JSON object (also known in other languages as a hash /
hashmap / associative array) which contains zero or more
<<glossary-field,fields>>, or key-value pairs.
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The original JSON document that is indexed will be stored in the
<<glossary-source_field,`_source` field>>, which is returned by default when
getting or searching for a document.

[[glossary-field]] field ::

A <<glossary-document,document>> contains a list of fields, or key-value
pairs. The value can be a simple (scalar) value (eg a string, integer,
date), or a nested structure like an array or an object. A field is
similar to a column in a table in a relational database.
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The <<glossary-mapping,mapping>> for each field has a field _type_ (not to
be confused with document <<glossary-type,type>>) which indicates the type
of data that can be stored in that field, eg `integer`, `string`,
`object`. The mapping also allows you to define (amongst other things)
how the value for a field should be analyzed.

[[glossary-filter]] filter ::

A filter is a non-scoring <<glossary-query,query>>, meaning that it does not score documents.
It is only concerned about answering the question - "Does this document match?". 
The answer is always a simple, binary yes or no. This kind of query is said to be made 
in a <<query-filter-context,filter context>>, 
hence it is called a filter. Filters are simple checks for set inclusion or exclusion. 
In most cases, the goal of filtering is to reduce the number of documents that have to be examined.

[[glossary-follower-index]] follower index ::  

Follower indices are the target indices for <<glossary-ccr,{ccr}>>. They exist
in your local cluster and replicate <<glossary-leader-index,leader indices>>.

[[glossary-id]] id ::

The ID of a <<glossary-document,document>> identifies a document. The
`index/id` of a document must be unique. If no ID is provided,
then it will be auto-generated. (also see <<glossary-routing,routing>>)  

[[glossary-index]] index ::

An index is like a _table_ in a relational database. It has a
<<glossary-mapping,mapping>> which contains a <<glossary-type,type>>,
which contains the <<glossary-field,fields>> in the index.
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An index is a logical namespace which maps to one or more
<<glossary-primary-shard,primary shards>> and can have zero or more
<<glossary-replica-shard,replica shards>>.

[[glossary-leader-index]] leader index ::  
  
Leader indices are the source indices for <<glossary-ccr,{ccr}>>. They exist
on remote clusters and are replicated to 
<<glossary-follower-index,follower indices>>.

[[glossary-mapping]] mapping ::

A mapping is like a _schema definition_ in a relational database. Each
<<glossary-index,index>> has a mapping, which defines a <<glossary-type,type>>,
plus a number of index-wide settings.
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A mapping can either be defined explicitly, or it will be generated
automatically when a document is indexed.

[[glossary-node]] node ::

A node is a running instance of Elasticsearch which belongs to a
<<glossary-cluster,cluster>>. Multiple nodes can be started on a single
server for testing purposes, but usually you should have one node per
server.
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At startup, a node will use unicast to discover an existing cluster with
the same cluster name and will try to join that cluster.

[[glossary-primary-shard]] primary shard ::

Each document is stored in a single primary <<glossary-shard,shard>>. When
you index a document, it is indexed first on the primary shard, then
on all <<glossary-replica-shard,replicas>> of the primary shard.
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By default, an <<glossary-index,index>> has one primary shard. You can specify
more primary shards to scale the number of <<glossary-document,documents>>
that your index can handle.
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You cannot change the number of primary shards in an index, once the index is
index is created. However, an index can be split into a new index using the
<<indices-split-index, split API>>.
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See also <<glossary-routing,routing>>

[[glossary-query]] query ::

A request for information from {es}. You can think of a query as a question,
written in a way {es} understands. A search consists of one or more queries
combined.
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There are two types of queries: _scoring queries_ and _filters_. For more
information about query types, see <<query-filter-context>>.

[[glossary-recovery]] recovery ::
The process of syncing a shard copy from a source shard. Upon completion, the recovery process makes the shard copy available for queries.
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Recovery automatically occurs anytime a shard moves to a different node in the same cluster, including:

* Node startup
* Node failure
* Index shard replication
* Snapshot restoration

[[glossary-replica-shard]] replica shard ::

Each <<glossary-primary-shard,primary shard>> can have zero or more
replicas. A replica is a copy of the primary shard, and has two
purposes:
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1.  increase failover: a replica shard can be promoted to a primary
shard if the primary fails
2.  increase performance: get and search requests can be handled by
primary or replica shards.
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By default, each primary shard has one replica, but the number of
replicas can be changed dynamically on an existing index. A replica
shard will never be started on the same node as its primary shard.

[[glossary-routing]] routing ::

When you index a document, it is stored on a single
<<glossary-primary-shard,primary shard>>. That shard is chosen by hashing
the `routing` value. By default, the `routing` value is derived from
the ID of the document or, if the document has a specified parent
document, from the ID of the parent document (to ensure that child and
parent documents are stored on the same shard).
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This value can be overridden by specifying a `routing` value at index
time, or a <<mapping-routing-field,routing
field>> in the <<glossary-mapping,mapping>>.

[[glossary-shard]] shard ::

A shard is a single Lucene instance. It is a low-level “worker” unit
which is managed automatically by Elasticsearch. An index is a logical
namespace which points to <<glossary-primary-shard,primary>> and
<<glossary-replica-shard,replica>> shards.
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Other than defining the number of primary and replica shards that an
index should have, you never need to refer to shards directly.
Instead, your code should deal only with an index.
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Elasticsearch distributes shards amongst all <<glossary-node,nodes>> in the
<<glossary-cluster,cluster>>, and can move shards automatically from one
node to another in the case of node failure, or the addition of new
nodes.

[[glossary-source_field]] source field ::

By default, the JSON document that you index will be stored in the
`_source` field and will be returned by all get and search requests.
This allows you access to the original object directly from search
results, rather than requiring a second step to retrieve the object
from an ID.

[[glossary-term]] term ::

A term is an exact value that is indexed in Elasticsearch. The terms
`foo`, `Foo`, `FOO` are NOT equivalent. Terms (i.e. exact values) can
be searched for using _term_ queries.
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See also <<glossary-text,text>> and <<glossary-analysis,analysis>>.

[[glossary-text]] text ::

Text (or full text) is ordinary unstructured text, such as this
paragraph. By default, text will be <<glossary-analysis,analyzed>> into
<<glossary-term,terms>>, which is what is actually stored in the index.
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Text <<glossary-field,fields>> need to be analyzed at index time in order to
be searchable as full text, and keywords in full text queries must be
analyzed at search time to produce (and search for) the same terms
that were generated at index time.
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See also <<glossary-term,term>> and <<glossary-analysis,analysis>>.

[[glossary-type]] type ::

A type used to represent the _type_ of document, e.g. an `email`, a `user`, or a `tweet`.
Types are deprecated and are in the process of being removed.  See <<removal-of-types>>.