Glossary of terms

Analysis is the process of converting full text to 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.

A full text query (not a term query) for FoO:bAR will also be analyzed to the terms foo,bar and will thus match the terms stored in the index.

It is this process of analysis (both at index time and at search time) that allows Elasticsearch to perform full text queries.

Also see text and term.

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 index and has a type and an id.

A document is a JSON object (also known in other languages as a hash / hashmap / associative array) which contains zero or more fields, or key-value pairs.

The original JSON document that is indexed will be stored in the _source field, which is returned by default when getting or searching for a document.

A 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.

The mapping for each field has a field type (not to be confused with document 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.

An index is like a table in a relational database. It has a mapping which contains a type, which contains the fields in the index.

An index is a logical namespace which maps to one or more primary shards and can have zero or more replica shards.

An index alias is a secondary name used to refer to one or more existing indices.

Most Elasticsearch APIs accept an index alias in place of an index name.

See Add index alias.

Defines settings and mappings to apply to new indexes that match a simple naming pattern, such as logs-*. An index template can also attach a lifecycle policy to the new index. Index templates are used to automatically configure indices created during rollover.

A mapping is like a schema definition in a relational database. Each index has a mapping, which defines a type, plus a number of index-wide settings.

A mapping can either be defined explicitly, or it will be generated automatically when a document is indexed.

A node is a running instance of Elasticsearch which belongs to a cluster. Multiple nodes can be started on a single server for testing purposes, but usually you should have one node per server.

At startup, a node will use unicast to discover an existing cluster with the same cluster name and will try to join that cluster.

Each document is stored in a single primary shard. When you index a document, it is indexed first on the primary shard, then on all replicas of the primary shard.

By default, an index has one primary shard. You can specify more primary shards to scale the number of documents that your index can handle.

You cannot change the number of primary shards in an index, once the index is created. However, an index can be split into a new index using the split API.

See also routing

A request for information from Elasticsearch. You can think of a query as a question, written in a way Elasticsearch understands. A search consists of one or more queries combined.

There are two types of queries: scoring queries and filters. For more information about query types, see Query and filter context.

Shard recovery is the process of syncing a replica shard from a primary shard. Upon completion, the replica shard is available for search.

Recovery automatically occurs during the following processes:

Each primary shard can have zero or more replicas. A replica is a copy of the primary shard, and has two purposes:

Redirect an alias to begin writing to a new index when the existing index reaches a certain age, number of docs, or size. The new index is automatically configured according to any matching index templates. For example, if you’re indexing log data, you might use rollover to create daily or weekly indices. See the rollover index API.

When you index a document, it is stored on a single 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).

This value can be overridden by specifying a routing value at index time, or a routing field in the mapping.

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 primary and replica shards.

+ 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.

+ Elasticsearch distributes shards amongst all nodes in the cluster, and can move shards automatically from one node to another in the case of node failure, or the addition of new nodes.

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.

See also text and analysis.

Text (or full text) is ordinary unstructured text, such as this paragraph. By default, text will be analyzed into terms, which is what is actually stored in the index.

Text 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.

See also term and analysis.