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 defines the fields in the index, which are grouped by multiple type.

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

A mapping is like a schema definition in a relational database. Each index has a mapping, which defines each type within the index, 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 5 primary shards. You can specify fewer or 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.

See also routing

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

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.

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.