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Analysis is the process of converting full text to terms. Depending on which analyzer is used, these phrases:
foo,barwill probably all result in the terms
bar. These terms are what is actually stored in the index.
A full text query (not a term query) for
FoO:bARwill also be analyzed to the terms
barand 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.
- A cluster consists of one or more 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.
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
_sourcefield, which is returned by default when getting or searching for a document.
The ID of a document identifies a document. The
index/type/idof a document must be unique. If no ID is provided, then it will be auto-generated. (also see routing)
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
object. The mapping also allows you to define (amongst other things) how the value for a field should be analyzed.
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.
- primary shard
You cannot change the number of primary shards in an index, once the index is created.
See also routing
- replica shard
Each primary shard can have zero or more replicas. A replica is a copy of the primary shard, and has two purposes:
- increase failover: a replica shard can be promoted to a primary shard if the primary fails
increase performance: get and search requests can be handled by primary or replica shards.
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.
When you index a document, it is stored on a single primary shard. That shard is chosen by hashing the
routingvalue. By default, the
routingvalue 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).
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.
- source field
By default, the JSON document that you index will be stored in the
_sourcefield 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.
Note: the exact JSON string that you indexed will be returned to you, even if it contains invalid JSON. The contents of this field do not indicate anything about how the data in the object has been indexed.
A term is an exact value that is indexed in elasticsearch. The terms
FOOare NOT equivalent. Terms (i.e. exact values) can be searched for using term queries.
See also text and analysis.
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.
- A type is like a table in a relational database. Each type has a list of fields that can be specified for documents of that type. The mapping defines how each field in the document is analyzed.