Expand description for terms of Elasticsearch and give examples

Glossary of terms
analysis
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.

cluster
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.
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 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.

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

index
An index is like a database in a relational database. It has a mapping which defines multiple types.

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

mapping
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.

node
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 (or multicast, if specified) to discover an existing cluster with the same cluster name and will try to join that cluster.

primary shard
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.