anchor.diff:  
   If you come from a SQL background, the above is somewhat similar in concept to the `SQL SELECT FROM` field list.
   
  -Now let's move on to the query part. Previously, we've seen how the `match_all` query is used to match all documents. Let's now introduce a new query called the http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/query-dsl-match-query.html[`match` query], which can be thought of as a basic fielded search query (i.e. a search done against a specific field or set of fields).
  +Now let's move on to the query part. Previously, we've seen how the `match_all` query is used to match all documents. Let's now introduce a new query called the <<query-dsl-match-query,`match` query>>, which can be thought of as a basic fielded search query (i.e. a search done against a specific field or set of fields).
   
   This example returns the account numbered 20:
   
  
positive.code: 
  === Introducing the Query Language
  
  Elasticsearch provides a JSON-style domain-specific language that you can use to execute queries. This is referred to as the http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/query-dsl.html[Query DSL]. The query language is quite comprehensive and can be intimidating at first glance but the best way to actually learn it is to start with a few basic examples.
  
  Going back to our last example, we executed this query:
  
  
negative.code: 
  === Batch Processing
  
  In addition to being able to index, update, and delete individual documents, Elasticsearch also provides the ability to perform any of the above operations in batches using the <<docs-bulk,`_bulk` API>>. This functionality is important in that it provides a very efficient mechanism to do multiple operations as fast as possible with as little network roundtrips as possible.
  
  As a quick example, the following call indexes two documents (ID 1 - John Doe and ID 2 - Jane Doe) in one bulk operation:
  
  