Getting ready

You will require a working ElasticSearch cluster.

How to do it...

We can rewrite the order mapping of the Mapping base types recipe using an array of items as shown in the following code:

{
  "order" : {
    "properties" : {
      "id" : {"type" : "string", 
      "store" : "yes", "index":"not_analyzed"},
      "date" : {"type" : "date", "store" : "no", 
        "index":"not_analyzed"},
      "customer_id" : {"type" : "string", "store" : "yes",
        "index":"not_analyzed"},
      "sent" : {"type" : "boolean", "store" : "no", 
        "index":"not_analyzed"},
      "item" : {
        "type" : "object",
        "properties" : {
          "name" : {"type" : "string", "store" : "no",
            "index":"analyzed"},
          "quantity" : {"type" : "integer", 
            "store" : "no",
            "index":"not_analyzed"},
          "vat" : {"type" : "double", "store" : "no",
            "index":"not_analyzed"}
        }
      }
    }
  }
}

How it works...

ElasticSearch speaks native JSON. So, every complex JSON structure can be mapped into it.

When ElasticSearch is parsing an object type, it tries to extract fields and processes them as its defined mapping; otherwise, it learns the structure of the object using reflection.

The most important properties for an object are as follows:

The most used property is properties that allows mapping the fields of the object in ElasticSearch fields.

Disabling the indexing part of a document reduces the index size and allows storing data that must not be searched.

There's more...

Also, there are properties that are rarely used, such as index_name and path, which changes the how Lucene index the object, modifying the inner structure of an index.

See also