Getting ready

You need a working ElasticSearch cluster.

How to do it...

To define a multifield we need to do the following:

If we consider the item of our order example, we can index the name as multi_field as shown in the following code:

"name": {
  "type": "multi_field",
  "fields": {
    "name": {
      "type": "string",
      "index": "not_analyzed"
    },
  "tk": {
    "type": "string",
    "index": "analyzed"
  },
  "code": {
    "type": "string",
    "index": "analyzed",
    "analyzer": "code_analyzer"
  }
  }
},

If we already have a mapping stored in ElasticSearch, and if we want to upgrade the field in a multifield, it's enough to save a new mapping with a different type and ElasticSearch provides automatic merging.

How it works...

During indexing, when ElasticSearch processes a type field as multi_field, it reprocesses the same field for every subfield defined in the mapping.

To access the subfields of multi_field, we have a new path built on the base field plus the subfield name. If we consider the preceding example, we have:

If you notice in the preceding example, we have changed the analyzer to introduce a code extractor analyzer that allows extraction of the item code from a string.

Using the multifield if we index a string, such as "Good item to buy - ABC1234", we'll have:

There's more...

MultiField is very useful in data processing, because it allows you to define several ways to process a field data.

For example, if we are working for document content, we can define them as subfield analyzers to extract names, places, date/time, geo location, and so on. The fields of a multifield are standard core type fields; we can do every process we want on them, such as search, filter, facet, and scripting.

See also