15_Multi_value_fields.md

=== Multivalue Fields

A curious thing can happen when you try to use phrase matching on multivalue fields. ((("proximity matching", "on multivalue fields")))((("match_phrase query", "on multivalue fields"))) Imagine that you index this document:

[source,js]

PUT /my_index/groups/1 { "names": [ "John Abraham", "Lincoln Smith"] }

// SENSE: 120_Proximity_Matching/15_Multi_value_fields.json

Then run a phrase query for Abraham Lincoln:

[source,js]

GET /my_index/groups/_search { "query": { "match_phrase": { "names": "Abraham Lincoln" } } }

// SENSE: 120_Proximity_Matching/15_Multi_value_fields.json

Surprisingly, our document matches, even though Abraham and Lincoln belong to two different people in the names array. The reason for this comes down to the way arrays are indexed in Elasticsearch.

When John Abraham is analyzed, it produces this:

• Position 1: john
• Position 2: abraham

Then when Lincoln Smith is analyzed, it produces this:

• Position 3: lincoln
• Position 4: smith

In other words, Elasticsearch produces exactly the same list of tokens as it would have for the single string John Abraham Lincoln Smith. Our example query looks for abraham directly followed by lincoln, and these two terms do indeed exist, and they are right next to each other, so the query matches.

Fortunately, there is a simple workaround for cases like these, called the position_offset_gap, which((("mapping (types)", "position_offset_gap")))((("position_offset_gap"))) we need to configure in the field mapping:

[source,js]

DELETE /my_index/groups/ <1>

PUT /my_index/_mapping/groups <2> { "properties": { "names": { "type": "string", "position_offset_gap": 100 } } }

// SENSE: 120_Proximity_Matching/15_Multi_value_fields.json

<1> First delete the groups mapping and all documents of that type. <2> Then create a new groups mapping with the correct values.

The position_offset_gap setting tells Elasticsearch that it should increase the current term position by the specified value for every new array element. So now, when we index the array of names, the terms are emitted with the following positions:

• Position 1: john
• Position 2: abraham
• Position 103: lincoln
• Position 104: smith

Our phrase query would no longer match a document like this because abraham and lincoln are now 100 positions apart. You would have to add a slop value of 100 in order for this document to match.