[[field-centric]] === Field-Centric Queries

All three of the preceding problems stem from ((("field-centric queries")))((("multifield search", "field-centric queries, problems with")))((("most fields queries", "problems with field-centric queries")))most_fields being field-centric rather than term-centric: it looks for the most matching fields, when really what we're interested is the most matching terms.

NOTE: The best_fields type is also field-centric((("best fields queries", "problems with field-centric queries"))) and suffers from similar problems.

First we'll look at why these problems exist, and then how we can combat them.

==== Problem 1: Matching the Same Word in Multiple Fields

Think about how the most_fields query is executed: Elasticsearch generates a separate match query for each field and then wraps these match queries in an outer bool query.

We can see this by passing our query through the validate-query API:


GET /_validate/query?explain { "query": { "multi_match": { "query": "Poland Street W1V", "type": "most_fields", "fields": [ "street", "city", "country", "postcode" ] } }


// SENSE: 110_Multi_Field_Search/40_Entity_search_problems.json

which yields this explanation:

(street:poland   street:street   street:w1v)
(city:poland     city:street     city:w1v)
(country:poland  country:street  country:w1v)
(postcode:poland postcode:street postcode:w1v)

You can see that a document matching just the word poland in two fields could score higher than a document matching poland and street in one field.

==== Problem 2: Trimming the Long Tail

In <>, we talked about((("and operator", "most fields and best fields queries and")))((("minimum_should_match parameter", "most fields and best fields queries"))) using the and operator or the minimum_should_match parameter to trim the long tail of almost irrelevant results. Perhaps we could try this:


{ "query": { "multi_match": { "query": "Poland Street W1V", "type": "most_fields", "operator": "and", <1> "fields": [ "street", "city", "country", "postcode" ] } }


// SENSE: 110_Multi_Field_Search/40_Entity_search_problems.json

<1> All terms must be present.

However, with best_fields or most_fields, these parameters are passed down to the generated match queries. The explanation for this query shows the following:

(+street:poland   +street:street   +street:w1v)
(+city:poland     +city:street     +city:w1v)
(+country:poland  +country:street  +country:w1v)
(+postcode:poland +postcode:street +postcode:w1v)

In other words, using the and operator means that all words must exist in the same field, which is clearly wrong! It is unlikely that any documents would match this query.

==== Problem 3: Term Frequencies

In <>, we explained that the default similarity algorithm used to calculate the relevance score ((("term frequency", "problems with field-centric queries")))for each term is TF/IDF:

Term frequency::

The more often a term appears in a field in a single document, the more
relevant the document.

Inverse document frequency::

The more often a term appears in a field in all documents in the index,
the less relevant is that term.

When searching against multiple fields, TF/IDF can((("Term Frequency/Inverse Document Frequency (TF/IDF) similarity algorithm", "surprising results when searching against multiple fields"))) introduce some surprising results.

Consider our example of searching for `Peter Smith'' using thefirst_nameandlast_name` fields.((("inverse document frequency", "field-centric queries and"))) Peter is a common first name and Smith is a common last name--both will have low IDFs. But what if we have another person in the index whose name is Smith Williams? Smith as a first name is very uncommon and so will have a high IDF!

A simple query like the following may well return Smith Williams above Peter Smith in spite of the fact that the second person is a better match than the first.


{ "query": { "multi_match": { "query": "Peter Smith", "type": "most_fields", "fields": [ "*_name" ] } }


// SENSE: 110_Multi_Field_Search/40_Bad_frequencies.json

The high IDF of smith in the first name field can overwhelm the two low IDFs of peter as a first name and smith as a last name.

==== Solution

These problems only exist because we are dealing with multiple fields. If we were to combine all of these fields into a single field, the problems would vanish. We could achieve this by adding a full_name field to our person document:


{ "first_name": "Peter", "last_name": "Smith", "full_name": "Peter Smith"


When querying just the full_name field:

  • Documents with more matching words would trump documents with the same word repeated.

  • The minimum_should_match and operator parameters would function as expected.

  • The inverse document frequencies for first and last names would be combined so it wouldn't matter whether Smith were a first or last name anymore.

While this would work, we don't like having to store redundant data. Instead, Elasticsearch offers us two solutions--one at index time and one at search time--which we discuss next.