Query and filter context
editQuery and filter context
editRelevance scores
editBy default, Elasticsearch sorts matching search results by relevance score, which measures how well each document matches a query.
The relevance score is a positive floating point number, returned in the
_score
metadata field of the search API. The higher the
_score
, the more relevant the document. While each query type can calculate
relevance scores differently, score calculation also depends on whether the
query clause is run in a query or filter context.
Query context
editIn the query context, a query clause answers the question “How well does this
document match this query clause?” Besides deciding whether or not the
document matches, the query clause also calculates a relevance score in the
_score
metadata field.
Query context is in effect whenever a query clause is passed to a query
parameter, such as the query
parameter in the
search API.
Filter context
editA filter answers the binary question “Does this document match this query clause?”. The answer is simply "yes" or "no". Filtering has several benefits:
- Simple binary logic: In a filter context, a query clause determines document matches based on a yes/no criterion, without score calculation.
- Performance: Because they don’t compute relevance scores, filters execute faster than queries.
- Caching: Elasticsearch automatically caches frequently used filters, speeding up subsequent search performance.
- Resource efficiency: Filters consume less CPU resources compared to full-text queries.
- Query combination: Filters can be combined with scored queries to refine result sets efficiently.
Filters are particularly effective for querying structured data and implementing "must have" criteria in complex searches.
Structured data refers to information that is highly organized and formatted in a predefined manner. In the context of Elasticsearch, this typically includes:
- Numeric fields (integers, floating-point numbers)
- Dates and timestamps
- Boolean values
- Keyword fields (exact match strings)
- Geo-points and geo-shapes
Unlike full-text fields, structured data has a consistent, predictable format, making it ideal for precise filtering operations.
Common filter applications include:
-
Date range checks: for example is the
timestamp
field between 2015 and 2016 -
Specific field value checks: for example is the
status
field equal to "published" or is theauthor
field equal to "John Doe"
Filter context applies when a query clause is passed to a filter
parameter, such as:
-
filter
ormust_not
parameters inbool
queries -
filter
parameter inconstant_score
queries -
filter
aggregations
Filters optimize query performance and efficiency, especially for structured data queries and when combined with full-text searches.
Example of query and filter contexts
editBelow is an example of query clauses being used in query and filter context
in the search
API. This query will match documents where all of the following
conditions are met:
-
The
title
field contains the wordsearch
. -
The
content
field contains the wordelasticsearch
. -
The
status
field contains the exact wordpublished
. -
The
publish_date
field contains a date from 1 Jan 2015 onwards.
$params = [ 'body' => [ 'query' => [ 'bool' => [ 'must' => [ [ 'match' => [ 'title' => 'Search', ], ], [ 'match' => [ 'content' => 'Elasticsearch', ], ], ], 'filter' => [ [ 'term' => [ 'status' => 'published', ], ], [ 'range' => [ 'publish_date' => [ 'gte' => '2015-01-01', ], ], ], ], ], ], ], ]; $response = $client->search($params);
resp = client.search( query={ "bool": { "must": [ { "match": { "title": "Search" } }, { "match": { "content": "Elasticsearch" } } ], "filter": [ { "term": { "status": "published" } }, { "range": { "publish_date": { "gte": "2015-01-01" } } } ] } }, ) print(resp)
response = client.search( body: { query: { bool: { must: [ { match: { title: 'Search' } }, { match: { content: 'Elasticsearch' } } ], filter: [ { term: { status: 'published' } }, { range: { publish_date: { gte: '2015-01-01' } } } ] } } } ) puts response
res, err := es.Search( es.Search.WithBody(strings.NewReader(`{ "query": { "bool": { "must": [ { "match": { "title": "Search" } }, { "match": { "content": "Elasticsearch" } } ], "filter": [ { "term": { "status": "published" } }, { "range": { "publish_date": { "gte": "2015-01-01" } } } ] } } }`)), es.Search.WithPretty(), ) fmt.Println(res, err)
const response = await client.search({ query: { bool: { must: [ { match: { title: "Search", }, }, { match: { content: "Elasticsearch", }, }, ], filter: [ { term: { status: "published", }, }, { range: { publish_date: { gte: "2015-01-01", }, }, }, ], }, }, }); console.log(response);
GET /_search { "query": { "bool": { "must": [ { "match": { "title": "Search" }}, { "match": { "content": "Elasticsearch" }} ], "filter": [ { "term": { "status": "published" }}, { "range": { "publish_date": { "gte": "2015-01-01" }}} ] } } }
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Scores calculated for queries in query context are represented as single precision floating point numbers; they have only 24 bits for significand’s precision. Score calculations that exceed the significand’s precision will be converted to floats with loss of precision.
Use query clauses in query context for conditions which should affect the score of matching documents (i.e. how well does the document match), and use all other query clauses in filter context.