Query and filter context

By 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.

In 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.

A filter answers the binary question “Does this document match this query clause?”. The answer is simply "yes" or "no". Filtering has several benefits:

1. Simple binary logic: In a filter context, a query clause determines document matches based on a yes/no criterion, without score calculation.
2. Performance: Because they don’t compute relevance scores, filters execute faster than queries.
3. Caching: Elasticsearch automatically caches frequently used filters, speeding up subsequent search performance.
4. Resource efficiency: Filters consume less CPU resources compared to full-text queries.
5. 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 the author field equal to "John Doe"

Filter context applies when a query clause is passed to a filter parameter, such as:

• filter or must_not parameters in bool queries
• filter parameter in constant_score queries
• filter aggregations

Filters optimize query performance and efficiency, especially for structured data queries and when combined with full-text searches.

Below 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 word search.
• The content field contains the word elasticsearch.
• The status field contains the exact word published.
• The publish_date field contains a date from 1 Jan 2015 onwards.

				GET /_search
					{
  "query": {
    "bool": {
      "must": [
        { "match": { "title":   "Search"        }},
        { "match": { "content": "Elasticsearch" }}
      ],
      "filter": [
        { "term":  { "status": "published" }},
        { "range": { "publish_date": { "gte": "2015-01-01" }}}
      ]
    }
  }
}
		

1. The query parameter indicates query context.
2. The bool and two match clauses are used in query context, which means that they are used to score how well each document matches.
3. The filter parameter indicates filter context. Its term and range clauses are used in filter context. They will filter out documents which do not match, but they will not affect the score for matching documents.