- Elasticsearch Guide: other versions:
- Getting Started
- Setup
- Breaking changes
- API Conventions
- Document APIs
- Search APIs
- Search
- URI Search
- Request Body Search
- Search Template
- Search Shards API
- Aggregations
- Min Aggregation
- Max Aggregation
- Sum Aggregation
- Avg Aggregation
- Stats Aggregation
- Extended Stats Aggregation
- Value Count Aggregation
- Percentiles Aggregation
- Percentile Ranks Aggregation
- Cardinality Aggregation
- Geo Bounds Aggregation
- Top hits Aggregation
- Scripted Metric Aggregation
- Global Aggregation
- Filter Aggregation
- Filters Aggregation
- Missing Aggregation
- Nested Aggregation
- Reverse nested Aggregation
- Children Aggregation
- Terms Aggregation
- Significant Terms Aggregation
- Range Aggregation
- Date Range Aggregation
- IPv4 Range Aggregation
- Histogram Aggregation
- Date Histogram Aggregation
- Geo Distance Aggregation
- GeoHash grid Aggregation
- Facets
- Suggesters
- Multi Search API
- Count API
- Search Exists API
- Validate API
- Explain API
- Percolator
- More Like This API
- Indices APIs
- Create Index
- Delete Index
- Get Index
- Indices Exists
- Open / Close Index API
- Put Mapping
- Get Mapping
- Get Field Mapping
- Types Exists
- Delete Mapping
- Index Aliases
- Update Indices Settings
- Get Settings
- Analyze
- Index Templates
- Warmers
- Status
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- Indices Recovery
- Clear Cache
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- Optimize
- Upgrade
- Shadow replica indices
- cat APIs
- Cluster APIs
- Query DSL
- Queries
- Match Query
- Multi Match Query
- Bool Query
- Boosting Query
- Common Terms Query
- Constant Score Query
- Dis Max Query
- Filtered Query
- Fuzzy Like This Query
- Fuzzy Like This Field Query
- Function Score Query
- Fuzzy Query
- GeoShape Query
- Has Child Query
- Has Parent Query
- Ids Query
- Indices Query
- Match All Query
- More Like This Query
- Nested Query
- Prefix Query
- Query String Query
- Simple Query String Query
- Range Query
- Regexp Query
- Span First Query
- Span Multi Term Query
- Span Near Query
- Span Not Query
- Span Or Query
- Span Term Query
- Term Query
- Terms Query
- Top Children Query
- Wildcard Query
- Minimum Should Match
- Multi Term Query Rewrite
- Template Query
- Filters
- And Filter
- Bool Filter
- Exists Filter
- Geo Bounding Box Filter
- Geo Distance Filter
- Geo Distance Range Filter
- Geo Polygon Filter
- GeoShape Filter
- Geohash Cell Filter
- Has Child Filter
- Has Parent Filter
- Ids Filter
- Indices Filter
- Limit Filter
- Match All Filter
- Missing Filter
- Nested Filter
- Not Filter
- Or Filter
- Prefix Filter
- Query Filter
- Range Filter
- Regexp Filter
- Script Filter
- Term Filter
- Terms Filter
- Type Filter
- Queries
- Mapping
- Analysis
- Analyzers
- Tokenizers
- Token Filters
- Standard Token Filter
- ASCII Folding Token Filter
- Length Token Filter
- Lowercase Token Filter
- Uppercase Token Filter
- NGram Token Filter
- Edge NGram Token Filter
- Porter Stem Token Filter
- Shingle Token Filter
- Stop Token Filter
- Word Delimiter Token Filter
- Stemmer Token Filter
- Stemmer Override Token Filter
- Keyword Marker Token Filter
- Keyword Repeat Token Filter
- KStem Token Filter
- Snowball Token Filter
- Phonetic Token Filter
- Synonym Token Filter
- Compound Word Token Filter
- Reverse Token Filter
- Elision Token Filter
- Truncate Token Filter
- Unique Token Filter
- Pattern Capture Token Filter
- Pattern Replace Token Filter
- Trim Token Filter
- Limit Token Count Token Filter
- Hunspell Token Filter
- Common Grams Token Filter
- Normalization Token Filter
- CJK Width Token Filter
- CJK Bigram Token Filter
- Delimited Payload Token Filter
- Keep Words Token Filter
- Keep Types Token Filter
- Classic Token Filter
- Apostrophe Token Filter
- Character Filters
- ICU Analysis Plugin
- Modules
- Index Modules
- Testing
- Glossary of terms
WARNING: Version 1.5 of Elasticsearch has passed its EOL date.
This documentation is no longer being maintained and may be removed. If you are running this version, we strongly advise you to upgrade. For the latest information, see the current release documentation.
Executing Filters
editExecuting Filters
editIn the previous section, we skipped over a little detail called the document score (_score
field in the search results). The score is a numeric value that is a relative measure of how well the document matches the search query that we specified. The higher the score, the more relevant the document is, the lower the score, the less relevant the document is.
All queries in Elasticsearch trigger computation of the relevance scores. In cases where we do not need the relevance scores, Elasticsearch provides another query capability in the form of <<query-dsl-filters,filters>. Filters are similar in concept to queries except that they are optimized for much faster execution speeds for two primary reasons:
- Filters do not score so they are faster to execute than queries
- Filters can be cached in memory allowing repeated search executions to be significantly faster than queries
To understand filters, let’s first introduce the filtered
query, which allows you to combine a query (like match_all
, match
, bool
, etc.) together with a filter. As an example, let’s introduce the range
filter, which allows us to filter documents by a range of values. This is generally used for numeric or date filtering.
This example uses a filtered query to return all accounts with balances between 20000 and 30000, inclusive. In other words, we want to find accounts with a balance that is greater than or equal to 20000 and less than or equal to 30000.
curl -XPOST 'localhost:9200/bank/_search?pretty' -d ' { "query": { "filtered": { "query": { "match_all": {} }, "filter": { "range": { "balance": { "gte": 20000, "lte": 30000 } } } } } }'
Dissecting the above, the filtered query contains a match_all
query (the query part) and a range
filter (the filter part). We can substitute any other query into the query part as well as any other filter into the filter part. In the above case, the range filter makes perfect sense since documents falling into the range all match "equally", i.e., no document is more relevant than another.
In general, the easiest way to decide whether you want a filter or a query is to ask yourself if you care about the relevance score or not. If relevance is not important, use filters, otherwise, use queries. If you come from a SQL background, queries and filters are similar in concept to the SELECT WHERE
clause, although more so for filters than queries.
In addition to the match_all
, match
, bool
, filtered
, and range
queries, there are a lot of other query/filter types that are available and we won’t go into them here. Since we already have a basic understanding of how they work, it shouldn’t be too difficult to apply this knowledge in learning and experimenting with the other query/filter types.