- 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
- Field stats 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
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- Analyze
- Index Templates
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- Clear Cache
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- 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 1.7 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 Searches
editExecuting Searches
editNow that we have seen a few of the basic search parameters, let’s dig in some more into the Query DSL. Let’s first take a look at the returned document fields. By default, the full JSON document is returned as part of all searches. This is referred to as the source (_source
field in the search hits). If we don’t want the entire source document returned, we have the ability to request only a few fields from within source to be returned.
This example shows how to return two fields, account_number
and balance
(inside of _source
), from the search:
curl -XPOST 'localhost:9200/bank/_search?pretty' -d ' { "query": { "match_all": {} }, "_source": ["account_number", "balance"] }'
Note that the above example simply reduces the _source
field. It will still only return one field named _source
but within it, only the fields account_number
and balance
are included.
If you come from a SQL background, the above is somewhat similar in concept to the SQL SELECT FROM
field list.
Now let’s move on to the query part. Previously, we’ve seen how the match_all
query is used to match all documents. Let’s now introduce a new query called the match
query, which can be thought of as a basic fielded search query (i.e. a search done against a specific field or set of fields).
This example returns the account numbered 20:
curl -XPOST 'localhost:9200/bank/_search?pretty' -d ' { "query": { "match": { "account_number": 20 } } }'
This example returns all accounts containing the term "mill" in the address:
curl -XPOST 'localhost:9200/bank/_search?pretty' -d ' { "query": { "match": { "address": "mill" } } }'
This example returns all accounts containing the term "mill" or "lane" in the address:
curl -XPOST 'localhost:9200/bank/_search?pretty' -d ' { "query": { "match": { "address": "mill lane" } } }'
This example is a variant of match
(match_phrase
) that returns all accounts containing the phrase "mill lane" in the address:
curl -XPOST 'localhost:9200/bank/_search?pretty' -d ' { "query": { "match_phrase": { "address": "mill lane" } } }'
Let’s now introduce the bool
(ean) query. The bool
query allows us to compose smaller queries into bigger queries using boolean logic.
This example composes two match
queries and returns all accounts containing "mill" and "lane" in the address:
curl -XPOST 'localhost:9200/bank/_search?pretty' -d ' { "query": { "bool": { "must": [ { "match": { "address": "mill" } }, { "match": { "address": "lane" } } ] } } }'
In the above example, the bool must
clause specifies all the queries that must be true for a document to be considered a match.
In contrast, this example composes two match
queries and returns all accounts containing "mill" or "lane" in the address:
curl -XPOST 'localhost:9200/bank/_search?pretty' -d ' { "query": { "bool": { "should": [ { "match": { "address": "mill" } }, { "match": { "address": "lane" } } ] } } }'
In the above example, the bool should
clause specifies a list of queries either of which must be true for a document to be considered a match.
This example composes two match
queries and returns all accounts that contain neither "mill" nor "lane" in the address:
curl -XPOST 'localhost:9200/bank/_search?pretty' -d ' { "query": { "bool": { "must_not": [ { "match": { "address": "mill" } }, { "match": { "address": "lane" } } ] } } }'
In the above example, the bool must_not
clause specifies a list of queries none of which must be true for a document to be considered a match.
We can combine must
, should
, and must_not
clauses simultaneously inside a bool
query. Furthermore, we can compose bool
queries inside any of these bool
clauses to mimic any complex multi-level boolean logic.
This example returns all accounts of anybody who is 40 years old but don’t live in ID(aho):
curl -XPOST 'localhost:9200/bank/_search?pretty' -d ' { "query": { "bool": { "must": [ { "match": { "age": "40" } } ], "must_not": [ { "match": { "state": "ID" } } ] } } }'