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- Elasticsearch version 8.10.4
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- Dependencies and versions
Percolate query
editPercolate query
editThe percolate
query can be used to match queries
stored in an index. The percolate
query itself
contains the document that will be used as query
to match with the stored queries.
Sample usage
editTo provide a simple example, this documentation uses one index,
my-index-000001
, for both the percolate queries and documents. This setup can
work well when there are just a few percolate queries registered. For heavier
usage, we recommend you store queries and documents in separate indices. For
more details, refer to How it Works Under the Hood.
Create an index with two fields:
response = client.indices.create( index: 'my-index-000001', body: { mappings: { properties: { message: { type: 'text' }, query: { type: 'percolator' } } } } ) puts response
PUT /my-index-000001 { "mappings": { "properties": { "message": { "type": "text" }, "query": { "type": "percolator" } } } }
The message
field is the field used to preprocess the document defined in
the percolator
query before it gets indexed into a temporary index.
The query
field is used for indexing the query documents. It will hold a
json object that represents an actual Elasticsearch query. The query
field
has been configured to use the percolator field type. This field
type understands the query dsl and stores the query in such a way that it can be
used later on to match documents defined on the percolate
query.
Register a query in the percolator:
response = client.index( index: 'my-index-000001', id: 1, refresh: true, body: { query: { match: { message: 'bonsai tree' } } } ) puts response
PUT /my-index-000001/_doc/1?refresh { "query": { "match": { "message": "bonsai tree" } } }
Match a document to the registered percolator queries:
GET /my-index-000001/_search { "query": { "percolate": { "field": "query", "document": { "message": "A new bonsai tree in the office" } } } }
The above request will yield the following response:
{ "took": 13, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped" : 0, "failed": 0 }, "hits": { "total" : { "value": 1, "relation": "eq" }, "max_score": 0.26152915, "hits": [ { "_index": "my-index-000001", "_id": "1", "_score": 0.26152915, "_source": { "query": { "match": { "message": "bonsai tree" } } }, "fields" : { "_percolator_document_slot" : [0] } } ] } }
Parameters
editThe following parameters are required when percolating a document:
|
The field of type |
|
The suffix to be used for the |
|
The source of the document being percolated. |
|
Like the |
|
The type / mapping of the document being percolated. This parameter is deprecated and will be removed in Elasticsearch 8.0. |
Instead of specifying the source of the document being percolated, the source can also be retrieved from an already
stored document. The percolate
query will then internally execute a get request to fetch that document.
In that case the document
parameter can be substituted with the following parameters:
|
The index the document resides in. This is a required parameter. |
|
The type of the document to fetch. This parameter is deprecated and will be removed in Elasticsearch 8.0. |
|
The id of the document to fetch. This is a required parameter. |
|
Optionally, routing to be used to fetch document to percolate. |
|
Optionally, preference to be used to fetch document to percolate. |
|
Optionally, the expected version of the document to be fetched. |
Percolating in a filter context
editIn case you are not interested in the score, better performance can be expected by wrapping
the percolator query in a bool
query’s filter clause or in a constant_score
query:
GET /my-index-000001/_search { "query": { "constant_score": { "filter": { "percolate": { "field": "query", "document": { "message": "A new bonsai tree in the office" } } } } } }
At index time terms are extracted from the percolator query and the percolator
can often determine whether a query matches just by looking at those extracted
terms. However, computing scores requires to deserialize each matching query
and run it against the percolated document, which is a much more expensive
operation. Hence if computing scores is not required the percolate
query
should be wrapped in a constant_score
query or a bool
query’s filter clause.
Note that the percolate
query never gets cached by the query cache.
Percolating multiple documents
editThe percolate
query can match multiple documents simultaneously with the indexed percolator queries.
Percolating multiple documents in a single request can improve performance as queries only need to be parsed and
matched once instead of multiple times.
The _percolator_document_slot
field that is being returned with each matched percolator query is important when percolating
multiple documents simultaneously. It indicates which documents matched with a particular percolator query. The numbers
correlate with the slot in the documents
array specified in the percolate
query.
GET /my-index-000001/_search { "query": { "percolate": { "field": "query", "documents": [ { "message": "bonsai tree" }, { "message": "new tree" }, { "message": "the office" }, { "message": "office tree" } ] } } }
{ "took": 13, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped" : 0, "failed": 0 }, "hits": { "total" : { "value": 1, "relation": "eq" }, "max_score": 0.7093853, "hits": [ { "_index": "my-index-000001", "_id": "1", "_score": 0.7093853, "_source": { "query": { "match": { "message": "bonsai tree" } } }, "fields" : { "_percolator_document_slot" : [0, 1, 3] } } ] } }
Percolating an Existing Document
editIn order to percolate a newly indexed document, the percolate
query can be used. Based on the response
from an index request, the _id
and other meta information can be used to immediately percolate the newly added
document.
Example
editBased on the previous example.
Index the document we want to percolate:
response = client.index( index: 'my-index-000001', id: 2, body: { message: 'A new bonsai tree in the office' } ) puts response
PUT /my-index-000001/_doc/2 { "message" : "A new bonsai tree in the office" }
Index response:
{ "_index": "my-index-000001", "_id": "2", "_version": 1, "_shards": { "total": 2, "successful": 1, "failed": 0 }, "result": "created", "_seq_no" : 1, "_primary_term" : 1 }
Percolating an existing document, using the index response as basis to build to new search request:
GET /my-index-000001/_search { "query": { "percolate": { "field": "query", "index": "my-index-000001", "id": "2", "version": 1 } } }
The version is optional, but useful in certain cases. We can ensure that we are trying to percolate the document we just have indexed. A change may be made after we have indexed, and if that is the case the search request would fail with a version conflict error. |
The search response returned is identical as in the previous example.
Percolate query and highlighting
editThe percolate
query is handled in a special way when it comes to highlighting. The queries hits are used
to highlight the document that is provided in the percolate
query. Whereas with regular highlighting the query in
the search request is used to highlight the hits.
Example
editThis example is based on the mapping of the first example.
Save a query:
response = client.index( index: 'my-index-000001', id: 3, refresh: true, body: { query: { match: { message: 'brown fox' } } } ) puts response
PUT /my-index-000001/_doc/3?refresh { "query": { "match": { "message": "brown fox" } } }
Save another query:
response = client.index( index: 'my-index-000001', id: 4, refresh: true, body: { query: { match: { message: 'lazy dog' } } } ) puts response
PUT /my-index-000001/_doc/4?refresh { "query": { "match": { "message": "lazy dog" } } }
Execute a search request with the percolate
query and highlighting enabled:
GET /my-index-000001/_search { "query": { "percolate": { "field": "query", "document": { "message": "The quick brown fox jumps over the lazy dog" } } }, "highlight": { "fields": { "message": {} } } }
This will yield the following response.
{ "took": 7, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped" : 0, "failed": 0 }, "hits": { "total" : { "value": 2, "relation": "eq" }, "max_score": 0.26152915, "hits": [ { "_index": "my-index-000001", "_id": "3", "_score": 0.26152915, "_source": { "query": { "match": { "message": "brown fox" } } }, "highlight": { "message": [ "The quick <em>brown</em> <em>fox</em> jumps over the lazy dog" ] }, "fields" : { "_percolator_document_slot" : [0] } }, { "_index": "my-index-000001", "_id": "4", "_score": 0.26152915, "_source": { "query": { "match": { "message": "lazy dog" } } }, "highlight": { "message": [ "The quick brown fox jumps over the <em>lazy</em> <em>dog</em>" ] }, "fields" : { "_percolator_document_slot" : [0] } } ] } }
Instead of the query in the search request highlighting the percolator hits, the percolator queries are highlighting
the document defined in the percolate
query.
When percolating multiple documents at the same time like the request below then the highlight response is different:
GET /my-index-000001/_search { "query": { "percolate": { "field": "query", "documents": [ { "message": "bonsai tree" }, { "message": "new tree" }, { "message": "the office" }, { "message": "office tree" } ] } }, "highlight": { "fields": { "message": {} } } }
The slightly different response:
{ "took": 13, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped" : 0, "failed": 0 }, "hits": { "total" : { "value": 1, "relation": "eq" }, "max_score": 0.7093853, "hits": [ { "_index": "my-index-000001", "_id": "1", "_score": 0.7093853, "_source": { "query": { "match": { "message": "bonsai tree" } } }, "fields" : { "_percolator_document_slot" : [0, 1, 3] }, "highlight" : { "0_message" : [ "<em>bonsai</em> <em>tree</em>" ], "3_message" : [ "office <em>tree</em>" ], "1_message" : [ "new <em>tree</em>" ] } } ] } }
Specifying multiple percolate queries
editIt is possible to specify multiple percolate
queries in a single search request:
GET /my-index-000001/_search { "query": { "bool": { "should": [ { "percolate": { "field": "query", "document": { "message": "bonsai tree" }, "name": "query1" } }, { "percolate": { "field": "query", "document": { "message": "tulip flower" }, "name": "query2" } } ] } } }
The |
The _percolator_document_slot
field name will be suffixed with what is specified in the _name
parameter.
If that isn’t specified then the field
parameter will be used, which in this case will result in ambiguity.
The above search request returns a response similar to this:
{ "took": 13, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped" : 0, "failed": 0 }, "hits": { "total" : { "value": 1, "relation": "eq" }, "max_score": 0.26152915, "hits": [ { "_index": "my-index-000001", "_id": "1", "_score": 0.26152915, "_source": { "query": { "match": { "message": "bonsai tree" } } }, "fields" : { "_percolator_document_slot_query1" : [0] } } ] } }
How it Works Under the Hood
editWhen indexing a document into an index that has the percolator field type mapping configured, the query part of the document gets parsed into a Lucene query and is stored into the Lucene index. A binary representation of the query gets stored, but also the query’s terms are analyzed and stored into an indexed field.
At search time, the document specified in the request gets parsed into a Lucene document and is stored in a in-memory temporary Lucene index. This in-memory index can just hold this one document and it is optimized for that. After this a special query is built based on the terms in the in-memory index that select candidate percolator queries based on their indexed query terms. These queries are then evaluated by the in-memory index if they actually match.
The selecting of candidate percolator queries matches is an important performance optimization during the execution
of the percolate
query as it can significantly reduce the number of candidate matches the in-memory index needs to
evaluate. The reason the percolate
query can do this is because during indexing of the percolator queries the query
terms are being extracted and indexed with the percolator query. Unfortunately the percolator cannot extract terms from
all queries (for example the wildcard
or geo_shape
query) and as a result of that in certain cases the percolator
can’t do the selecting optimization (for example if an unsupported query is defined in a required clause of a boolean query
or the unsupported query is the only query in the percolator document). These queries are marked by the percolator and
can be found by running the following search:
response = client.search( body: { query: { term: { "query.extraction_result": 'failed' } } } ) puts response
GET /_search { "query": { "term" : { "query.extraction_result" : "failed" } } }
The above example assumes that there is a query
field of type
percolator
in the mappings.
Given the design of percolation, it often makes sense to use separate indices for the percolate queries and documents being percolated, as opposed to a single index as we do in examples. There are a few benefits to this approach:
- Because percolate queries contain a different set of fields from the percolated documents, using two separate indices allows for fields to be stored in a denser, more efficient way.
- Percolate queries do not scale in the same way as other queries, so percolation performance may benefit from using a different index configuration, like the number of primary shards.
Notes
editAllow expensive queries
editPercolate queries will not be executed if search.allow_expensive_queries
is set to false.
Using custom similarities
editPercolate queries will not respect any configured custom similarity. They always use the default Lucene similarity.
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