Percolate query

The 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.

Create an index with two fields:

PUT /my-index
{
    "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:

PUT /my-index/_doc/1?refresh
{
    "query" : {
        "match" : {
            "message" : "bonsai tree"
        }
    }
}

Match a document to the registered percolator queries:

GET /my-index/_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",
        "_type": "_doc",
        "_id": "1",
        "_score": 0.26152915,
        "_source": {
          "query": {
            "match": {
              "message": "bonsai tree"
            }
          }
        },
        "fields" : {
          "_percolator_document_slot" : [0] 
        }
      }
    ]
  }
}

The following parameters are required when percolating a document:

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:

In 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/_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.

The 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/_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",
        "_type": "_doc",
        "_id": "1",
        "_score": 0.7093853,
        "_source": {
          "query": {
            "match": {
              "message": "bonsai tree"
            }
          }
        },
        "fields" : {
          "_percolator_document_slot" : [0, 1, 3] 
        }
      }
    ]
  }
}

In 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.

Based on the previous example.

Index the document we want to percolate:

PUT /my-index/_doc/2
{
  "message" : "A new bonsai tree in the office"
}

Index response:

{
  "_index": "my-index",
  "_type": "_doc",
  "_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/_search
{
    "query" : {
        "percolate" : {
            "field": "query",
            "index" : "my-index",
            "id" : "2",
            "version" : 1 
        }
    }
}

The search response returned is identical as in the previous example.

The 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.

This example is based on the mapping of the first example.

Save a query:

PUT /my-index/_doc/3?refresh
{
    "query" : {
        "match" : {
            "message" : "brown fox"
        }
    }
}

Save another query:

PUT /my-index/_doc/4?refresh
{
    "query" : {
        "match" : {
            "message" : "lazy dog"
        }
    }
}

Execute a search request with the percolate query and highlighting enabled:

GET /my-index/_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",
        "_type": "_doc",
        "_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",
        "_type": "_doc",
        "_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/_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",
        "_type": "_doc",
        "_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>"
          ]
        }
      }
    ]
  }
}

It is possible to specify multiple percolate queries in a single search request:

GET /my-index/_search
{
    "query" : {
        "bool" : {
            "should" : [
                {
                    "percolate" : {
                        "field" : "query",
                        "document" : {
                            "message" : "bonsai tree"
                        },
                        "name": "query1" 
                    }
                },
                {
                    "percolate" : {
                        "field" : "query",
                        "document" : {
                            "message" : "tulip flower"
                        },
                        "name": "query2" 
                    }
                }
            ]
        }
    }
}

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",
        "_type": "_doc",
        "_id": "1",
        "_score": 0.26152915,
        "_source": {
          "query": {
            "match": {
              "message": "bonsai tree"
            }
          }
        },
        "fields" : {
          "_percolator_document_slot_query1" : [0] 
        }
      }
    ]
  }
}

When 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:

GET /_search
{
  "query": {
    "term" : {
      "query.extraction_result" : "failed"
    }
  }
}

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: