Percolator type

The percolator field type parses a json structure into a native query and stores that query, so that the percolate query can use it to match provided documents.

Any field that contains a json object can be configured to be a percolator field. The percolator field type has no settings. Just configuring the percolator field type is sufficient to instruct Elasticsearch to treat a field as a query.

If the following mapping configures the percolator field type for the query field:

PUT my_index
{
    "mappings": {
        "properties": {
            "query": {
                "type": "percolator"
            },
            "field": {
                "type": "text"
            }
        }
    }
}

Then you can index a query:

PUT my_index/_doc/match_value
{
    "query" : {
        "match" : {
            "field" : "value"
        }
    }
}

Reindexing percolator queries is sometimes required to benefit from improvements made to the percolator field type in new releases.

Reindexing percolator queries can be reindexed by using the reindex api. Lets take a look at the following index with a percolator field type:

PUT index
{
  "mappings": {
    "properties": {
      "query" : {
        "type" : "percolator"
      },
      "body" : {
        "type": "text"
      }
    }
  }
}

POST _aliases
{
  "actions": [
    {
      "add": {
        "index": "index",
        "alias": "queries" 
      }
    }
  ]
}

PUT queries/_doc/1?refresh
{
  "query" : {
    "match" : {
      "body" : "quick brown fox"
    }
  }
}

Lets say you’re going to upgrade to a new major version and in order for the new Elasticsearch version to still be able to read your queries you need to reindex your queries into a new index on the current Elasticsearch version:

PUT new_index
{
  "mappings": {
    "properties": {
      "query" : {
        "type" : "percolator"
      },
      "body" : {
        "type": "text"
      }
    }
  }
}

POST /_reindex?refresh
{
  "source": {
    "index": "index"
  },
  "dest": {
    "index": "new_index"
  }
}

POST _aliases
{
  "actions": [ 
    {
      "remove": {
        "index" : "index",
        "alias": "queries"
      }
    },
    {
      "add": {
        "index": "new_index",
        "alias": "queries"
      }
    }
  ]
}

Executing the percolate query via the queries alias:

GET /queries/_search
{
  "query": {
    "percolate" : {
      "field" : "query",
      "document" : {
        "body" : "fox jumps over the lazy dog"
      }
    }
  }
}

now returns matches from the new index:

{
  "took": 3,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped" : 0,
    "failed": 0
  },
  "hits": {
    "total" : {
        "value": 1,
        "relation": "eq"
    },
    "max_score": 0.13076457,
    "hits": [
      {
        "_index": "new_index", 
        "_type": "_doc",
        "_id": "1",
        "_score": 0.13076457,
        "_source": {
          "query": {
            "match": {
              "body": "quick brown fox"
            }
          }
        },
        "fields" : {
          "_percolator_document_slot" : [0]
        }
      }
    ]
  }
}

When the percolator verifies a percolator candidate match it is going to parse, perform query time text analysis and actually run the percolator query on the document being percolated. This is done for each candidate match and every time the percolate query executes. If your query time text analysis is relatively expensive part of query parsing then text analysis can become the dominating factor time is being spent on when percolating. This query parsing overhead can become noticeable when the percolator ends up verifying many candidate percolator query matches.

To avoid the most expensive part of text analysis at percolate time. One can choose to do the expensive part of text analysis when indexing the percolator query. This requires using two different analyzers. The first analyzer actually performs text analysis that needs be performed (expensive part). The second analyzer (usually whitespace) just splits the generated tokens that the first analyzer has produced. Then before indexing a percolator query, the analyze api should be used to analyze the query text with the more expensive analyzer. The result of the analyze api, the tokens, should be used to substitute the original query text in the percolator query. It is important that the query should now be configured to override the analyzer from the mapping and just the second analyzer. Most text based queries support an analyzer option (match, query_string, simple_query_string). Using this approach the expensive text analysis is performed once instead of many times.

Lets demonstrate this workflow via a simplified example.

Lets say we want to index the following percolator query:

{
  "query" : {
    "match" : {
      "body" : {
        "query" : "missing bicycles"
      }
    }
  }
}

with these settings and mapping:

PUT /test_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer" : {
          "tokenizer": "standard",
          "filter" : ["lowercase", "porter_stem"]
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "query" : {
        "type": "percolator"
      },
      "body" : {
        "type": "text",
        "analyzer": "my_analyzer" 
      }
    }
  }
}

First we need to use the analyze api to perform the text analysis prior to indexing:

POST /test_index/_analyze
{
  "analyzer" : "my_analyzer",
  "text" : "missing bicycles"
}

This results the following response:

{
  "tokens": [
    {
      "token": "miss",
      "start_offset": 0,
      "end_offset": 7,
      "type": "<ALPHANUM>",
      "position": 0
    },
    {
      "token": "bicycl",
      "start_offset": 8,
      "end_offset": 16,
      "type": "<ALPHANUM>",
      "position": 1
    }
  ]
}

All the tokens in the returned order need to replace the query text in the percolator query:

PUT /test_index/_doc/1?refresh
{
  "query" : {
    "match" : {
      "body" : {
        "query" : "miss bicycl",
        "analyzer" : "whitespace" 
      }
    }
  }
}

The analyze api prior to the indexing the percolator flow should be done for each percolator query.

At percolate time nothing changes and the percolate query can be defined normally:

GET /test_index/_search
{
  "query": {
    "percolate" : {
      "field" : "query",
      "document" : {
        "body" : "Bycicles are missing"
      }
    }
  }
}

This results in a response like this:

{
  "took": 6,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped" : 0,
    "failed": 0
  },
  "hits": {
    "total" : {
        "value": 1,
        "relation": "eq"
    },
    "max_score": 0.13076457,
    "hits": [
      {
        "_index": "test_index",
        "_type": "_doc",
        "_id": "1",
        "_score": 0.13076457,
        "_source": {
          "query": {
            "match": {
              "body": {
                "query": "miss bicycl",
                "analyzer": "whitespace"
              }
            }
          }
        },
        "fields" : {
          "_percolator_document_slot" : [0]
        }
      }
    ]
  }
}

Wildcard queries are more expensive than other queries for the percolator, especially if the wildcard expressions are large.

In the case of wildcard queries with prefix wildcard expressions or just the prefix query, the edge_ngram token filter can be used to replace these queries with regular term query on a field where the edge_ngram token filter is configured.

Creating an index with custom analysis settings:

PUT my_queries1
{
  "settings": {
    "analysis": {
      "analyzer": {
        "wildcard_prefix": { 
          "type": "custom",
          "tokenizer": "standard",
          "filter": [
            "lowercase",
            "wildcard_edge_ngram"
          ]
        }
      },
      "filter": {
        "wildcard_edge_ngram": { 
          "type": "edge_ngram",
          "min_gram": 1,
          "max_gram": 32
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "query": {
        "type": "percolator"
      },
      "my_field": {
        "type": "text",
        "fields": {
          "prefix": { 
            "type": "text",
            "analyzer": "wildcard_prefix",
            "search_analyzer": "standard"
          }
        }
      }
    }
  }
}

Then instead of indexing the following query:

{
  "query": {
    "wildcard": {
      "my_field": "abc*"
    }
  }
}

this query below should be indexed:

PUT /my_queries1/_doc/1?refresh
{
  "query": {
    "term": {
      "my_field.prefix": "abc"
    }
  }
}

This way can handle the second query more efficiently than the first query.

The following search request will match with the previously indexed percolator query:

GET /my_queries1/_search
{
  "query": {
    "percolate": {
      "field": "query",
      "document": {
        "my_field": "abcd"
      }
    }
  }
}

{
  "took": 6,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total" : {
        "value": 1,
        "relation": "eq"
    },
    "max_score": 0.18864399,
    "hits": [
      {
        "_index": "my_queries1",
        "_type": "_doc",
        "_id": "1",
        "_score": 0.18864399,
        "_source": {
          "query": {
            "term": {
              "my_field.prefix": "abc"
            }
          }
        },
        "fields": {
          "_percolator_document_slot": [
            0
          ]
        }
      }
    ]
  }
}

The same technique can also be used to speed up suffix wildcard searches. By using the reverse token filter before the edge_ngram token filter.

PUT my_queries2
{
  "settings": {
    "analysis": {
      "analyzer": {
        "wildcard_suffix": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": [
            "lowercase",
            "reverse",
            "wildcard_edge_ngram"
          ]
        },
        "wildcard_suffix_search_time": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": [
            "lowercase",
            "reverse"
          ]
        }
      },
      "filter": {
        "wildcard_edge_ngram": {
          "type": "edge_ngram",
          "min_gram": 1,
          "max_gram": 32
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "query": {
        "type": "percolator"
      },
      "my_field": {
        "type": "text",
        "fields": {
          "suffix": {
            "type": "text",
            "analyzer": "wildcard_suffix",
            "search_analyzer": "wildcard_suffix_search_time" 
          }
        }
      }
    }
  }
}

Then instead of indexing the following query:

{
  "query": {
    "wildcard": {
      "my_field": "*xyz"
    }
  }
}

the following query below should be indexed:

PUT /my_queries2/_doc/2?refresh
{
  "query": {
    "match": { 
      "my_field.suffix": "xyz"
    }
  }
}

The following search request will match with the previously indexed percolator query:

GET /my_queries2/_search
{
  "query": {
    "percolate": {
      "field": "query",
      "document": {
        "my_field": "wxyz"
      }
    }
  }
}

Percolate queries can be added to any index. Instead of adding percolate queries to the index the data resides in, these queries can also be added to a dedicated index. The advantage of this is that this dedicated percolator index can have its own index settings (For example the number of primary and replica shards). If you choose to have a dedicated percolate index, you need to make sure that the mappings from the normal index are also available on the percolate index. Otherwise percolate queries can be parsed incorrectly.

In certain cases it is unknown what kind of percolator queries do get registered, and if no field mapping exists for fields that are referred by percolator queries then adding a percolator query fails. This means the mapping needs to be updated to have the field with the appropriate settings, and then the percolator query can be added. But sometimes it is sufficient if all unmapped fields are handled as if these were default text fields. In those cases one can configure the index.percolator.map_unmapped_fields_as_text setting to true (default to false) and then if a field referred in a percolator query does not exist, it will be handled as a default text field so that adding the percolator query doesn’t fail.

Because the percolate query is processing one document at a time, it doesn’t support queries and filters that run against child documents such as has_child and has_parent.

There are a number of queries that fetch data via a get call during query parsing. For example the terms query when using terms lookup, template query when using indexed scripts and geo_shape when using pre-indexed shapes. When these queries are indexed by the percolator field type then the get call is executed once. So each time the percolator query evaluates these queries, the fetches terms, shapes etc. as the were upon index time will be used. Important to note is that fetching of terms that these queries do, happens both each time the percolator query gets indexed on both primary and replica shards, so the terms that are actually indexed can be different between shard copies, if the source index changed while indexing.

The script inside a script query can only access doc values fields. The percolate query indexes the provided document into an in-memory index. This in-memory index doesn’t support stored fields and because of that the _source field and other stored fields are not stored. This is the reason why in the script query the _source and other stored fields aren’t available.

Percolator queries that contain field aliases may not always behave as expected. In particular, if a percolator query is registered that contains a field alias, and then that alias is updated in the mappings to refer to a different field, the stored query will still refer to the original target field. To pick up the change to the field alias, the percolator query must be explicitly reindexed.