Our Elasticsearch stack has evolved, so we are not actually using this component anymore. Therefore it has been deprecated and is no longer actively maintained by Meltwater.

The batch percolator is a fork of the official elasticsearch percolator. It's highly optimized for large volume percolation with complex Lucene queries like wildcards, spans and phrases.

Using the official multi percolator we were able to reach ~1 document/second with 100.000 registered queries. With the batch-percolator, we are currently handling ~1000 documents/second with 225.000 registered queries. However, this will differ greatly depending on the nature of you queries and if you have an efficient strategy for filtering out queries.

For more information, see this blog post.

elasticsearch/bin/plugin --install elasticsearch-batch-percolator -u "https://dl.bintray.com/meltwater/elasticsearch-batch-percolator/com/meltwater/elasticsearch-batch-percolator/1.1.2/elasticsearch-batch-percolator-1.1.2.zip"

Version matrix:

┌─────────────────────────────────────────┬──────────────────────────┐
│ Elasticsearch batch percolator          │ ElasticSearch            │
├─────────────────────────────────────────┼──────────────────────────┤
│ 1.x.x                                   │ 1.7.0 ─► 1.7.6           │
└─────────────────────────────────────────┴──────────────────────────┘

curl -XPUT localhost:9200/index -d '{
  "mappings": {
    "type": {
      "_source": {
        "enabled": false
      },
      "properties": {
        "field1": {
          "type": "string",
          "index": "analyzed"
        },
        "field2": {
          "type": "string",
          "index": "analyzed"
        }
      }
    }
  }
}'

You could also use a template, or store a document to get dynamic mapping for the document type to percolate agains

curl -XPOST localhost:9200/index/.batchpercolator/query1 -d '{
  "query": {
    "term": {
      "field1": "fox"
    }
  },
  "highlight": {
    "pre_tags": [
      "<b>"
    ],
    "post_tags": [
      "</b>"
    ],
    "fields": {
      "field1": {}
    }
  }
}'

curl -XPOST localhost:9200/index/type/_batchpercolate -d '{
  "docs": [
    {
      "_id": "doc1",
      "field1": "the fox is here",
      "field2": "meltwater"
    },
    {
      "_id": "doc2",
      "field1": "the fox is not here",
      "field2": "percolator"
    }
  ]
}'

example response:

{
  "took": 23,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "results": [
    {
      "doc": "doc1",
      "matches": [
        {
          "query_id": "query1",
          "highlights": {
            "field1": [
              "the <b>fox</b> is here"
            ]
          }
        }
      ]
    }
  ]
}

The official multi-percolator uses a 'MemoryIndex' which is a highly optimized index often used for percolation. The downside with the MemoryIndex is that it can only hold one document at a time.

The batch percolator instead uses a RamDirectory which means that we can process documents in batches.

Complex queries like Span, Phrase and especially MultiPhraseQueries are magnitudes slower than Term or Boolean queries. All complex queries can be approximated using cheaper queries (for example, a SpanNear can be approximated using an AND query).

In the batch-percolator, a simplified approximation of each query is first executed on the batch of documents. We only execute the original expensive query if the approximated query has any matches in the batch. This is similar to how Lucene 5 executes those queries, and we expect to phase out this step once Elasticsearch 2.0 has a stable release.

We've removed a lot of features from the official multi-percolator. This means that you can no longer use filter queries or aggregations on matching queries. You should consider this plugin to be 'vanilla percolation'. Some of the features were removed because they cannot be supported in batch-mode. Some have been removed to reduce the complexity of the code.