Oversample processor

The oversample request processor multiplies the size parameter of the search request by a specified sample_factor (>= 1.0), saving the original value in the original_size pipeline variable. The oversample processor is designed to work with the truncate_hits response processor but may be used on its own.

Request fields

The following table lists all request fields.

Field	Data type	Description
sample_factor	Float	The multiplicative factor (>= 1.0) that will be applied to the size parameter before processing the search request. Required.
context_prefix	String	May be used to scope the original_size variable in order to avoid collisions. Optional.
tag	String	The processor's identifier. Optional.
description	String	A description of the processor. Optional.
ignore_failure	Boolean	If true, Lucenia ignores any failure of this processor and continues to run the remaining processors in the search pipeline. Optional. Default is false.

Example

The following example demonstrates using a search pipeline with an oversample processor.

Setup

Create an index named my_index containing many documents:

POST /_bulk
{ "create":{"_index":"my_index","_id":1}}
{ "doc": { "title" : "document 1" }}
{ "create":{"_index":"my_index","_id":2}}
{ "doc": { "title" : "document 2" }}
{ "create":{"_index":"my_index","_id":3}}
{ "doc": { "title" : "document 3" }}
{ "create":{"_index":"my_index","_id":4}}
{ "doc": { "title" : "document 4" }}
{ "create":{"_index":"my_index","_id":5}}
{ "doc": { "title" : "document 5" }}
{ "create":{"_index":"my_index","_id":6}}
{ "doc": { "title" : "document 6" }}
{ "create":{"_index":"my_index","_id":7}}
{ "doc": { "title" : "document 7" }}
{ "create":{"_index":"my_index","_id":8}}
{ "doc": { "title" : "document 8" }}
{ "create":{"_index":"my_index","_id":9}}
{ "doc": { "title" : "document 9" }}
{ "create":{"_index":"my_index","_id":10}}
{ "doc": { "title" : "document 10" }}

Creating a search pipeline

The following request creates a search pipeline named my_pipeline with an oversample request processor that requests 50% more hits than specified in size:

PUT /_search/pipeline/my_pipeline 
{
  "request_processors": [
    {
      "oversample" : {
        "tag" : "oversample_1",
        "description" : "This processor will multiply `size` by 1.5.",
        "sample_factor" : 1.5
      }
    }
  ]
}

Using a search pipeline

Search for documents in my_index without a search pipeline:

POST /my_index/_search
{
  "size": 5
}

The response contains five hits:

Response

{
  "took" : 3,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 10,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "my_index",
        "_id" : "1",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 1"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "2",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 2"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "3",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 3"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "4",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 4"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "5",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 5"
          }
        }
      }
    ]
  }
}

To search with a pipeline, specify the pipeline name in the search_pipeline query parameter:

POST /my_index/_search?search_pipeline=my_pipeline
{
  "size": 5
}

The response contains 8 documents (5 * 1.5 = 7.5, rounded up to 8):

Response

{
  "took" : 13,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 10,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "my_index",
        "_id" : "1",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 1"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "2",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 2"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "3",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 3"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "4",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 4"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "5",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 5"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "6",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 6"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "7",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 7"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "8",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 8"
          }
        }
      }
    ]
  }
}