Index a runtime field

Runtime fields are defined by the context where they run. For example, you can define runtime fields in the context of a search query or within the runtime section of an index mapping. If you decide to index a runtime field for greater performance, just move the full runtime field definition (including the script) to the context of an index mapping. Elasticsearch automatically uses these indexed fields to drive queries, resulting in a fast response time. This capability means you can write a script only once, and apply it to any context that supports runtime fields.

You can then use runtime fields to limit the number of fields that Elasticsearch needs to calculate values for. Using indexed fields in tandem with runtime fields provides flexibility in the data that you index and how you define queries for other fields.

For example, let’s say your company wants to replace some old pressure valves. The connected sensors are only capable of reporting a fraction of the true readings. Rather than outfit the pressure valves with new sensors, you decide to calculate the values based on reported readings. Based on the reported data, you define the following fields in your mapping for my-index-000001:

PUT my-index-000001/
{
  "mappings": {
    "properties": {
      "timestamp": {
        "type": "date"
      },
      "temperature": {
        "type": "long"
      },
      "voltage": {
        "type": "double"
      },
      "node": {
        "type": "keyword"
      }
    }
  }
}

You then bulk index some sample data from your sensors. This data includes voltage readings for each sensor:

POST my-index-000001/_bulk?refresh=true
{"index":{}}
{"timestamp": 1516729294000, "temperature": 200, "voltage": 5.2, "node": "a"}
{"index":{}}
{"timestamp": 1516642894000, "temperature": 201, "voltage": 5.8, "node": "b"}
{"index":{}}
{"timestamp": 1516556494000, "temperature": 202, "voltage": 5.1, "node": "a"}
{"index":{}}
{"timestamp": 1516470094000, "temperature": 198, "voltage": 5.6, "node": "b"}
{"index":{}}
{"timestamp": 1516383694000, "temperature": 200, "voltage": 4.2, "node": "c"}
{"index":{}}
{"timestamp": 1516297294000, "temperature": 202, "voltage": 4.0, "node": "c"}

After talking to a few site engineers, you realize that the sensors should be reporting at least double the current values, but potentially higher. You create a runtime field named voltage_corrected that retrieves the current voltage and multiplies it by 2:

PUT my-index-000001/_mapping
{
  "runtime": {
    "voltage_corrected": {
      "type": "double",
      "script": {
        "source": """
        emit(doc['voltage'].value * params['multiplier'])
        """,
        "params": {
          "multiplier": 2
        }
      }
    }
  }
}

You retrieve the calculated values using the fields parameter on the _search API:

GET my-index-000001/_search
{
  "fields": [
    "voltage_corrected",
    "node"
  ],
  "size": 2
}

After reviewing the sensor data and running some tests, you determine that the multiplier for reported sensor data should be 4. To gain greater performance, you decide to index the voltage_corrected runtime field with the new multiplier parameter.

In a new index named my-index-000001, copy the voltage_corrected runtime field definition into the mappings of the new index. It’s that simple! You can add an optional parameter named on_script_error that determines whether to reject the entire document if the script throws an error at index time (default).

PUT my-index-000001/
{
  "mappings": {
    "properties": {
      "timestamp": {
        "type": "date"
      },
      "temperature": {
        "type": "long"
      },
      "voltage": {
        "type": "double"
      },
      "node": {
        "type": "keyword"
      },
      "voltage_corrected": {
        "type": "double",
        "on_script_error": "fail", 
        "script": {
          "source": """
        emit(doc['voltage'].value * params['multiplier'])
        """,
          "params": {
            "multiplier": 4
          }
        }
      }
    }
  }
}

Bulk index some sample data from your sensors into the my-index-000001 index:

POST my-index-000001/_bulk?refresh=true
{ "index": {}}
{ "timestamp": 1516729294000, "temperature": 200, "voltage": 5.2, "node": "a"}
{ "index": {}}
{ "timestamp": 1516642894000, "temperature": 201, "voltage": 5.8, "node": "b"}
{ "index": {}}
{ "timestamp": 1516556494000, "temperature": 202, "voltage": 5.1, "node": "a"}
{ "index": {}}
{ "timestamp": 1516470094000, "temperature": 198, "voltage": 5.6, "node": "b"}
{ "index": {}}
{ "timestamp": 1516383694000, "temperature": 200, "voltage": 4.2, "node": "c"}
{ "index": {}}
{ "timestamp": 1516297294000, "temperature": 202, "voltage": 4.0, "node": "c"}

You can now retrieve calculated values in a search query, and find documents based on precise values. The following range query returns all documents where the calculated voltage_corrected is greater than or equal to 16, but less than or equal to 20. Again, use the fields parameter on the _search API to retrieve the fields you want:

POST my-index-000001/_search
{
  "query": {
    "range": {
      "voltage_corrected": {
        "gte": 16,
        "lte": 20,
        "boost": 1.0
      }
    }
  },
  "fields": ["voltage_corrected", "node"]
}

The response includes the voltage_corrected field for the documents that match the range query, based on the calculated value of the included script:

{
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "my-index-000001",
        "_id" : "yoSLrHgBdg9xpPrUZz_P",
        "_score" : 1.0,
        "_source" : {
          "timestamp" : 1516383694000,
          "temperature" : 200,
          "voltage" : 4.2,
          "node" : "c"
        },
        "fields" : {
          "voltage_corrected" : [
            16.8
          ],
          "node" : [
            "c"
          ]
        }
      },
      {
        "_index" : "my-index-000001",
        "_id" : "y4SLrHgBdg9xpPrUZz_P",
        "_score" : 1.0,
        "_source" : {
          "timestamp" : 1516297294000,
          "temperature" : 202,
          "voltage" : 4.0,
          "node" : "c"
        },
        "fields" : {
          "voltage_corrected" : [
            16.0
          ],
          "node" : [
            "c"
          ]
        }
      }
    ]
  }
}