Metric aggregation map context

Serverless Stack

Use a Painless script to map values for use in a scripted metric aggregation. A map script is run once per collected document following an optional initialization script and is required as part of a full metric aggregation.

params (Map, read-only)

User-defined parameters passed in as part of the query.

state (Map)

Map used to add values for processing in a combine script or to be returned from the aggregation.

doc (Map, read-only)

Contains the fields of the current document where each field is a List of values.

_score (double read-only)

The similarity score of the current document.

state (Map)

Use this Map to add values for processing in a combine script. Additional values must be of the type Map, List, String or primitive. The same state Map is shared between all aggregated documents on a given shard. If an initialization script is provided as part of the aggregation then values added from the initialization script are available. If no combine script is specified, values must be directly stored in state in a usable form. If no combine script and no reduce script are specified, the state values are used as the result.

void

No expected return value.

The standard Painless API is available.

To run the example, first install the eCommerce sample data.

In the following example, we build a query that analyzes the data to calculate the total number of products sold across all orders, using the map-reduce pattern where each shard processes documents locally and results are combined into a final total.

Initialization phase (sets up data structures):
The first code snippet is part of the init_script that initializes an empty array to collect quantity values from each document. It runs once per shard.

state.quantities = []
		

>Map phase (this context - processes each document):
The code in the map_script section runs for each document. It extracts the total quantity of products in each order and adds it to the shard's collection array.

state.quantities.add(doc['total_quantity'].value)
		

Combine phase (returns shard results):
The combine_script processes all the quantities collected in this shard by iterating through the array and summing all values. This reduces the data sent to the reduce phase from an array of individual quantities to a single total per shard.

int shardTotal = 0;

for (qty in state.quantities) {
  shardTotal += qty;
}

return shardTotal;
		

Reduce phase (merges all shard results):
Finally, the reduce_script merges results from all shards by iterating through each shard's total, and adds the results together to get the grand total of products sold across the entire dataset.

int grandTotal = 0;

for (shardTotal in states) {
  grandTotal += shardTotal;
}

return grandTotal;
		

The complete request looks like this:

GET kibana_sample_data_ecommerce/_search
{
  "size": 0,
  "aggs": {
	"total_quantity_sold": {
  	"scripted_metric": {
    	"init_script": "state.quantities = []",
    	"map_script": "state.quantities.add(doc['total_quantity'].value)",
    	"combine_script": """
          int shardTotal = 0;

          for (qty in state.quantities) {
            shardTotal += qty;
          }

          return shardTotal;
        """,
    	"reduce_script": """
          int grandTotal = 0;

          for (shardTotal in states) {
            grandTotal += shardTotal;
          }

          return grandTotal;
        """
      }
    }
  }
}