---
title: Metric aggregation combine context
description: Use a Painless script to combine values for use in a scripted metric aggregation. A combine script is run once per shard following a map script and is...
url: https://www.elastic.co/docs/reference/scripting-languages/painless/painless-metric-agg-combine-context
products:
  - Elasticsearch
  - Painless
applies_to:
  - Elastic Cloud Serverless: Generally available
  - Elastic Stack: Generally available
---

# Metric aggregation combine context
Use a Painless script to [combine](https://www.elastic.co/docs/reference/aggregations/search-aggregations-metrics-scripted-metric-aggregation) values for use in a scripted metric aggregation. A combine script is run once per shard following a [map script](https://www.elastic.co/docs/reference/scripting-languages/painless/painless-metric-agg-map-context) and is optional as part of a full metric aggregation.
<warning>
  `scripted_metric` is not available in Elastic Cloud Serverless.
</warning>


## Variables

<definitions>
  <definition term="params (Map, read-only)">
    User-defined parameters passed in as part of the query.
  </definition>
  <definition term="state (Map)">
    `Map` with values available from the prior map script.
  </definition>
</definitions>


## Return

<definitions>
  <definition term="List, Map, String, or primitive">
    A value collected for use in a [reduce script](https://www.elastic.co/docs/reference/scripting-languages/painless/painless-metric-agg-reduce-context). If no reduce script is specified, the value is used as part of the result.
  </definition>
</definitions>


## API

The standard [Painless API](https://www.elastic.co/guide/en/elasticsearch/painless/current/painless-api-reference-shared.html) is available.

## Example

To run the example, first [install the eCommerce sample data](/docs/reference/scripting-languages/painless/painless-context-examples#painless-sample-data-install).
<tip>
</tip>

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.
```java
state.quantities = []
```

**Map phase (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.
```java
state.quantities.add(doc['total_quantity'].value)
```

**>Combine phase (this context - 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.
```java
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.
```java
int grandTotal = 0;
 
for (shardTotal in states) {
  grandTotal += shardTotal;
}
 
return grandTotal;
```

The complete request looks like this:
```json
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;
        """
      }
    }
  }
}
```