## Adding a Metric to the Mixedit

The previous example told us the number of documents in each bucket, which is useful. But often, our applications require more-sophisticated metrics about the documents. For example, what is the average price of cars in each bucket?

To get this information, we need to tell Elasticsearch which metrics to calculate,
and on which fields.
This requires *nesting* metrics inside the buckets.
Metrics will calculate mathematical statistics based on the values of documents
within a bucket.

Let’s go ahead and add an `average`

metric to our car example:

GET /cars/transactions/_search { "size" : 0, "aggs": { "colors": { "terms": { "field": "color.keyword" }, "aggs": { "avg_price": { "avg": { "field": "price" } } } } } }

We add a new | |

We then give the metric a name: | |

And finally, we define it as an |

As you can see, we took the previous example and tacked on a new `aggs`

level.
This new aggregation level allows us to nest the `avg`

metric inside the
`terms`

bucket. Effectively, this means we will generate an average for each
color.

Just like the `colors`

example, we need to name our metric (`avg_price`

) so we
can retrieve the values later. Finally, we specify the metric itself (`avg`

)
and what field we want the average to be calculated on (`price`

):

{ ... "aggregations": { "colors": { ... "buckets": [ { "key": "red", "doc_count": 4, "avg_price": { "value": 32500 } }, { "key": "blue", "doc_count": 2, "avg_price": { "value": 20000 } }, { "key": "green", "doc_count": 2, "avg_price": { "value": 21000 } } ] } } ... }

Although the response has changed minimally, the data we get out of it has grown substantially. Before, we knew there were four red cars. Now we know that the average price of red cars is $32,500. This is something that you can plug directly into reports or graphs.