The true power of aggregations becomes apparent once you start playing with different nesting schemes. In the previous examples, we saw how you could nest a metric inside a bucket, which is already quite powerful.
But the real exciting analytics come from nesting buckets inside other buckets. This time, we want to find out the distribution of car manufacturers for each color:
Notice that we can leave the previous
Another aggregation named
This aggregation is a
A few interesting things happened here. First, you’ll notice that the previous
avg_price metric is left entirely intact. Each level of an aggregation can
have many metrics or buckets. The
avg_price metric tells us the average price
for each car color. This is independent of other buckets and metrics that
are also being built.
This is important for your application, since there are often many related, but entirely distinct, metrics that you need to collect. Aggregations allow you to collect all of them in a single pass over the data.
The other important thing to note is that the aggregation we added,
terms bucket (nested inside the
terms bucket). This means we will
generate a (
make) tuple for every unique combination in your dataset.
Let’s take a look at the response (truncated for brevity, since it is now growing quite long):
Our new aggregation is nested under each color bucket, as expected.
We now see a breakdown of car makes for each color.
Finally, you can see that our previous
The response tells us the following:
- There are four red cars.
- The average price of a red car is $32,500.
- Three of the red cars are made by Honda, and one is a BMW.