## Histogram Aggregationedit

A multi-bucket values source based aggregation that can be applied on numeric values extracted from the documents.
It dynamically builds fixed size (a.k.a. interval) buckets over the values. For example, if the documents have a field
that holds a price (numeric), we can configure this aggregation to dynamically build buckets with interval `5`

(in case of price it may represent $5). When the aggregation executes, the price field of every document will be
evaluated and will be rounded down to its closest bucket - for example, if the price is `32`

and the bucket size is `5`

then the rounding will yield `30`

and thus the document will "fall" into the bucket that is associated with the key `30`

.
To make this more formal, here is the rounding function that is used:

rem = value % interval if (rem < 0) { rem += interval } bucket_key = value - rem

From the rounding function above it can be seen that the intervals themselves **must** be integers.

Currently, values are cast to integers before being bucketed, which
might cause negative floating-point values to fall into the wrong bucket. For
instance, `-4.5`

with an interval of `2`

would be cast to `-4`

, and so would
end up in the `-4 <= val < -2`

bucket instead of the `-6 <= val < -4`

bucket.

The following snippet "buckets" the products based on their `price`

by interval of `50`

:

{ "aggs" : { "prices" : { "histogram" : { "field" : "price", "interval" : 50 } } } }

And the following may be the response:

{ "aggregations": { "prices" : { "buckets": [ { "key": 0, "doc_count": 2 }, { "key": 50, "doc_count": 4 }, { "key": 100, "doc_count": 0 }, { "key": 150, "doc_count": 3 } ] } } }

### Minimum document countedit

The response above show that no documents has a price that falls within the range of `[100 - 150)`

. By default the
response will fill gaps in the histogram with empty buckets. It is possible change that and request buckets with
a higher minimum count thanks to the `min_doc_count`

setting:

{ "aggs" : { "prices" : { "histogram" : { "field" : "price", "interval" : 50, "min_doc_count" : 1 } } } }

Response:

{ "aggregations": { "prices" : { "buckets": [ { "key": 0, "doc_count": 2 }, { "key": 50, "doc_count": 4 }, { "key": 150, "doc_count": 3 } ] } } }

By default the `histogram`

returns all the buckets within the range of the data itself, that is, the documents with
the smallest values (on which with histogram) will determine the min bucket (the bucket with the smallest key) and the
documents with the highest values will determine the max bucket (the bucket with the highest key). Often, when
requesting empty buckets, this causes a confusion, specifically, when the data is also filtered.

To understand why, let’s look at an example:

Lets say the you’re filtering your request to get all docs with values between `0`

and `500`

, in addition you’d like
to slice the data per price using a histogram with an interval of `50`

. You also specify `"min_doc_count" : 0`

as you’d
like to get all buckets even the empty ones. If it happens that all products (documents) have prices higher than `100`

,
the first bucket you’ll get will be the one with `100`

as its key. This is confusing, as many times, you’d also like
to get those buckets between `0 - 100`

.

With `extended_bounds`

setting, you now can "force" the histogram aggregation to start building buckets on a specific
`min`

values and also keep on building buckets up to a `max`

value (even if there are no documents anymore). Using
`extended_bounds`

only makes sense when `min_doc_count`

is 0 (the empty buckets will never be returned if `min_doc_count`

is greater than 0).

Note that (as the name suggest) `extended_bounds`

is **not** filtering buckets. Meaning, if the `extended_bounds.min`

is higher
than the values extracted from the documents, the documents will still dictate what the first bucket will be (and the
same goes for the `extended_bounds.max`

and the last bucket). For filtering buckets, one should nest the histogram aggregation
under a range `filter`

aggregation with the appropriate `from`

/`to`

settings.

Example:

{ "query" : { "constant_score" : { "filter": { "range" : { "price" : { "to" : "500" } } } } }, "aggs" : { "prices" : { "histogram" : { "field" : "price", "interval" : 50, "extended_bounds" : { "min" : 0, "max" : 500 } } } } }

### Orderedit

By default the returned buckets are sorted by their `key`

ascending, though the order behaviour can be controlled
using the `order`

setting.

Ordering the buckets by their key - descending:

{ "aggs" : { "prices" : { "histogram" : { "field" : "price", "interval" : 50, "order" : { "_key" : "desc" } } } } }

Ordering the buckets by their `doc_count`

- ascending:

{ "aggs" : { "prices" : { "histogram" : { "field" : "price", "interval" : 50, "order" : { "_count" : "asc" } } } } }

If the histogram aggregation has a direct metrics sub-aggregation, the latter can determine the order of the buckets:

{ "aggs" : { "prices" : { "histogram" : { "field" : "price", "interval" : 50, "order" : { "price_stats.min" : "asc" } }, "aggs" : { "price_stats" : { "stats" : {} } } } } }

The | |

There is no need to configure the |

It is also possible to order the buckets based on a "deeper" aggregation in the hierarchy. This is supported as long
as the aggregations path are of a single-bucket type, where the last aggregation in the path may either by a single-bucket
one or a metrics one. If it’s a single-bucket type, the order will be defined by the number of docs in the bucket (i.e. `doc_count`

),
in case it’s a metrics one, the same rules as above apply (where the path must indicate the metric name to sort by in case of
a multi-value metrics aggregation, and in case of a single-value metrics aggregation the sort will be applied on that value).

The path must be defined in the following form:

AGG_SEPARATOR := '>' METRIC_SEPARATOR := '.' AGG_NAME := <the name of the aggregation> METRIC := <the name of the metric (in case of multi-value metrics aggregation)> PATH := <AGG_NAME>[<AGG_SEPARATOR><AGG_NAME>]*[<METRIC_SEPARATOR><METRIC>]

{ "aggs" : { "prices" : { "histogram" : { "field" : "price", "interval" : 50, "order" : { "promoted_products>rating_stats.avg" : "desc" } }, "aggs" : { "promoted_products" : { "filter" : { "term" : { "promoted" : true }}, "aggs" : { "rating_stats" : { "stats" : { "field" : "rating" }} } } } } } }

The above will sort the buckets based on the avg rating among the promoted products

### Offsetedit

By default the bucket keys start with 0 and then continue in even spaced steps of `interval`

, e.g. if the interval is 10 the first buckets
(assuming there is data inside them) will be [0 - 9], [10-19], [20-29]. The bucket boundaries can be shifted by using the `offset`

option.

This can be best illustrated with an example. If there are 10 documents with values ranging from 5 to 14, using interval `10`

will result in
two buckets with 5 documents each. If an additional offset `5`

is used, there will be only one single bucket [5-14] containing all the 10
documents.

### Response Formatedit

By default, the buckets are returned as an ordered array. It is also possible to request the response as a hash instead keyed by the buckets keys:

{ "aggs" : { "prices" : { "histogram" : { "field" : "price", "interval" : 50, "keyed" : true } } } }

Response:

{ "aggregations": { "prices": { "buckets": { "0": { "key": 0, "doc_count": 2 }, "50": { "key": 50, "doc_count": 4 }, "150": { "key": 150, "doc_count": 3 } } } } }

### Missing valueedit

The `missing`

parameter defines how documents that are missing a value should be treated.
By default they will be ignored but it is also possible to treat them as if they
had a value.