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 withe 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 themsevles 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": 150,
"doc_count": 3
}
]
}
}
}```

The response above shows that none of the aggregated products has a price that falls within the range of `[100 - 150)`. By default, the response will only contain those buckets with a `doc_count` greater than 0. It is possible change that and request buckets with either a higher minimum count or even 0 (in which case elasticsearch will "fill in the gaps" and create buckets with zero documents). This can be configured using the `min_doc_count` setting:

```{
"aggs" : {
"prices" : {
"histogram" : {
"field" : "price",
"interval" : 50,
"min_doc_count" : 0
}
}
}
}```

Response:

```{
"aggregations": {
"prices" : {
"buckets": [
{
"key": 0,
"doc_count": 2
},
{
"key": 50,
"doc_count": 4
},
{
"key" : 100,
"doc_count" : 0
},
{
"key": 150,
"doc_count": 3
}
]
}
}
}```
 No documents were found that belong in this bucket, yet it is still returned with zero `doc_count`.

By default the date_/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 when requesting empty buckets (`"min_doc_count" : 0`), 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" : {
"filtered" : { "filter": { "range" : { "price" : { "to" : "500" } } } }
},
"aggs" : {
"prices" : {
"histogram" : {
"field" : "price",
"interval" : 50,
"min_doc_count" : 0,
"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 `{ "price_stats.min" : asc" }` will sort the buckets based on `min` value of their `price_stats` sub-aggregation. There is no need to configure the `price` field for the `price_stats` aggregation as it will inherit it by default from its parent histogram aggregation.

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

Minimum document countedit

It is possible to only return buckets that have a document count that is greater than or equal to a configured limit through the `min_doc_count` option.

```{
"aggs" : {
"prices" : {
"histogram" : {
"field" : "price",
"interval" : 50,
"min_doc_count": 10
}
}
}
}```

The above aggregation would only return buckets that contain 10 documents or more. Default value is `1`.

The special value `0` can be used to add empty buckets to the response between the minimum and the maximum buckets. Here is an example of what the response could look like:

```{
"aggregations": {
"prices": {
"buckets": {
"0": {
"key": 0,
"doc_count": 2
},
"50": {
"key": 50,
"doc_count": 0
},
"150": {
"key": 150,
"doc_count": 3
},
"200": {
"key": 150,
"doc_count": 0
},
"250": {
"key": 150,
"doc_count": 0
},
"300": {
"key": 150,
"doc_count": 1
}
}
}
}
}```

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
}
}
}
}
}```