elasticsearch/docs/reference/aggregations/bucket/datehistogram-aggregation.asciidoc

[[search-aggregations-bucket-datehistogram-aggregation]]
=== Date Histogram Aggregation

A multi-bucket aggregation similar to the <<search-aggregations-bucket-histogram-aggregation,histogram>> except it can
only be applied on date values. Since dates are represented in elasticsearch internally as long values, it is possible
to use the normal `histogram` on dates as well, though accuracy will be compromised. The reason for this is in the fact
that time based intervals are not fixed (think of leap years and on the number of days in a month). For this reason,
we need special support for time based data. From a functionality perspective, this histogram supports the same features
as the normal <<search-aggregations-bucket-histogram-aggregation,histogram>>. The main difference is that the interval can be specified by date/time expressions.

Requesting bucket intervals of a month.

[source,js]
--------------------------------------------------
{
    "aggs" : {
        "articles_over_time" : {
            "date_histogram" : {
                "field" : "date",
                "interval" : "month"
            }
        }
    }
}
--------------------------------------------------

Available expressions for interval: `year`, `quarter`, `month`, `week`, `day`, `hour`, `minute`, `second`


Fractional values are allowed for seconds, minutes, hours, days and weeks. For example 1.5 hours:

[source,js]
--------------------------------------------------
{
    "aggs" : {
        "articles_over_time" : {
            "date_histogram" : {
                "field" : "date",
                "interval" : "1.5h"
            }
        }
    }
}
--------------------------------------------------

See <<time-units>> for accepted abbreviations.

==== Time Zone

By default, times are stored as UTC milliseconds since the epoch. Thus, all computation and "bucketing" / "rounding" is
done on UTC. It is possible to provide a time zone value, which will cause all bucket
computations to take place in the specified zone. The time returned for each bucket/entry is milliseconds since the
epoch in UTC. The parameters is called `time_zone`. It accepts either a numeric value for the hours offset, for example:
`"time_zone" : -2`. It also accepts a format of hours and minutes, like `"time_zone" : "-02:30"`.
Another option is to provide a time zone accepted as one of the values listed here.

Lets take an example. For `2012-04-01T04:15:30Z` (UTC), with a `time_zone` of `"-08:00"`. For day interval, the actual time by
applying the time zone and rounding falls under `2012-03-31`, so the returned value will be (in millis) of
`2012-03-31T08:00:00Z` (UTC). For hour interval, internally applying the time zone results in `2012-03-31T20:15:30`, so rounding it
in the time zone results in `2012-03-31T20:00:00`, but we return that rounded value converted back in UTC so be consistent as
`2012-04-01T04:00:00Z` (UTC).

==== Offset

The `offset` option can be provided for shifting the date bucket intervals boundaries after any other shifts because of
time zones are applies. This for example makes it possible that daily buckets go from 6AM to 6AM the next day instead of starting at 12AM
or that monthly buckets go from the 10th of the month to the 10th of the next month instead of the 1st.

The `offset` option accepts positive or negative time durations like "1h" for an hour or "1M" for a Month. See <<time-units>> for more
possible time duration options.

==== Keys

Since internally, dates are represented as 64bit numbers, these numbers are returned as the bucket keys (each key
representing a date - milliseconds since the epoch). It is also possible to define a date format, which will result in
returning the dates as formatted strings next to the numeric key values:

[source,js]
--------------------------------------------------
{
    "aggs" : {
        "articles_over_time" : {
            "date_histogram" : {
                "field" : "date",
                "interval" : "1M",
                "format" : "yyyy-MM-dd" <1>
            }
        }
    }
}
--------------------------------------------------

<1> Supports expressive date <<date-format-pattern,format pattern>>

Response:

[source,js]
--------------------------------------------------
{
    "aggregations": {
        "articles_over_time": {
            "buckets": [
                {
                    "key_as_string": "2013-02-02",
                    "key": 1328140800000,
                    "doc_count": 1
                },
                {
                    "key_as_string": "2013-03-02",
                    "key": 1330646400000,
                    "doc_count": 2
                },
                ...
            ]
        }
    }
}
--------------------------------------------------

Like with the normal <<search-aggregations-bucket-histogram-aggregation,histogram>>, both document level scripts and
value level scripts are supported. It is also possible to control the order of the returned buckets using the `order`
settings and filter the returned buckets based on a `min_doc_count` setting (by default all buckets between the first
bucket that matches documents and the last one are returned). This histogram also supports the `extended_bounds`
setting, which enables extending the bounds of the histogram beyond the data itself (to read more on why you'd want to
do that please refer to the explanation <<search-aggregations-bucket-histogram-aggregation-extended-bounds,here>>).

==== Missing value

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.

[source,js]
--------------------------------------------------
{
    "aggs" : {
        "publish_date" : {
             "datehistogram" : {
                 "field" : "publish_date",
                 "interval": "year",
                 "missing": "2000-01-01" <1>
             }
         }
    }
}
--------------------------------------------------

<1> Documents without a value in the `publish_date` field will fall into the same bucket as documents that have the value `2000-01-01`.