OpenSearch/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.

==== Keys

Internally, a date is represented as a 64 bit number representing a timestamp
in milliseconds-since-the-epoch. These timestamps are returned as the bucket
++key++s. The `key_as_string` is the same timestamp converted to a formatted
date string using the format specified with the `format` parameter:

TIP: If no `format` is specified, then it will use the first date
<<mapping-date-format,format>> specified in the field mapping.

[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
                },
                ...
            ]
        }
    }
}
--------------------------------------------------

==== Time Zone

Date-times are stored in Elasticsearch in UTC.  By default, all bucketing and
rounding is also done in UTC. The `time_zone` parameter can be used to indicate
that bucketing should use a different time zone.

Time zones may either be specified as an ISO 8601 UTC offset (e.g. `+01:00` or
`-08:00`)  or as a timezone id, an identifier used in the TZ database like
`America/Los_Angeles`.

Consider the following example:

[source,js]
---------------------------------
PUT my_index/log/1
{
  "date": "2015-10-01T00:30:00Z"
}

PUT my_index/log/2
{
  "date": "2015-10-01T01:30:00Z"
}

GET my_index/_search?size=0
{
  "aggs": {
    "by_day": {
      "date_histogram": {
        "field":     "date",
        "interval":  "day"
      }
    }
  }
}
---------------------------------

UTC is used if no time zone is specified, which would result in both of these
documents being placed into the same day bucket, which starts at midnight UTC
on 1 October 2015:

[source,js]
---------------------------------
"aggregations": {
  "by_day": {
    "buckets": [
      {
        "key_as_string": "2015-10-01T00:00:00.000Z",
        "key":           1443657600000,
        "doc_count":     2
      }
    ]
  }
}
---------------------------------

If a `time_zone` of `-01:00` is specified, then midnight starts at one hour before
midnight UTC:

[source,js]
---------------------------------
GET my_index/_search?size=0
{
  "aggs": {
    "by_day": {
      "date_histogram": {
        "field":     "date",
        "interval":  "day",
        "time_zone": "-01:00"
      }
    }
  }
}
---------------------------------

Now the first document falls into the bucket for 30 September 2015, while the
second document falls into the bucket for 1 October 2015:

[source,js]
---------------------------------
"aggregations": {
  "by_day": {
    "buckets": [
      {
        "key_as_string": "2015-09-30T00:00:00.000-01:00", <1>
        "key": 1443571200000,
        "doc_count": 1
      },
      {
        "key_as_string": "2015-10-01T00:00:00.000-01:00", <1>
        "key": 1443657600000,
        "doc_count": 1
      }
    ]
  }
}
---------------------------------
<1> The `key_as_string` value represents midnight on each day
    in the specified time zone.

==== Offset

The `offset` parameter is used to change the start value of each bucket by the
specified positive (`+`) or negative offset (`-`) duration, such as `1h` for
an hour, or `1M` for a month. See <<time-units>> for more possible time
duration options.

For instance, when using an interval of `day`, each bucket runs from midnight
to midnight.  Setting the `offset` parameter to `+6h` would change each bucket
to run from 6am to 6am:

[source,js]
-----------------------------
PUT my_index/log/1
{
  "date": "2015-10-01T05:30:00Z"
}

PUT my_index/log/2
{
  "date": "2015-10-01T06:30:00Z"
}

GET my_index/_search?size=0
{
  "aggs": {
    "by_day": {
      "date_histogram": {
        "field":     "date",
        "interval":  "day",
        "offset":    "+6h"
      }
    }
  }
}
-----------------------------

Instead of a single bucket starting at midnight, the above request groups the
documents into buckets starting at 6am:

[source,js]
-----------------------------
"aggregations": {
  "by_day": {
    "buckets": [
      {
        "key_as_string": "2015-09-30T06:00:00.000Z",
        "key": 1443592800000,
        "doc_count": 1
      },
      {
        "key_as_string": "2015-10-01T06:00:00.000Z",
        "key": 1443679200000,
        "doc_count": 1
      }
    ]
  }
}
-----------------------------

NOTE: The start `offset` of each bucket is calculated after the `time_zone`
adjustments have been made.

==== Scripts

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" : {
             "date_histogram" : {
                 "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`.