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Elastic Docs ›Elasticsearch Guide [2.4] ›Aggregations ›Bucket Aggregations

Date Histogram Aggregation

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Date Histogram Aggregation

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A multi-bucket aggregation similar to the 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 histogram. The main difference is that the interval can be specified by date/time expressions.

Requesting bucket intervals of a month.

{
    "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:

{
    "aggs" : {
        "articles_over_time" : {
            "date_histogram" : {
                "field" : "date",
                "interval" : "1.5h"
            }
        }
    }
}

See Time units for accepted abbreviations.

Keys

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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 keys. The key_as_string is the same timestamp converted to a formatted date string using the format specified with the format parameter:

If no format is specified, then it will use the first date format specified in the field mapping.

{
    "aggs" : {
        "articles_over_time" : {
            "date_histogram" : {
                "field" : "date",
                "interval" : "1M",
                "format" : "yyyy-MM-dd" 
            }
        }
    }
}

Supports expressive date format pattern

Response:

{
    "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

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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:

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:

"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:

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:

"aggregations": {
  "by_day": {
    "buckets": [
      {
        "key_as_string": "2015-09-30T00:00:00.000-01:00", 
        "key": 1443571200000,
        "doc_count": 1
      },
      {
        "key_as_string": "2015-10-01T00:00:00.000-01:00", 
        "key": 1443657600000,
        "doc_count": 1
      }
    ]
  }
}

The key_as_string value represents midnight on each day in the specified time zone.

Offset

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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:

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:

"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
      }
    ]
  }
}

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

Scripts

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Like with the normal 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 here).

Missing value

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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.

{
    "aggs" : {
        "publish_date" : {
             "date_histogram" : {
                 "field" : "publish_date",
                 "interval": "year",
                 "missing": "2000-01-01" 
             }
         }
    }
}

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