Date Histogram Aggregation

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 (both pre rounding, and post rounding) value, which will cause all computations to take the relevant zone into account. The time returned for each bucket/entry is milliseconds since the epoch of the provided time zone.

The parameters are pre_zone (pre rounding based on interval) and post_zone (post rounding based on interval). The time_zone parameter simply sets the pre_zone parameter. By default, those are set to UTC.

The zone value 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, with a pre_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 2012-03-31T00:00:00Z (UTC). For hour interval, applying the time zone results in 2012-03-31T20:15:30, rounding it results in 2012-03-31T20:00:00, but, we want to return it in UTC (post_zone is not set), so we convert it back to UTC: 2012-04-01T04:00:00Z. Note, we are consistent in the results, returning the rounded value in UTC.

post_zone simply takes the result, and adds the relevant offset.

Sometimes, we want to apply the same conversion to UTC we did above for hour also for day (and up) intervals. We can set pre_zone_adjust_large_interval to true, which will apply the same conversion done for hour interval in the example, to day and above intervals (it can be set regardless of the interval, but only kick in when using day and higher intervals).

Specific offsets can be provided for pre rounding and post rounding. The pre_offset for pre rounding, and post_offset for post rounding. The format is the date time format (1h, 1d, etc…​).

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:

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

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
                },
                ...
            ]
        }
    }
}

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 with min_doc_count > 0 will be 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).