This multi-bucket aggregation is similar to the normal
histogram, but it can
only be used with date values. Because dates are represented internally in
Elasticsearch as long values, it is possible, but not as accurate, to use the
normal histogram
on dates as well. The main difference in the two APIs is
that here the interval can be specified using date/time expressions. Time-based
data requires special support because time-based intervals are not always a
fixed length.
There seems to be no limit to the creativity we humans apply to setting our clocks and calendars. We’ve invented leap years and leap seconds, standard and daylight savings times, and timezone offsets of 30 or 45 minutes rather than a full hour. While these creations help keep us in sync with the cosmos and our environment, they can make specifying time intervals accurately a real challenge. The only universal truth our researchers have yet to disprove is that a millisecond is always the same duration, and a second is always 1000 milliseconds. Beyond that, things get complicated.
Generally speaking, when you specify a single time unit, such as 1 hour or 1 day, you are working with a calendar interval, but multiples, such as 6 hours or 3 days, are fixed-length intervals.
For example, a specification of 1 day (1d) from now is a calendar interval that means "at this exact time tomorrow" no matter the length of the day. A change to or from daylight savings time that results in a 23 or 25 hour day is compensated for and the specification of "this exact time tomorrow" is maintained. But if you specify 2 or more days, each day must be of the same fixed duration (24 hours). In this case, if the specified interval includes the change to or from daylight savings time, the interval will end an hour sooner or later than you expect.
There are similar differences to consider when you specify single versus multiple minutes or hours. Multiple time periods longer than a day are not supported.
Here are the valid time specifications and their meanings:
All minutes begin at 00 seconds.
All hours begin at 00 minutes and 00 seconds.
All days begin at the earliest possible time, which is usually 00:00:00 (midnight).
NOTE: In all cases, when the specified end time does not exist, the actual end time is the closest available time after the specified end.
Widely distributed applications must also consider vagaries such as countries that start and stop daylight savings time at 12:01 A.M., so end up with one minute of Sunday followed by an additional 59 minutes of Saturday once a year, and countries that decide to move across the international date line. Situations like that can make irregular timezone offsets seem easy.
As always, rigorous testing, especially around time-change events, will ensure that your time interval specification is what you intend it to be.
WARNING: To avoid unexpected results, all connected servers and clients must sync to a reliable network time service.
Requesting bucket intervals of a month.
POST /sales/_search?size=0 { "aggs" : { "sales_over_time" : { "date_histogram" : { "field" : "date", "interval" : "month" } } } }
You can also specify time values using abbreviations supported by
time units parsing.
Note that fractional time values are not supported, but you can address this by
shifting to another
time unit (e.g., 1.5h
could instead be specified as 90m
).
POST /sales/_search?size=0 { "aggs" : { "sales_over_time" : { "date_histogram" : { "field" : "date", "interval" : "90m" } } } }
Internally, a date is represented as a 64 bit number representing a timestamp
in milliseconds-since-the-epoch (01/01/1970 midnight UTC). These timestamps are
returned as the key
name of the bucket. The key_as_string
is the same
timestamp converted to a formatted
date string using the format
parameter specification:
If you don’t specify format
, the first date
format specified in the field mapping is used.
POST /sales/_search?size=0 { "aggs" : { "sales_over_time" : { "date_histogram" : { "field" : "date", "interval" : "1M", "format" : "yyyy-MM-dd" } } } }
Supports expressive date format pattern |
Response:
{ ... "aggregations": { "sales_over_time": { "buckets": [ { "key_as_string": "2015-01-01", "key": 1420070400000, "doc_count": 3 }, { "key_as_string": "2015-02-01", "key": 1422748800000, "doc_count": 2 }, { "key_as_string": "2015-03-01", "key": 1425168000000, "doc_count": 2 } ] } } }
Date-times are stored in Elasticsearch in UTC. By default, all bucketing and
rounding is also done in UTC. Use the time_zone
parameter to indicate
that bucketing should use a different timezone.
You can specify timezones as either an ISO 8601 UTC offset (e.g. +01:00
or
-08:00
) or as a timezone ID as specified in the IANA timezone database,
such as`America/Los_Angeles`.
Consider the following example:
PUT my_index/_doc/1?refresh { "date": "2015-10-01T00:30:00Z" } PUT my_index/_doc/2?refresh { "date": "2015-10-01T01:30:00Z" } GET my_index/_search?size=0 { "aggs": { "by_day": { "date_histogram": { "field": "date", "interval": "day" } } } }
If you don’t specify a timezone, UTC is used. This 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 you specify a time_zone
of -01:00
, midnight in that timezone is 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": 1443574800000, "doc_count": 1 }, { "key_as_string": "2015-10-01T00:00:00.000-01:00", "key": 1443661200000, "doc_count": 1 } ] } } }
When using time zones that follow DST (daylight savings time) changes,
buckets close to the moment when those changes happen can have slightly different
sizes than you would expect from the used interval
.
For example, consider a DST start in the CET
time zone: on 27 March 2016 at 2am,
clocks were turned forward 1 hour to 3am local time. If you use day
as interval
,
the bucket covering that day will only hold data for 23 hours instead of the usual
24 hours for other buckets. The same is true for shorter intervals, like 12h,
where you’ll have only a 11h bucket on the morning of 27 March when the DST shift
happens.
Use the offset
parameter to change the start value of each bucket by the
specified positive (+
) or negative offset (-
) duration, such as 1h
for
an hour, or 1d
for a day. See Time units for more possible time
duration options.
For example, when using an interval of day
, each bucket runs from midnight
to midnight. Setting the offset
parameter to +6h
changes each bucket
to run from 6am to 6am:
PUT my_index/_doc/1?refresh { "date": "2015-10-01T05:30:00Z" } PUT my_index/_doc/2?refresh { "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 time_zone
adjustments have been made.
Setting the keyed
flag to true
associates a unique string key with each
bucket and returns the ranges as a hash rather than an array:
POST /sales/_search?size=0 { "aggs" : { "sales_over_time" : { "date_histogram" : { "field" : "date", "interval" : "1M", "format" : "yyyy-MM-dd", "keyed": true } } } }
Response:
{ ... "aggregations": { "sales_over_time": { "buckets": { "2015-01-01": { "key_as_string": "2015-01-01", "key": 1420070400000, "doc_count": 3 }, "2015-02-01": { "key_as_string": "2015-02-01", "key": 1422748800000, "doc_count": 2 }, "2015-03-01": { "key_as_string": "2015-03-01", "key": 1425168000000, "doc_count": 2 } } } } }
As with the normal histogram,
both document-level scripts and
value-level scripts are supported. You can 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. For more information, see
Extended Bounds
.
The missing
parameter defines how to treat documents that are missing a value.
By default, they are ignored, but it is also possible to treat them as if they
have a value.
By default the returned buckets are sorted by their key
ascending, but you can
control the order using
the order
setting. This setting supports the same order
functionality as
Terms Aggregation
.
Use _key
instead of _time
to order buckets by their dates/keys
When you need to aggregate the results by day of the week, use a script that returns the day of the week:
POST /sales/_search?size=0 { "aggs": { "dayOfWeek": { "terms": { "script": { "lang": "painless", "source": "doc['date'].value.dayOfWeekEnum.value" } } } } }
Response:
{ ... "aggregations": { "dayOfWeek": { "doc_count_error_upper_bound": 0, "sum_other_doc_count": 0, "buckets": [ { "key": "7", "doc_count": 4 }, { "key": "4", "doc_count": 3 } ] } } }
The response will contain all the buckets having the relative day of the week as key : 1 for Monday, 2 for Tuesday… 7 for Sunday.