« Date histogram aggregation Diversified sampler aggregation »
Elastic Docs ›Elasticsearch Guide [8.15] ›Aggregations ›Bucket aggregations

Date range aggregation

edit

Date range aggregation

edit

A range aggregation that is dedicated for date values. The main difference between this aggregation and the normal range aggregation is that the from and to values can be expressed in Date Math expressions, and it is also possible to specify a date format by which the from and to response fields will be returned. Note that this aggregation includes the from value and excludes the to value for each range.

Example:

resp = client.search(
    index="sales",
    size="0",
    aggs={
        "range": {
            "date_range": {
                "field": "date",
                "format": "MM-yyyy",
                "ranges": [
                    {
                        "to": "now-10M/M"
                    },
                    {
                        "from": "now-10M/M"
                    }
                ]
            }
        }
    },
)
print(resp)
response = client.search(
  index: 'sales',
  size: 0,
  body: {
    aggregations: {
      range: {
        date_range: {
          field: 'date',
          format: 'MM-yyyy',
          ranges: [
            {
              to: 'now-10M/M'
            },
            {
              from: 'now-10M/M'
            }
          ]
        }
      }
    }
  }
)
puts response
const response = await client.search({
  index: "sales",
  size: 0,
  aggs: {
    range: {
      date_range: {
        field: "date",
        format: "MM-yyyy",
        ranges: [
          {
            to: "now-10M/M",
          },
          {
            from: "now-10M/M",
          },
        ],
      },
    },
  },
});
console.log(response);
POST /sales/_search?size=0
{
  "aggs": {
    "range": {
      "date_range": {
        "field": "date",
        "format": "MM-yyyy",
        "ranges": [
          { "to": "now-10M/M" },  
          { "from": "now-10M/M" } 
        ]
      }
    }
  }
}

< now minus 10 months, rounded down to the start of the month.

>= now minus 10 months, rounded down to the start of the month.

In the example above, we created two range buckets, the first will "bucket" all documents dated prior to 10 months ago and the second will "bucket" all documents dated since 10 months ago

Response:

{
  ...
  "aggregations": {
    "range": {
      "buckets": [
        {
          "to": 1.4436576E12,
          "to_as_string": "10-2015",
          "doc_count": 7,
          "key": "*-10-2015"
        },
        {
          "from": 1.4436576E12,
          "from_as_string": "10-2015",
          "doc_count": 0,
          "key": "10-2015-*"
        }
      ]
    }
  }
}

If a format or date value is incomplete, the date range aggregation replaces any missing components with default values. See Missing date components.

Missing Values

edit

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. This is done by adding a set of fieldname : value mappings to specify default values per field.

resp = client.search(
    index="sales",
    size="0",
    aggs={
        "range": {
            "date_range": {
                "field": "date",
                "missing": "1976/11/30",
                "ranges": [
                    {
                        "key": "Older",
                        "to": "2016/02/01"
                    },
                    {
                        "key": "Newer",
                        "from": "2016/02/01",
                        "to": "now/d"
                    }
                ]
            }
        }
    },
)
print(resp)
response = client.search(
  index: 'sales',
  size: 0,
  body: {
    aggregations: {
      range: {
        date_range: {
          field: 'date',
          missing: '1976/11/30',
          ranges: [
            {
              key: 'Older',
              to: '2016/02/01'
            },
            {
              key: 'Newer',
              from: '2016/02/01',
              to: 'now/d'
            }
          ]
        }
      }
    }
  }
)
puts response
const response = await client.search({
  index: "sales",
  size: 0,
  aggs: {
    range: {
      date_range: {
        field: "date",
        missing: "1976/11/30",
        ranges: [
          {
            key: "Older",
            to: "2016/02/01",
          },
          {
            key: "Newer",
            from: "2016/02/01",
            to: "now/d",
          },
        ],
      },
    },
  },
});
console.log(response);
POST /sales/_search?size=0
{
   "aggs": {
       "range": {
           "date_range": {
               "field": "date",
               "missing": "1976/11/30",
               "ranges": [
                  {
                    "key": "Older",
                    "to": "2016/02/01"
                  }, 
                  {
                    "key": "Newer",
                    "from": "2016/02/01",
                    "to" : "now/d"
                  }
              ]
          }
      }
   }
}

Documents without a value in the date field will be added to the "Older" bucket, as if they had a date value of "1976-11-30".

Date Format/Pattern

edit

this information was copied from DateTimeFormatter

All ASCII letters are reserved as format pattern letters, which are defined as follows:

Symbol Meaning Presentation Examples

G

era

text

AD; Anno Domini; A

u

year

year

2004; 04

y

year-of-era

year

2004; 04

D

day-of-year

number

189

M/L

month-of-year

number/text

7; 07; Jul; July; J

d

day-of-month

number

10

Q/q

quarter-of-year

number/text

3; 03; Q3; 3rd quarter

Y

week-based-year

year

1996; 96

w

week-of-week-based-year

number

27

W

week-of-month

number

4

E

day-of-week

text

Tue; Tuesday; T

e/c

localized day-of-week

number/text

2; 02; Tue; Tuesday; T

F

week-of-month

number

3

a

am-pm-of-day

text

PM

h

clock-hour-of-am-pm (1-12)

number

12

K

hour-of-am-pm (0-11)

number

0

k

clock-hour-of-am-pm (1-24)

number

0

H

hour-of-day (0-23)

number

0

m

minute-of-hour

number

30

s

second-of-minute

number

55

S

fraction-of-second

fraction

978

A

milli-of-day

number

1234

n

nano-of-second

number

987654321

N

nano-of-day

number

1234000000

V

time-zone ID

zone-id

America/Los_Angeles; Z; -08:30

z

time-zone name

zone-name

Pacific Standard Time; PST

O

localized zone-offset

offset-O

GMT+8; GMT+08:00; UTC-08:00;

X

zone-offset Z for zero

offset-X

Z; -08; -0830; -08:30; -083015; -08:30:15;

x

zone-offset

offset-x

+0000; -08; -0830; -08:30; -083015; -08:30:15;

Z

zone-offset

offset-Z

+0000; -0800; -08:00;

p

pad next

pad modifier

1

'

escape for text

delimiter

''

single quote

literal

'

[

optional section start

]

optional section end

#

reserved for future use

{

reserved for future use

}

The count of pattern letters determines the format.

Text
The text style is determined based on the number of pattern letters used. Less than 4 pattern letters will use the short form. Exactly 4 pattern letters will use the full form. Exactly 5 pattern letters will use the narrow form. Pattern letters L, c, and q specify the stand-alone form of the text styles.
Number
If the count of letters is one, then the value is output using the minimum number of digits and without padding. Otherwise, the count of digits is used as the width of the output field, with the value zero-padded as necessary. The following pattern letters have constraints on the count of letters. Only one letter of c and F can be specified. Up to two letters of d, H, h, K, k, m, and s can be specified. Up to three letters of D can be specified.
Number/Text
If the count of pattern letters is 3 or greater, use the Text rules above. Otherwise use the Number rules above.
Fraction
Outputs the nano-of-second field as a fraction-of-second. The nano-of-second value has nine digits, thus the count of pattern letters is from 1 to 9. If it is less than 9, then the nano-of-second value is truncated, with only the most significant digits being output.
Year
The count of letters determines the minimum field width below which padding is used. If the count of letters is two, then a reduced two digit form is used. For printing, this outputs the rightmost two digits. For parsing, this will parse using the base value of 2000, resulting in a year within the range 2000 to 2099 inclusive. If the count of letters is less than four (but not two), then the sign is only output for negative years as per SignStyle.NORMAL. Otherwise, the sign is output if the pad width is exceeded, as per SignStyle.EXCEEDS_PAD.
ZoneId
This outputs the time-zone ID, such as Europe/Paris. If the count of letters is two, then the time-zone ID is output. Any other count of letters throws IllegalArgumentException.
Zone names
This outputs the display name of the time-zone ID. If the count of letters is one, two or three, then the short name is output. If the count of letters is four, then the full name is output. Five or more letters throws IllegalArgumentException.
Offset X and x
This formats the offset based on the number of pattern letters. One letter outputs just the hour, such as +01, unless the minute is non-zero in which case the minute is also output, such as +0130. Two letters outputs the hour and minute, without a colon, such as +0130. Three letters outputs the hour and minute, with a colon, such as +01:30. Four letters outputs the hour and minute and optional second, without a colon, such as +013015. Five letters outputs the hour and minute and optional second, with a colon, such as +01:30:15. Six or more letters throws IllegalArgumentException. Pattern letter X (upper case) will output Z when the offset to be output would be zero, whereas pattern letter x (lower case) will output +00, +0000, or +00:00.
Offset O
This formats the localized offset based on the number of pattern letters. One letter outputs the short form of the localized offset, which is localized offset text, such as GMT, with hour without leading zero, optional 2-digit minute and second if non-zero, and colon, for example GMT+8. Four letters outputs the full form, which is localized offset text, such as GMT, with 2-digit hour and minute field, optional second field if non-zero, and colon, for example `GMT+08:00. Any other count of letters throws IllegalArgumentException.
Offset Z
This formats the offset based on the number of pattern letters. One, two or three letters outputs the hour and minute, without a colon, such as +0130. The output will be +0000 when the offset is zero. Four letters outputs the full form of localized offset, equivalent to four letters of Offset-O. The output will be the corresponding localized offset text if the offset is zero. Five letters outputs the hour, minute, with optional second if non-zero, with colon. It outputs Z if the offset is zero. Six or more letters throws IllegalArgumentException.
Optional section
The optional section markers work exactly like calling DateTimeFormatterBuilder.optionalStart() and DateTimeFormatterBuilder.optionalEnd().
Pad modifier
Modifies the pattern that immediately follows to be padded with spaces. The pad width is determined by the number of pattern letters. This is the same as calling DateTimeFormatterBuilder.padNext(int).

For example, ppH outputs the hour-of-day padded on the left with spaces to a width of 2.

Any unrecognized letter is an error. Any non-letter character, other than [, ], {, }, # and the single quote will be output directly. Despite this, it is recommended to use single quotes around all characters that you want to output directly to ensure that future changes do not break your application.

Time zone in date range aggregations

edit

Dates can be converted from another time zone to UTC by specifying the time_zone parameter.

Time zones may either be specified as an ISO 8601 UTC offset (e.g. +01:00 or -08:00) or as one of the time zone ids from the TZ database.

The time_zone parameter is also applied to rounding in date math expressions. As an example, to round to the beginning of the day in the CET time zone, you can do the following:

resp = client.search(
    index="sales",
    size="0",
    aggs={
        "range": {
            "date_range": {
                "field": "date",
                "time_zone": "CET",
                "ranges": [
                    {
                        "to": "2016/02/01"
                    },
                    {
                        "from": "2016/02/01",
                        "to": "now/d"
                    },
                    {
                        "from": "now/d"
                    }
                ]
            }
        }
    },
)
print(resp)
response = client.search(
  index: 'sales',
  size: 0,
  body: {
    aggregations: {
      range: {
        date_range: {
          field: 'date',
          time_zone: 'CET',
          ranges: [
            {
              to: '2016/02/01'
            },
            {
              from: '2016/02/01',
              to: 'now/d'
            },
            {
              from: 'now/d'
            }
          ]
        }
      }
    }
  }
)
puts response
const response = await client.search({
  index: "sales",
  size: 0,
  aggs: {
    range: {
      date_range: {
        field: "date",
        time_zone: "CET",
        ranges: [
          {
            to: "2016/02/01",
          },
          {
            from: "2016/02/01",
            to: "now/d",
          },
          {
            from: "now/d",
          },
        ],
      },
    },
  },
});
console.log(response);
POST /sales/_search?size=0
{
   "aggs": {
       "range": {
           "date_range": {
               "field": "date",
               "time_zone": "CET",
               "ranges": [
                  { "to": "2016/02/01" }, 
                  { "from": "2016/02/01", "to" : "now/d" }, 
                  { "from": "now/d" }
              ]
          }
      }
   }
}

This date will be converted to 2016-02-01T00:00:00.000+01:00.

now/d will be rounded to the beginning of the day in the CET time zone.

Keyed Response

edit

Setting the keyed flag to true will associate a unique string key with each bucket and return the ranges as a hash rather than an array:

resp = client.search(
    index="sales",
    size="0",
    aggs={
        "range": {
            "date_range": {
                "field": "date",
                "format": "MM-yyy",
                "ranges": [
                    {
                        "to": "now-10M/M"
                    },
                    {
                        "from": "now-10M/M"
                    }
                ],
                "keyed": True
            }
        }
    },
)
print(resp)
response = client.search(
  index: 'sales',
  size: 0,
  body: {
    aggregations: {
      range: {
        date_range: {
          field: 'date',
          format: 'MM-yyy',
          ranges: [
            {
              to: 'now-10M/M'
            },
            {
              from: 'now-10M/M'
            }
          ],
          keyed: true
        }
      }
    }
  }
)
puts response
const response = await client.search({
  index: "sales",
  size: 0,
  aggs: {
    range: {
      date_range: {
        field: "date",
        format: "MM-yyy",
        ranges: [
          {
            to: "now-10M/M",
          },
          {
            from: "now-10M/M",
          },
        ],
        keyed: true,
      },
    },
  },
});
console.log(response);
POST /sales/_search?size=0
{
  "aggs": {
    "range": {
      "date_range": {
        "field": "date",
        "format": "MM-yyy",
        "ranges": [
          { "to": "now-10M/M" },
          { "from": "now-10M/M" }
        ],
        "keyed": true
      }
    }
  }
}

Response:

{
  ...
  "aggregations": {
    "range": {
      "buckets": {
        "*-10-2015": {
          "to": 1.4436576E12,
          "to_as_string": "10-2015",
          "doc_count": 7
        },
        "10-2015-*": {
          "from": 1.4436576E12,
          "from_as_string": "10-2015",
          "doc_count": 0
        }
      }
    }
  }
}

It is also possible to customize the key for each range:

resp = client.search(
    index="sales",
    size="0",
    aggs={
        "range": {
            "date_range": {
                "field": "date",
                "format": "MM-yyy",
                "ranges": [
                    {
                        "from": "01-2015",
                        "to": "03-2015",
                        "key": "quarter_01"
                    },
                    {
                        "from": "03-2015",
                        "to": "06-2015",
                        "key": "quarter_02"
                    }
                ],
                "keyed": True
            }
        }
    },
)
print(resp)
response = client.search(
  index: 'sales',
  size: 0,
  body: {
    aggregations: {
      range: {
        date_range: {
          field: 'date',
          format: 'MM-yyy',
          ranges: [
            {
              from: '01-2015',
              to: '03-2015',
              key: 'quarter_01'
            },
            {
              from: '03-2015',
              to: '06-2015',
              key: 'quarter_02'
            }
          ],
          keyed: true
        }
      }
    }
  }
)
puts response
const response = await client.search({
  index: "sales",
  size: 0,
  aggs: {
    range: {
      date_range: {
        field: "date",
        format: "MM-yyy",
        ranges: [
          {
            from: "01-2015",
            to: "03-2015",
            key: "quarter_01",
          },
          {
            from: "03-2015",
            to: "06-2015",
            key: "quarter_02",
          },
        ],
        keyed: true,
      },
    },
  },
});
console.log(response);
POST /sales/_search?size=0
{
  "aggs": {
    "range": {
      "date_range": {
        "field": "date",
        "format": "MM-yyy",
        "ranges": [
          { "from": "01-2015", "to": "03-2015", "key": "quarter_01" },
          { "from": "03-2015", "to": "06-2015", "key": "quarter_02" }
        ],
        "keyed": true
      }
    }
  }
}

Response:

{
  ...
  "aggregations": {
    "range": {
      "buckets": {
        "quarter_01": {
          "from": 1.4200704E12,
          "from_as_string": "01-2015",
          "to": 1.425168E12,
          "to_as_string": "03-2015",
          "doc_count": 5
        },
        "quarter_02": {
          "from": 1.425168E12,
          "from_as_string": "03-2015",
          "to": 1.4331168E12,
          "to_as_string": "06-2015",
          "doc_count": 2
        }
      }
    }
  }
}
« Date histogram aggregation Diversified sampler aggregation »