Date bucketing is the segmenting of a given data set into "buckets", based on some date-based column value or expression within the set.
Date bucketing is accomplished through the use of the DATE_BUCKET function, which calculates the date range in which a given timestamp falls, based on a set of fixed-width "buckets", start-aligned to a date/time, and offset from that start date/time.
The basic form of the DATE_BUCKET function is:
|The number of days each bucket should span. An INTERVAL can also be used to specify the width.
|A date/time column or expression, the date portion of which will be used in placing the corresponding record within the correct date bucket.
|The number of days after (for a positive offset) or number of days before (for a negative offset) the base date/time to which the buckets should be aligned. An INTERVAL can also be used to specify the offset. The default is no offset.
|The starting date/time to which buckets will be aligned. The default is 2000-01-03.
Typically, DATE_BUCKET is used in the following type of query:
The result will be as follows:
- Dates in the ds column of the example.host_metrics_summary table will be grouped into buckets
- Each bucket will span a range of 7 days
- The baseline bucket will start at 2023-02-18 (2023-02-21 offset by -3 days) and continue through 2023-02-24 (7 days, including 2023-02-18)
- Buckets will extend before & after the baseline bucket in contiguous, non-overlapping fashion
- Each result record will show the date in the middle of the bucket's date range (+ INTERVAL 3 DAYS from the start of each 7 day span) and the average CPU usage across the records contained within that date range
- Gaps in the data will not be filled in with empty buckets--only buckets containing the dates found in the ds column of example.host_metrics_summary will be returned in the result set