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Time bucketing is the segmenting of a given data set into “buckets”, based on some time-based column value or expression within the set.

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TIME_BUCKET

Time bucketing is accomplished through the use of the TIME_BUCKET function, which calculates the time 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 TIME_BUCKET function is: 
TIME_BUCKET(width, timestamp[, offset[, base]])

width

The number of milliseconds each bucket should span. An INTERVAL can also be used to specify the width.

timestamp

A date/time column or expression, the time portion of which will be used in placing the corresponding record within the correct time bucket.

offset

The number of milliseconds after (for a positive offset) or number of milliseconds 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.

base

The starting date/time to which buckets will be aligned. The default is 2000-01-03 00:00:00.

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Example

Typically, TIME_BUCKET is used in the following type of query:
TIME_BUCKET Example
SELECT
	TIME_BUCKET
	(
		INTERVAL 5 MINUTES,
		ts,
		INTERVAL -2.5 MINUTES,
		'2023-02-28'
	) + INTERVAL 2.5 MINUTES AS five_minute_midpoint,
	AVG(cpu) AS avg_cpu
FROM example.host_metrics
GROUP BY five_minute_midpoint
ORDER BY five_minute_midpoint
The result will be as follows:
  • Timestamps in the ts column of the example.host_metrics table will be grouped into buckets
  • Each bucket will span a 5 minute interval
  • The baseline bucket will start at 2023-02-27 23:57:30 (2023-02-28 offset by -2.5 minutes) and continue through 2023-02-28 00:02:30 (5 minutes from 2023-02-27 23:57:30)
  • Buckets will extend before & after the baseline bucket in contiguous, non-overlapping fashion
  • Each result record will show the timestamp in the middle of the bucket’s range (+ INTERVAL 2.5 MINUTES from the start of each 5 minute span) and the average CPU usage across the records contained within that date/time range
  • Gaps in the data will not be filled in with empty buckets—only buckets containing the timestamps found in the ts column of example.host_metrics will be returned in the result set