startofday

This page explains how to use the startofday function in APL.

Use the startofday function in APL to round a datetime value down to the start of the day. The function returns midnight (00:00:00) for the date that contains the given datetime value. You can optionally shift the result by a specified number of days using the offset parameter.

You can use startofday to bin events into daily buckets for aggregation, reporting, and trend analysis across log, trace, and security datasets.

Use it when you want to:

• Group events by day for daily summaries and dashboards.
• Align timestamps to day boundaries for consistent aggregation.
• Compare metrics across different days.

For users of other query languages

If you come from other query languages, this section explains how to adjust your existing queries to achieve the same results in APL.

Splunk SPL users

In Splunk SPL, you use relative_time with the @d snap-to modifier to round a timestamp to the start of the day. In APL, the startofday function achieves the same result and supports an optional day offset.

Splunk example

... | eval day_start=relative_time(_time, "@d")

... | extend day_start = startofday(_time)

ANSI SQL users

In ANSI SQL, you use DATE_TRUNC('day', timestamp_column) to truncate a timestamp to the start of the day. In APL, startofday provides the same functionality with an optional offset parameter.

SQL example

SELECT DATE_TRUNC('day', timestamp_column) AS day_start FROM events;

['dataset']
| extend day_start = startofday(_time)

Usage

Syntax

startofday(datetime [, offset])

Parameters

Name	Type	Description
datetime	datetime	The input datetime value.
offset	long	Optional: The number of days to offset from the input datetime. Default is 0.

Returns

A datetime representing the start of the day (00:00:00) for the given date value, shifted by the offset if specified.

Use case examples

Log analysis

Count requests per day to identify daily traffic patterns.

Query

['sample-http-logs']
| extend day_start = startofday(_time)
| summarize request_count = count() by day_start
| sort by day_start asc

Run in Playground

Output

day_start	request_count
2025-01-13T00:00:00Z	1523
2025-01-14T00:00:00Z	1687
2025-01-15T00:00:00Z	1445

This query bins each HTTP request to the start of its day and counts the total requests per day.

OpenTelemetry traces

Calculate the daily average span duration for each service.

Query

['otel-demo-traces']
| extend day_start = startofday(_time)
| summarize avg_duration = avg(duration) by day_start, ['service.name']
| sort by day_start asc

Run in Playground

Output

day_start	service.name	avg_duration
2025-01-13T00:00:00Z	frontend	00:00:01.2340000
2025-01-14T00:00:00Z	frontend	00:00:01.1750000
2025-01-15T00:00:00Z	frontend	00:00:01.2890000

This query groups trace spans by day and service, then calculates the average span duration for each combination.

Security logs

Track daily error counts to identify days with unusual server error activity.

Query

['sample-http-logs']
| where toint(status) >= 500
| extend day_start = startofday(_time)
| summarize error_count = count() by day_start
| sort by day_start asc

Run in Playground

Output

day_start	error_count
2025-01-13T00:00:00Z	12
2025-01-14T00:00:00Z	27
2025-01-15T00:00:00Z	8

This query filters for server errors and counts them per day to reveal daily error patterns.

List of related functions

• endofday: Returns the end of the day for a datetime value.
• startofweek: Returns the start of the week for a datetime value.
• startofmonth: Returns the start of the month for a datetime value.
• startofyear: Returns the start of the year for a datetime value.
• bin: Rounds values down to a fixed-size bin, useful for grouping timestamps.