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bin_at()

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Applies to: ✅ Microsoft Fabric ✅ Azure Data Explorer ✅ Azure Monitor ✅ Microsoft Sentinel

Returns the value rounded down to the nearest bin size, which is aligned to a fixed reference point.

In contrast to the bin() function, where the point of alignment is predefined, bin_at() allows you to define a fixed point for alignment. Results can align before or after the fixed point.

Syntax

bin_at (value,bin_size,fixed_point)

Learn more about syntax conventions.

Parameters

Name Type Required Description
value int, long, real, timespan, or datetime ✔️ The value to round.
bin_size int, long, real, or timespan ✔️ The size of each bin.
fixed_point int, long, real, timespan, or datetime ✔️ A constant of the same type as value, which is used as a fixed reference point.

Note

If value is a timespan or datetime, then the bin_size must be a timespan.

Returns

The nearest multiple of bin_size below the given value that aligns to the specified fixed_point.

Examples

In the following example, value is rounded down to the nearest bin_size that aligns to the fixed_point.

print bin_at(6.5, 2.5, 7)

Output

print_0
4.5

In the following example, the time interval is binned into daily bins aligned to a 12 hour fixed point. The return value is -12 since a daily bin aligned to 12 hours rounds down to 12 on the previous day.

print bin_at(time(1h), 1d, 12h)

Output

print_0
-12:00:00

In the following example, daily bins align to noon.

print bin_at(datetime(2017-05-15 10:20:00.0), 1d, datetime(1970-01-01 12:00:00.0))

Output

print_0
2017-05-14T12:00:00Z

In the following example, bins are weekly and align to the start of Sunday June 6, 2017. The example returns a bin aligned to Sundays.

print bin_at(datetime(2017-05-17 10:20:00.0), 7d, datetime(2017-06-04 00:00:00.0))

Output

print_0
2017-05-14T00:00:00Z

In the following example, the total number of events are grouped into daily bins aligned to the fixed_point date and time. The fixed_point value is included in one of the returned bins.

datatable(Date:datetime, NumOfEvents:int)[
datetime(2018-02-24T15:14),3,
datetime(2018-02-24T15:24),4,
datetime(2018-02-23T16:14),4,
datetime(2018-02-23T17:29),4,
datetime(2018-02-26T15:14),5]
| summarize TotalEvents=sum(NumOfEvents) by bin_at(Date, 1d, datetime(2018-02-24 15:14:00.0000000))

Output

Date TotalEvents
2018-02-23T15:14:00Z 8
2018-02-24T15:14:00Z 7
2018-02-26T15:14:00Z 5

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