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Introduction

Query reference overview

APL

IntroductionSample queriesAll features
Functions
Scalar functions
Array functions
Overview
array_concat
array_extract
array_iff
array_index_of
array_length
array_reverse
array_rotate_left
array_rotate_right
array_select_dict
array_shift_left
array_shift_right
array_slice
array_sort_asc
array_sort_desc
array_split
array_sum
bag_has_key
bag_keys
bag_pack
bag_zip
isarray
len
pack_array
pack_dictionary
strcat_array
Conditional functions
Overview
case
iff
Conversion functions
Overview
dynamic_to_json
ensure_field
isbool
toarray
tobool
todatetime
todouble, toreal
todynamic
tohex
toint, tolong
tostring
totimespan
Datetime functions
Overview
ago
datetime_add
datetime_diff
datetime_part
dayofmonth
dayofweek
dayofyear
endofday
endofmonth
endofweek
endofyear
getmonth
getyear
hourofday
monthofyear
now
startofday
startofmonth
startofweek
startofyear
unixtime_microseconds_todatetime
unixtime_milliseconds_todatetime
unixtime_nanoseconds_todatetime
unixtime_seconds_todatetime
week_of_year
GenAI functions
Overview
genai_concat_contents
genai_conversation_turns
genai_cost
genai_estimate_tokens
genai_extract_assistant_response
genai_extract_function_results
genai_extract_system_prompt
genai_extract_tool_calls
genai_extract_user_prompt
genai_get_content_by_index
genai_get_content_by_role
genai_get_pricing
genai_get_role
genai_has_tool_calls
genai_input_cost
genai_is_truncated
genai_message_roles
genai_output_cost
Hash functions
Overview
hash
hash_md5
hash_sha1
hash_sha256
hash_sha512
IP functions
Overview
format_ipv4
format_ipv4_mask
geo_info_from_ip_address
has_any_ipv4
has_any_ipv4_prefix
has_ipv4
has_ipv4_prefix
ipv4_compare
ipv4_is_in_range
ipv4_is_in_any_range
ipv4_is_match
ipv4_is_private
ipv4_netmask_suffix
ipv6_compare
ipv6_is_in_any_range
ipv6_is_in_range
ipv6_is_match
parse_ipv4
parse_ipv4_mask
Mathematical functions
Overview
abs
acos
asin
atan
atan2
cos
cot
degrees
exp
exp2
exp10
gamma
isfinite
isinf
isint
isnan
log
log2
log10
loggamma
max_of
min_of
not
pi
pow
radians
rand
range
round
set_difference
set_has_element
set_intersect
set_union
sign
sin
sqrt
tan
Metadata functions
Overview
column_ifexists
cursor_current
ingestion_time
Pair functions
Overview
find_pair
pair
parse_pair
Rounding functions
Overview
bin
bin_auto
ceiling
floor
String functions
Overview
base64_decode_toarray
base64_decode_tostring
base64_encode_fromarray
base64_encode_tostring
coalesce
countof
countof_regex
extract
extract_all
format_bytes
format_url
gettype
indexof
indexof_regex
isascii
isempty
isnotempty
isnotnull
isnull
parse_bytes
parse_csv
parse_json
parse_path
parse_url
parse_urlquery
quote
regex_quote
replace
replace_regex
replace_string
reverse
split
strcat
strcat_delim
strcmp
string_size
strlen
strrep
strip_ansi_escapes
substring
tolower
totitle
toupper
translate
trim
trim_end
trim_end_regex
trim_regex
trim_space
trim_start
trim_start_regex
unicode_codepoints_from_string
unicode_codepoints_to_string
url_decode
url_encode
SQL functions
Overview
parse_sql
format_sql
Time series functions
Overview
series_abs
series_acos
series_add
series_asin
series_atan
series_ceiling
series_cos
series_cosine_similarity
series_divide
series_dot_product
series_equals
series_exp
series_fft
series_fill_backward
series_fill_const
series_fill_forward
series_fill_linear
series_fir
series_floor
series_greater
series_greater_equals
series_ifft
series_iir
series_less
series_less_equals
series_log
series_magnitude
series_max
series_min
series_multiply
series_not_equals
series_pearson_correlation
series_pow
series_sign
series_sin
series_stats
series_stats_dynamic
series_subtract
series_sum
series_tan
Type functions
Overview
isimei
ismap
isreal
iscc
isstring
isutf8
Aggregation functions
Overview
arg_min
arg_max
avg
avgif
count
countif
dcount
dcountif
histogram
histogramif
make_list
make_list_if
make_set
make_set_if
max
maxif
min
minif
percentile
percentileif
percentiles_array
percentiles_arrayif
phrases
rate
spotlight
stdev
stdevif
sum
sumif
topk
topkif
variance
varianceif
Operators
Tabular operators
Overview
count
distinct
extend
extend-valid
externaldata
getschema
join
limit
lookup
make-series
mv-expand
order
parse
parse-kv
parse-where
project
project-away
project-keep
project-rename
project-reorder
redact
sample
search
sort
summarize
take
top
union
where
Scalar operators
Set membership operators
Overview
in
!in
in~
!in~
Logical
Numerical
String
Reference
Entity names
Map fields
Null values
Scalar data types
Set statement
Special field attributes
Migrate
Splunk SPL
SQL
Sumo Logic

MPL

Language featuresSample queriesMigrate
APL/Functions

hourofday

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

Use the hourofday function in APL to extract the hour of the day from a datetime value. The function returns an integer from 0 to 23, where 0 represents midnight and 23 represents 11 PM.

You can use hourofday to group records by hour for time-of-day analysis, peak traffic identification, and intraday pattern detection. This is useful for operational dashboards, capacity planning, and anomaly detection.

Use it when you want to:

  • Identify peak traffic hours in your services.
  • Analyze hourly patterns in request volume or error rates.
  • Create time-of-day summaries across log, trace, or security datasets.

Usage

Syntax

hourofday(datetime)

Parameters

NameTypeDescription
datetimedatetimeThe input datetime value.

Returns

An int from 0 to 23 representing the hour of the day.

Use case examples

Analyze HTTP request volume by hour to identify peak traffic periods.

Query

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

Run in Playground

Output

hourrequest_count
0312
1287
141523

This query groups HTTP log entries by hour and counts the requests per hour, revealing peak and off-peak periods.

Find peak hours for trace activity by service to understand when services experience the highest load.

Query

['otel-demo-traces']
| extend hour = hourofday(_time)
| summarize trace_count = count() by hour, ['service.name']
| sort by hour asc

Run in Playground

Output

hourservice.nametrace_count
9frontend2450
10frontend2780
14cart1340

This query shows the hourly distribution of traces per service, helping you identify when each service is busiest.

Detect after-hours error spikes by analyzing the hourly distribution of HTTP errors.

Query

['sample-http-logs']
| where toint(status) >= 400
| extend hour = hourofday(_time)
| summarize error_count = count() by hour
| sort by hour asc

Run in Playground

Output

hourerror_count
287
392
1534

This query reveals the hourly pattern of HTTP errors, helping you detect unusual activity during off-peak hours.

List of related functions

  • dayofweek: Returns the day of the week as a timespan, complementing hourly analysis with day-level detail.
  • dayofmonth: Returns the day of the month from a datetime.
  • datetime-part: Extracts a specific date part (such as hour) as an integer.
  • startofday: Returns the start of the day for a datetime, useful for daily binning.
  • endofday: Returns the end of the day for a datetime value.

Other query languages

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