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Introduction

Query reference overview

APL

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Functions
Scalar functions
Array functions
Overview
array_concat
array_extract
array_iff
array_index_of
array_length
array_reverse
array_rotate_left
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array_select_dict
array_shift_left
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isarray
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case
iff
Conversion functions
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Datetime functions
Overview
ago
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getmonth
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startofmonth
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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
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hash
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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
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abs
acos
asin
atan
atan2
cos
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degrees
exp
exp2
exp10
gamma
isfinite
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isint
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log
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max_of
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not
pi
pow
radians
rand
range
round
set_difference
set_has_element
set_intersect
set_union
sign
sin
sqrt
tan
Metadata functions
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column_ifexists
cursor_current
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Pair functions
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find_pair
pair
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Rounding functions
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bin
bin_auto
ceiling
floor
String functions
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base64_decode_toarray
base64_decode_tostring
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coalesce
countof
countof_regex
extract
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format_bytes
format_url
gettype
indexof
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isascii
isempty
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SQL functions
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Time series functions
Overview
series_abs
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series_ceiling
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series_cosine_similarity
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series_greater_equals
series_ifft
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series_less_equals
series_log
series_magnitude
series_max
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series_multiply
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series_pow
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series_sin
series_stats
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series_tan
Type functions
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count
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histogram
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make_list
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percentile
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MPL

Language featuresSample queriesMigrate
APL/Functions

series_floor

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

The series_floor function rounds down each element in a numeric dynamic array (series) to the nearest integer that’s less than or equal to the original value. This function applies the mathematical floor operation element-wise across the entire array, which is useful for data discretization, quantization, and integer conversion in time series data.

You can use series_floor when you want to convert floating-point values to integers by rounding down, discretize continuous data into bins, or prepare data for categorical analysis. This is particularly useful for creating integer-based categories, implementing quantization schemes, or when you need to ensure values don’t exceed certain thresholds. Typical applications include data binning, performance categorization, and mathematical modeling where integer values are required.

Usage

Syntax

series_floor(array)

Parameters

ParameterTypeDescription
arraydynamicA dynamic array of numeric values.

Returns

A dynamic array where each element is the floor (largest integer less than or equal to) the corresponding input element.

Use case examples

In log analysis, you can use series_floor to discretize request durations into integer bins for categorical analysis or performance categorization.

Query

['sample-http-logs']
| summarize durations = make_list(req_duration_ms) by id
| extend floor_durations = series_floor(durations)

Run in Playground

Output

iddurationsfloor_durations
u123[150.7, 200.3, 250.9][150, 200, 250]
u456[100.2, 300.8, 400.1][100, 300, 400]

This query converts floating-point request durations to integers by rounding down, useful for creating discrete performance categories.

In OpenTelemetry traces, you can use series_floor to discretize span durations into integer milliseconds for consistent latency analysis.

Query

['otel-demo-traces']
| summarize durations = make_list(duration) by ['service.name']
| extend floor_durations = series_floor(durations)

Run in Playground

Output

service.namedurationsfloor_durations
frontend[100.7ms, 200.3ms, 300.9ms][100ms, 200ms, 300ms]
product-catalog[50.2ms, 150.8ms, 250.1ms][50ms, 150ms, 250ms]

This query converts floating-point span durations to integer milliseconds by rounding down, useful for consistent latency categorization across services.

In security logs, you can use series_floor to discretize request durations into integer bins for security analysis and attack pattern detection.

Query

['sample-http-logs']
| summarize durations = make_list(req_duration_ms) by status
| extend floor_durations = series_floor(durations)

Run in Playground

Output

statusdurationsfloor_durations
200[150.7, 200.3, 250.9][150, 200, 250]
500[100.2, 300.8, 400.1][100, 300, 400]

This query converts floating-point request durations to integers by rounding down grouped by status code, useful for creating discrete performance categories in security analysis.

List of related functions

  • series_abs: Returns the absolute value of each element in an array. Use when you need to normalize values before applying floor operations.
  • series_exp: Calculates the exponential of each element in an array. Use for exponential transformations instead of floor operations.
  • series_cos: Returns the cosine of each element in an array. Use for trigonometric transformations instead of floor operations.
  • series_sin: Returns the sine of each element in an array. Use for periodic transformations instead of floor operations.
  • series_tan: Returns the tangent of each element in an array. Use for trigonometric transformations with different periodicity.

Other query languages

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