This page explains how to use the percentileif aggregation function in APL.
percentileif
aggregation function calculates the percentile of a numeric column, conditional on a specified boolean predicate. This function is useful for filtering data dynamically and determining percentile values based only on relevant subsets of data.
You can use percentileif
to gain insights in various scenarios, such as:
Splunk SPL users
percentileif
aggregation in APL works similarly to percentile
combined with conditional filtering in SPL. However, APL integrates the condition directly into the aggregation for simplicity.ANSI SQL users
WHERE
clause. APL simplifies this by embedding the condition directly in the percentileif
aggregation.Parameter | Description |
---|---|
Field | The numeric field from which to calculate the percentile. |
Percentile | A number between 0 and 100 that specifies the percentile to calculate. |
Predicate | A Boolean expression that filters rows to include in the calculation. |
Field
for rows where the Predicate
evaluates to true
.
percentileif
to analyze request durations for specific HTTP methods.Querypost_p90 | get_p90 |
---|---|
1.691 ms | 1.453 ms |
percentile
when you don’t need conditional filtering.avgif
for mean calculations instead of percentiles.minif
for identifying the lowest values within subsets.maxif
for identifying the highest values within subsets.sumif
for conditional total calculations.