Over-time aggregations :: Kloudfuse Docs
Over-time aggregations
Over-time functions aggregate the samples of each individual series across a range window. They complement the aggregation operators, which combine values across series at one instant — fleet summaries typically use both.
absent_over_time
Returns a single series with value 1 when the selector matched no samples anywhere in the range window, and nothing otherwise. The windowed form of absent, and the standard silence detector for alerting.
Syntax
absent_over_time(<metric>{<matchers>}[<range>])
Parameters
| Parameter | Required | Description |
|---|---|---|
<range> |
Required | The silence window, such as [10m]. |
absent_over_time does not support the subquery form. It requires a raw metric selector, not a computed expression. |
Example
Check for samples of a metric that does not exist on this cluster — the result is 1, the signal an alert would fire on.
absent_over_time(nonexistent_demo_metric{app_kubernetes_io_instance="kfuse"}[10m])
| app_kubernetes_io_instance | Value |
|---|---|
| kfuse | 1 |
Expected output
| When samples exist the result is empty — dashboards show no data and alert rules stay quiet. |
avg_over_time
Averages the samples of each series within the range window — the standard smoothing wrapper for noisy gauges.
Syntax
avg_over_time(<metric>[<range>])
avg_over_time(<expr>[<range>:<step>])
The second form is a subquery: <expr> is any instant-vector PromQL expression evaluated at every <step> interval over the trailing <range> window. Use it when there is no raw metric to pass directly — for example, to smooth a rate() result.
Parameters
| Parameter | Required | Description |
|---|---|---|
<range> |
Required | The trailing window to aggregate over. |
<step> |
Subquery only | The evaluation interval within the subquery window. Defaults to the global evaluation interval when omitted (written as [<range>:]). |
Example
Apply avg_over_time to the query-service goroutine gauge over the last ten minutes, then average across pods for a single row.
avg by (app_kubernetes_io_name) (avg_over_time(go_goroutines{app_kubernetes_io_name="query-service"}[10m]))
| app_kubernetes_io_name | Value |
|---|---|
| query-service | 313.15 |
Expected output
Over-time functions aggregate one series across time; the aggregation operators (sum, avg, …) aggregate across series at one instant. Combine both for fleet summaries. |
count_over_time
Counts the samples of each series within the range window — effectively a scrape-health measure.
Syntax
count_over_time(<metric>[<range>])
count_over_time(<expr>[<range>:<step>])
The second form is a subquery: <expr> is any instant-vector PromQL expression evaluated at every <step> interval over the trailing <range> window.
Parameters
Example
Apply count_over_time to the query-service goroutine gauge over the last ten minutes, then average across pods for a single row.
avg by (app_kubernetes_io_name) (count_over_time(go_goroutines{app_kubernetes_io_name="query-service"}[10m]))
| app_kubernetes_io_name | Value |
|---|---|
| query-service | 19.89 |
Expected output
last_over_time
Returns the newest sample of each series within the range window — the right reader for sparsely reported gauges.
Syntax
last_over_time(<metric>[<range>])
last_over_time(<expr>[<range>:<step>])
The second form is a subquery: <expr> is any instant-vector PromQL expression evaluated at every <step> interval over the trailing <range> window.
Parameters
Example
Apply last_over_time to the query-service goroutine gauge over the last ten minutes, then average across pods for a single row.
avg by (app_kubernetes_io_name) (last_over_time(go_goroutines{app_kubernetes_io_name="query-service"}[10m]))
| app_kubernetes_io_name | Value |
|---|---|
| query-service | 313.16 |
Expected output
max_over_time
Returns the largest sample of each series within the range window — peaks that a downsampled chart would hide.
Syntax
max_over_time(<metric>[<range>])
max_over_time(<expr>[<range>:<step>])
The second form is a subquery: <expr> is any instant-vector PromQL expression evaluated at every <step> interval over the trailing <range> window.
Parameters
Example
Apply max_over_time to the query-service goroutine gauge over the last ten minutes, then average across pods for a single row.
avg by (app_kubernetes_io_name) (max_over_time(go_goroutines{app_kubernetes_io_name="query-service"}[10m]))
| app_kubernetes_io_name | Value |
|---|---|
| query-service | 316.70 |
Expected output
min_over_time
Returns the smallest sample of each series within the range window.
Syntax
min_over_time(<metric>[<range>])
min_over_time(<expr>[<range>:<step>])
The second form is a subquery: <expr> is any instant-vector PromQL expression evaluated at every <step> interval over the trailing <range> window.
Parameters
Example
Apply min_over_time to the query-service goroutine gauge over the last ten minutes, then average across pods for a single row.
avg by (app_kubernetes_io_name) (min_over_time(go_goroutines{app_kubernetes_io_name="query-service"}[10m]))
| app_kubernetes_io_name | Value |
|---|---|
| query-service | 310.97 |
Expected output
present_over_time
Returns 1 for each series that has at least one sample within the range window — existence without caring about values.
Syntax
present_over_time(<metric>[<range>])
Parameters
| Parameter | Required | Description |
|---|---|---|
<range> |
Required | The trailing window to aggregate over. |
present_over_time does not support the subquery form. It requires a raw metric selector, not a computed expression. |
Example
Apply present_over_time to the query-service goroutine gauge over the last ten minutes, then average across pods for a single row.
avg by (app_kubernetes_io_name) (present_over_time(go_goroutines{app_kubernetes_io_name="query-service"}[10m]))
| app_kubernetes_io_name | Value |
|---|---|
| query-service | 1 |
Expected output
quantile_over_time
Computes the given quantile (0 to 1) of each series' samples within the range window.
Syntax
quantile_over_time(<q>, <metric>[<range>])
quantile_over_time(<q>, <expr>[<range>:<step>])
The second form is a subquery: <expr> is any instant-vector PromQL expression evaluated at every <step> interval over the trailing <range> window.
Parameters
| Parameter | Required | Description |
|---|---|---|
<q> |
Required | The quantile, between 0 and 1. |
<range> |
Required | The trailing window to aggregate over. |
<step> |
Subquery only | The evaluation interval within the subquery window. Defaults to the global evaluation interval when omitted (written as [<range>:]). |
Example
Apply quantile_over_time to the query-service goroutine gauge over the last ten minutes, then average across pods for a single row.
avg by (app_kubernetes_io_name) (quantile_over_time(0.95, go_goroutines{app_kubernetes_io_name="query-service"}[10m]))
| app_kubernetes_io_name | Value |
|---|---|
| query-service | 313.15 |
Expected output
stddev_over_time
Computes the population standard deviation of each series' samples within the range window — how erratic the value is over time.
Syntax
stddev_over_time(<metric>[<range>])
stddev_over_time(<expr>[<range>:<step>])
The second form is a subquery: <expr> is any instant-vector PromQL expression evaluated at every <step> interval over the trailing <range> window.
Parameters
Example
Apply stddev_over_time to the query-service goroutine gauge over the last ten minutes, then average across pods for a single row.
avg by (app_kubernetes_io_name) (stddev_over_time(go_goroutines{app_kubernetes_io_name="query-service"}[10m]))
| app_kubernetes_io_name | Value |
|---|---|
| query-service | 0 |
Expected output
stdvar_over_time
Computes the population variance of each series' samples within the range window — the square of stddev_over_time.
Syntax
stdvar_over_time(<metric>[<range>])
stdvar_over_time(<expr>[<range>:<step>])
The second form is a subquery: <expr> is any instant-vector PromQL expression evaluated at every <step> interval over the trailing <range> window.
Parameters
Example
Apply stdvar_over_time to the query-service goroutine gauge over the last ten minutes, then average across pods for a single row.
avg by (app_kubernetes_io_name) (stdvar_over_time(go_goroutines{app_kubernetes_io_name="query-service"}[10m]))
| app_kubernetes_io_name | Value |
|---|---|
| query-service | 0 |
Expected output
sum_over_time
Adds up all samples of each series within the range window. Meaningful for quantities where samples accumulate; for counters, use increase instead.
Syntax
sum_over_time(<metric>[<range>])
sum_over_time(<expr>[<range>:<step>])
The second form is a subquery: <expr> is any instant-vector PromQL expression evaluated at every <step> interval over the trailing <range> window.
Parameters
Example
Apply sum_over_time to the query-service goroutine gauge over the last ten minutes, then average across pods for a single row.
avg by (app_kubernetes_io_name) (sum_over_time(go_goroutines{app_kubernetes_io_name="query-service"}[10m]))
| app_kubernetes_io_name | Value |
|---|---|
| query-service | 6,259.19 |
Expected output
Over-time functions aggregate one series across time; the aggregation operators (sum, avg, …) aggregate across series at one instant. Combine both for fleet summaries. |