PromQL

Selecting series

Select latest sample for series with a given metric name:

node_cpu_seconds_total

Select 5-minute range of samples for series with a given metric name:

node_cpu_seconds_total[5m]

Only series with given label values:

node_cpu_seconds_total{cpu="0",mode="idle"}

Select data from one day ago and shift it to the current time:

process_resident_memory_bytes offset 1d

Per-second rate of increase, averaged over last 5 minutes:

rate(demo_api_request_duration_seconds_count[5m])

Per-second rate of increase, calculated over last two samples in a 1-minute time window:

irate(demo_api_request_duration_seconds_count[1m])

Absolute increase over last hour:

increase(demo_api_request_duration_seconds_count[1h])

Sum over all series:

sum(node_filesystem_size_bytes)

Preserve the instance and job label dimensions:

sum by(job, instance) (node_filesystem_size_bytes)

Aggregate away the instance and job label dimensions:

sum without(instance, job) (node_filesystem_size_bytes)

Average within each series over a 5-minute period:

avg_over_time(go_goroutines[5m])

Get the maximum for each series over a one-day period:

max_over_time(process_resident_memory_bytes[1d])

Count the number of samples for each series over a 5-minute period:

count_over_time(process_resident_memory_bytes[5m])

Get the age of the last successful batch job run:

time() - demo_batch_last_success_timestamp_seconds

Find batch jobs which haven't succeeded in an hour:

time() - demo_batch_last_success_timestamp_seconds > 3600

Calculate the 5-minute-averaged rate over a 1-hour period, at the default subquery resolution (= global rule evaluation interval):

rate(demo_api_request_duration_seconds_count[5m])[1h:]

Calculate the 5-minute-averaged rate over a 1-hour period, at a 15-second subquery resolution:

rate(demo_api_request_duration_seconds_count[5m])[1h:15s]

Using the subquery result to get the maximum rate over a 1-hour period:

max_over_time(
  rate(
    demo_api_request_duration_seconds_count[5m]
  )[1h:]
)

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