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Using Expression browser in Prometheus

September 28, 2021 comments off

The expression browser is available at /graph on the Prometheus server, allowing you to enter any expression and see its result either in a table or graphed over time.

This is primarily useful for ad-hoc queries and debugging. For graphs, use Grafana or Console templates.

Using the expression browser

As you can gather from localhost:9090/metrics, one metric that Prometheus exports about itself is named prometheus_target_interval_length_seconds (the actual amount of time between target scrapes). Enter the below into the expression console and then click "Execute":

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prometheus_target_interval_length_seconds
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This should return a number of different time series (along with the latest value recorded for each), each with the metric name prometheus_target_interval_length_seconds, but with different labels. These labels designate different latency percentiles and target group intervals.

If we are interested only in 99th percentile latencies, we could use this query:

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prometheus_target_interval_length_seconds{quantile="0.99"}
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To count the number of returned time series, you could write:

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count(prometheus_target_interval_length_seconds)
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Using the graphing interface

To graph expressions, navigate to http://localhost:9090/graph and use the "Graph" tab.

For example, enter the following expression to graph the per-second rate of chunks being created in the self-scraped Prometheus:

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rate(prometheus_tsdb_head_chunks_created_total[1m])
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Experiment with the graph range parameters and other settings.

Configure Prometheus to monitor the sample targets such as Node Exporter

Install and Configure Prometheus Server and Node Exporter in RHEL

Example queries that aggregate over thousands

Though not a problem in our example, queries that aggregate over thousands of time series can get slow when computed ad-hoc. To make this more efficient, Prometheus can prerecord expressions into new persisted time series via configured recording rules. Let's say we are interested in recording the per-second rate of cpu time (node_cpu_seconds_total) averaged over all cpus per instance (but preserving the job, instance and mode dimensions) as measured over a window of 5 minutes. We could write this as:

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avg by (job, instance, mode) (rate(node_cpu_seconds_total[5m]))
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Rajesh Kumar
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