I'm familiar with the architecture etc, and am currently interested in the querying side of things. So here are my notes from reading Prometheus: Up & Running by Brian Brazil.

Basic queries

Use rate for counters, e.g. rate(prometheus_blah[1m])

Curly braces for labels i.e. tags, e.g. process_resident_memory_bytes{job="node"}

Basic alerting

up == 0 returns only results where the condition matches. You can set this as an alert if it happens over a particular duration, e.g 1m.

Basic calulations



Counters generally end with _total.

Strictly avoid ending anything with these:

  • _count and _sum: these are for Summaries.
  • _bucket which is for histograms.

Use the unit in the name, e.g. myapp_requests_processed_bytes_total.


A Summary has an observe method to which you pass a non-negative size. E.g.

LATENCY = prometheus_client.Summary('hello_world_latency_seconds', 'Time for a request Hello World.')

class blah
  def get(self):
  start = time.time()
  # do stuff here
  LATENCY.observe(time.time() - start)

Now the /metrics endpoint will show hello_world_latency_seconds containing a hello_world_latency_seconds_count and a hello_world_latency_seconds_sum. The former is the number of observe() calls made, the latter is the sum of the values passed.

So average latency over the last minute would be:


Here the numerator gives you total latency in that duration (say, 5s, 10s, 15s) and the denominator gives you your requests count (1, 1, 1 => 3). So the answer would be 30/3 i.e. 10s in this case. This is the average request latency in this window.

Simplify all this in the code by just using the @LATENCY.time() decorator.


Again you would use an observe method but here you would get quantiles like p95. Using this on a metric hello_world_latency_seconds would yield a hello_world_latency_seconds_bucket, which are a set of counters. Use a query like this to extract data out of it:

histogram_quantile(0.95, rate(hello_world_latency_seconds_bucket[1m]))

Default buckets cover latencies from 1ms to 10s. But you can create your own. e.g. A more interesting query:

/ ignoring(le)

What this does is: if you have a 500ms bucket in your histogram, show all requests that were below/before that bucket and divide by count of requests that were in the remaining buckets. le is a bucket label here.


No further calculations can happen on a quantile, like sum or avg.


increase is just syntactic sugar, and displays the rate * range where the range is something like [5m].

rate is for counters, and if your instance restarts, rate will automatically account for it. So metrics like 5, 7, 12, , 3, 6... will be interpreted as 5, 7, 12, 15, 18...

resets counts the number of times this has happened, so is helpful to detect the number of times your process has restarted. E.g. resets(process_cpu_seconds_total[1h])

Similarly changes tells you how often a gauge changed, and is useful for gauges that don't change very often.

Recording Rules

A way to run queries periodically. Helps to speed up dashboards or use the results elsewhere. Useful to reduce cardinality: when you have a slow query, you can split it up and use a recording rule. Prometheus will output a new metric that you can use in the outer query.