Prometheus onboarding

February 27, 2023

Prometheus is an open-source systems monitoring and alerting toolkit. It collects and stores its metrics as time series data, i.e. metrics information is stored with the timestamp at which it was recorded, alongside optional key-value pairs called labels.


I’m only covering Prometheus from an end-user perspective. This means we will investigate concepts related to metrics and querying while completely skipping infrastructure topics like installation, configuration or instrumentation.


Recently I’ve been migrating dashboards from Datadog to Prometheus, the goal of this post is to showcase concepts that could have helped me understand how to better query and represent metrics in Prometheus.

Queries and Vectors

Having grasped the terminology early on would have helped me understand why certain queries were not working or returning unexpected results.

  • query or range_query are API calls fetching data
  • instant vector or range vector is the shape of the data returned by your queries

In Prometheus we have one of the following combinations:

Query returning an instant vector

This is a query that returns a single vector containing a timestamp and the value associated with this timestamp for each combination of labels we are querying for.

Suppose we have a counter (some value that can only go up) called products_sold_total and that we have two distinct instances in our application. Querying for the following:


Would produce something like:

Name Counter
products_sold_total{“instance”=“a”} 1000
products_sold_total{“instance”=“b”} 834

Query returning a vector of instant vectors

You can think of this combination as running a query that returns an instant vector multiple times in distinct timestamps.

This result can be graphed since Prometheus can use each value per timestamp.

Query returning a range vector

This is a query that returns a vector containing multiple timestamps with its associated values. It is important to mention that these events are still nested under a single timestamp. This is the return of a query like:


The value [1m] means that we want all events happening in the range timestamp + 1m. Let’s see what this would return:

Name Counter
products_sold_total{“instance”=“a”} [1000@1677381615,
products_sold_total{“instance”=“b”} [834@1677381615,

Where the counter is represented as [<value_1>@<timestamp_1>, ..., <value_n>@<timestamp_n>]. In other words, it is now a vector containing a range of events.

Query returning a vector of range vectors

You can think of this combination as running a query that returns a range vector multiple times in distinct timestamps.

We cannot graph this result since Prometheus cannot use multiple values for a single timestamp. Remember that every range vector is still nested under a timestamp.

If you are a visual person, suppose our range is [3s] and we are starting on T1 (timestamp at second 1), this is how I visualize the data:

[ T1-3: [event_1, event_2, ..., event_n], # Where T1-3 represents the timestamp from second 1 to 3 T2-4: [event_1, event_2, ..., event_n], # Where T2-4 represents the timestamp from second 2 to 4 T3-5: [event_1, event_2, ..., event_n], # ... T4-6: [event_1, event_2, ..., event_n] # ... ]

So we have a vector of range vector elements.

What can we do in this case to graph results?

We would need to use a function like rate to aggregate this range of events into an average. This would result in a single timestamp containing a single value symbolizing the growth rate of our counter under that particular time frame. In summary, we transformed our result into multiple instant vectors.

Which range should I use?

  1. We are mostly interested in using $__rate_interval when dealing with the rate() function.
  2. We are mostly interested in using $__range when displaying a simple counter (no graphs).
    • It will provide a single range vector containing all the events for the time window we are inspecting.

What about $__interval? I’m mostly ignoring it for now since $__rate_interval seems to cover everything $__interval does and also addresses some downsides of using it.

I highly recommend this blog post for this particular topic.

Mental model for queries

The way I’m currently thinking about queries is by using the following mental model:

  • [] ([1m], [10m], ..) affects the API call that will return the data
  • {} (products_sold_total{instance="a"}) filters the data returned by our API call
  • Functions like rate() or sum() modifies the data returned by our API call

Knowing that, these are the troubleshooting steps I follow:

  1. Is my query returning the expected data?
    • If not, then I need to play with the [] and the time window I’m inspecting
    • If yes, go to the next step
  2. The data is correct, but my graph is not showing the expected result
    • Play with the {} to make sure I’m filtering the data correctly
    • Investigate the functions I’m using to make sure they are returning the expected result

Scrape interval, Interval, Steps

This video is a great intro into the subject and also does an amazing job at explaining rates, queries and pretty much every subject that this post mentioned so far.

Sum of rate

And by this I’m talking about a query like the following:


Suppose our web application has three different instances, each with a distinct rate:

Name Rate
products_sold_total{“instance”=“a”} 30.2
products_sold_total{“instance”=“b”} 11.7
products_sold_total{“instance”=“c”} 20.1

sum() will merge these into a single rate and produce the total: 30.2 + 11.7 + 20.1.

If our metric had multiple dimensions (labels) we could tell sum() to operate in a subset of these dimensions. Let’s say we have multiple flows to sell a product and these were represented in our metrics with the label flow. We could do:

sum by (flow) (rate(products_sold[1m]))

And this would sum() the rate value of distinct flows while aggregating every other dimension (label).

The aggregation operators section helped me with the syntax and examples.

Metric types

One of the first things I should have done is investigate the characteristics of each metric type in depth. There are not too many, but each one has their own characteristics and constraints. Let’s see some problems that I’ve faced due to my lack of understanding of some of these metric types.


It can produce decimal numbers for slow counters (not too many events) under a time range since Prometheus extrapolates these events to smooth things out and produce the correct result in aggregate. See this issue for more context.


We can use a gauge metric type to count things, but since it can go up or down we cannot use functions like rate() or increase().


Before anything, please make sure your buckets are configured according to the values your application expects. For example, if your application makes HTTP requests to an API and they usually take 200ms, having buckets that starts at 300ms will make your metrics mostly useless since 99% of your requests will be part of the first bucket, making it impossible to measure the true pX (p50, p95, p99. ..) values.

Histogram’s in Prometheus are cumulative. Suppose we had a histogram like the following:

Bucket Count
100ms 5
200ms 3
300ms 10

In Prometheus this is represented as:

Bucket Count
100ms 5
200ms 8
300ms 23

The 200ms bucket will hold values from its own bucket + the 100ms bucket, while the 300ms bucket will hold values from its own bucket + every other bucket lower than it. This can be rally confusing at first when we are just starting to build our dashboards.

Anything else?

It’s still early days in my Prometheus journey, if I’m misinterpreting things or missing crucial information please reach out to me and I will gladly adjust this post. I would also appreciate if you share resources that helped you along your journey, there’s also something new to be learned!

Bernardo de Araujo

Application Security Manager @Shopify.

© Bernardo de Araujo 2023