(Part 0) SLO Implementation: Overview

(Part 0) SLO Implementation: Overview

My last two blog posts enumerated this blog’s SLO and error budget. Our next logical step is adding the monitoring and alerting infrastructure which will transform our SLO usage from theoretical to practical. Like creating a Kubernetes of One’s Own, this project contains multiple steps which we’ll explore over multiple blog posts. While this series focuses on achieving this goal for this blog’s specific SLO, the techniques are applicable to many scenarios.

Our goal for this project is to create the monitoring infrastructure which will allow us to monitor metrics pertaining to our SLO, and alert me when this blog is violating its SLO (i.e. spent its error budget).

You can find the most recent version of the SLO on our SLO page.

What functionality do we need to accomplish this goal?


For us to succeed in monitoring and alerting on our SLO, we need a couple different components (and of course some pie).

First, we must track the application level metrics pertaining to our SLO. Remember, our SLO focuses on availability and latency. We measure availability via the status code returned by the web server and measure latency by the web server’s response time. In order for us to have any hope of monitoring our SLO, our application must make these metrics accessible.

Second, we need a method of aggregating, storing, and querying metrics. We need the ability to aggregate metrics because we define an application level SLO, and there may be multiple instances of our application, all of which are providing application level metrics. Only when we examine all the metrics together can we know if our service is meeting its SLO. We need the ability to store metrics because we calculate our SLO over a four-week rolling window, and also want the ability to compare current performance to historical performance. Finally, we need the ability to query our metrics because the SLI’s constructing our SLO are (relatively) complex. We need to calculate latency percentiles, alert on error budget burn down, etc. An sufficiently expressive querying language will make writing these calculations more pleasant.

Third, we need the ability to specify and manage alerts. Alert specification involves defining queries, and the query results for which we should alert, as well as running these alerts at a regular cadence. Alert management transforms alerts into notifications in an intelligent way. Essentially, its responsible for ensuring we get paged once, not a thousand times, for the same error.

Finally, while not a strict requirement for monitoring and alerting on our SLO, we want to create easily consumable dashboards tracking our SLIs and the percentage of error budget that we’ve spent. I will use these dashboards for snapshots of system health and comparing current performance with historical performance. I’ll also make them available on the SLO page, so y’all can see the exact same metrics I do regarding SLO performance.

What tools provide this functionality?


Our needs for monitoring and alerting around this application’s SLO are not unique. As a result, a number of excellent open-source technologies exist which can fulfill my use case.

We particularly focus on Prometheus, an open source, metrics-based monitoring system developed by the good folks at SoundCloud. Prometheus was based on Borgmon, which was Google’s internal metrics-based monitoring system. Prometheus has seen considerable adoption in the cloud native ecosystem, and is the only project other than Kubernetes to graduate from the Cloud Native Computing Foundation. Tl:dr; a ton of developers are choosing Prometheus, and they are pretty darn happy with their decision.

Prometheus, and its related ecosystem, provide all of the desired functionality that I enumerated earlier. From Brian Brazil’s excellent Prometheus: Up and Running, “Prometheus discovers targets to scrape from service discovery. These can be your own instrumented applications or third-party applications you scrape via an exporter. The scraped data is stored, and you can use it in dashboards using PromQL or send alerts to the AlertManager, which will convert them into pages, emails, and other notifications.” To unpack, Prometheus meets our first need by defining an standard interface with which applications can export metrics for Prometheus to pull in. It meets our second need by aggregating and storing all of the different metrics sources it scrapes, and providing a query language, PromQL. Prometheus meets our third need via AlertManager, which can aggregate our alerts into whatever notification form we desire. Finally, Prometheus provides a lightweight dashboard for exploring metrics. It also has first class support the much more powerful Grafana dashboard. In short, Prometheus provides everything we need and has a proven track record in the Cloud Native ecosystem.

What can we look forward to?


The next couple of blog posts in this series will walk through accomplishing our stated objectives using Prometheus and its related tools.

The first post will explore configuring our blog to expose the metrics Prometheus needs to scrape in order to monitor our service level indicators. The next post will discuss deploying Prometheus on our Kubernetes cluster, and configuring our Prometheus instance to scrape metrics from our blog. The following post will examine using Grafana to visualize our service level indicators and error budget. Finally, the last post will explore setting up alerts and notifications via AlertManager.

Looking forward to exploring this together :) Happy monitoring!

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