Observability In a Crime Scene

Photo by Mediamodifier on Unsplash

Andy Smith

June 09, 2023

Working in software can sometimes feel like working in a crime scene.

There are no murders, but quite often there’s plenty of dead code and criminal practices. We often end up investigating problems like a detective, having a theory and gradually eliminating suspects from the investigation until we find the culprit.

On a recent project, I was faced with a defect of mysterious origins that required a detective’s eye to piece together.

Working as a Site Reliability Engineer (SRE) requires detective skills, and this scenario was like an episode of Columbo. In that TV show, we see the killer in the act right at the start of the episode, and Columbo spends the entire time pestering the killer until he gathers enough evidence to arrest them — using his trusty tools of a notebook, pen, and cigar.

Like Lieutenant Columbo, in this entire episode we had a suspect (or a hypothesis), and we attempted to prove or disprove that hypothesis using our trusty tools of Service Level Objectives (SLOs), Distributed Tracing, and Metrics.

Exhibit A: Service Level Objectives

The system we were working on was a microservice architecture, and the perception of how this system was performing was not great. “Not great” and “perception” are not very scientific ways of knowing whether something is reliable or not. Service Level Objectives are a scientific way of knowing if something is reliable or not, and fortunately we had some implemented. The SLO in question was of the form:

  • “99% of all requests should return in 5 seconds or less”

This meant our error budget allowed for 1 percent of all requests to take longer than 5 seconds or not return at all.

The tool we were using to visualise SLOs (Nobl9) provides a nice graph that shows when we are using our error budget. As part of a daily ritual, the team looked at these graphs and could see that over time the latency was getting longer, breaching the SLO that we’d agreed on with the development team.

An example of an SLO graph is shown below (this is not the actual graph used in the investigation. It’s been redacted to protect its identity).

Observability 1

The example above shows the latencies, and the pink line denotes 5 seconds. Anything above that pink line starts to eat into your error budget.

As a reminder, the rule or guideline for error budgets is:

If there’s error budget left then
	Release new features
	Focus on reliability


In our case, this meant the team should be focusing on reliability.

Exhibit B: Distributed Tracing for Observability

“Traces help you understand system interdependencies. Those interdependencies can obscure problems and make them particularly difficult to debug unless the relationship between them are clearly understood.”
— Observability Engineering by Charity Majors, Liz Fong-Jones, and George Miranda

As the system comprised many microservices, tracking down the problem would have been quite difficult if we had to trawl through multiple log files, which is a laborious process in the best of times. Tracing is one of the tools described in Observability Engineering, and fortunately distributed tracing was implemented across the system — providing us with observability into the interdependencies, and clues leading closer to the culprit. Traces were sent to our observability tool of choice, Lightstep (shown below).

Observability 2

The graph above shows response times of requests to the service that the SLO highlighted as being an issue. The advantage of the tool is that it allows us to click on a data point on that graph and get a trace of all the calls behind this request. We clicked on a point with a large latency (one of the large spikes in the graph), and this displayed the call stack between all the microservices and the time spent in each one. In this case we could see that on a particular span, it was taking around 60 seconds on the client side, but on the other end of the HTTP request it was only taking a few milliseconds. An example distributed trace is shown below (the real one has been redacted to protect its identity):

Observability 3
Side note: Also of particular interest in the trace was at the top, which was the front-end component. This was timing-out at 15 seconds, but in all the services below, the timeout was much greater. Interesting and definitely an issue, and wouldn’t be helping stability in this case. As Sam Newman says in Building Microservices: “Timeouts are .. easy to overlook, but in a downstream system they are important to get right.”

My team was developing a hypothesis that it was trying to acquire a HTTP connection, and for some reason this was taking some time. Opening sockets are relatively expensive operations, so quite often connection pools are used to hold a number of already created socket connections that can be leased out to a client wanting to make a request. This is to help with performance. Our theory was that there were maybe not enough connections in the pool to make the request — but of course we needed evidence. We needed to see if the pool had no free connections.

Exhibit C: Metrics

We added Prometheus metrics to monitor the connection pool, such as “number of free connections,” “max number of connections,” and “number of pending connections;” and then we deployed to production to gather evidence to prove or disprove our theory.

After a day of it being deployed to production, we graphed our metrics over 24 hours and compared it to our Lightstep graph. Bingo! We found our smoking gun (we could see a correlation).

Observability 4

In Exhibit B we can see latency spikes at exactly the same time as the spikes in Exhibit C (the graph of pending connections). There were no free connections in the pool to make the requests, and instead the requests would wait until a connection was free. Now that we had our evidence, we could make a simple fix, which was to increase the size of the connection pool.

We made our change, deployed the fix, and again monitored our graphs. Hurrah, the latency is gone. Columbo always catches the killer!


SLOs are the first line of defense in knowing when reliability is getting worse. In our case it showed large latencies that gave us more information to investigate further.

Distributed tracing is cool. It allowed us to track down the exact function in Lightstep that was causing the latency in minutes with laser sharp focus. It also gave a clue as to what the issue may have been.

Even in the days of increased web performance with HTTP/2, connection pools are still vitally important. You still need them! Don’t go with default settings for them, as they’re more than likely wrong for your scenario. Make sure you set the number of connections to a sensible number. Use load testing if necessary to work out what this figure should be.

Timeouts are important. Again, do not go with the defaults. If the frontend times out after 15 seconds, does it make sense for downstream components to timeout in 60 seconds? Do your research, talk to owners of other components.

Just One More Thing ...

Fixing microservice architectures can be like a game of Whac-A-Mole. Fix one issue and another one pops up. But like the great Columbo, in every episode after the next, with enough persistent pestering, the Site Reliability Engineer will always catch the next criminal (code).

Andy Smith

Lead Crafter

Andy Smith first cut his teeth on a Commodore 64 and had a game published. Certain fruity companies would call the game "revolutionary," others would call it a "shoot em-up." He has been tinkering with computers ever since.