Title: Microservices Meltdown: A Day in the Life of a Platform Engineer

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It’s been a while since I’ve written something personal about my work. Lately, it feels like we’re all stuck in a perpetual state of firefighting with microservices and container orchestration, but today felt different. We finally got Kubernetes set up, and things were running smoothly for once. But then… it fell apart.

Today started off as usual: sipping coffee while checking the status of our services on CoreOS clusters. I’ve grown used to seeing red indicators signaling containers that have failed or are in a weird state, but today they seemed unusually numerous. “Must be some network issue,” I thought. I checked etcd and fleet, both seemed fine, so I went through each service one by one.

Service A was misbehaving. It’s an API gateway we use to route requests between services. Suddenly, the logs were flooded with 502 errors from Nginx. “Hmm, maybe a configuration issue,” I mused and rolled up my sleeves. After a few minutes of digging through the nginx.conf file, I found the culprit: one of our upstream servers had died.

I killed the dying container and started it back up again. The service recovered quickly, but the 502 errors persisted. I checked the network traffic with tcpdump to see if anything strange was happening. Turns out, some of the requests were timing out. I went through each downstream service to see which ones might be causing this.

Service B stood out as it had recently been rewritten in Go and deployed with Docker Compose. It seemed to be under heavy load. I checked the metrics on CoreOS’s built-in statsd service and noticed that Service B was indeed hitting 100% CPU usage during peak hours. “Time for some profiling,” I thought, and started using gopprof to analyze the bottleneck.

As I was analyzing the Go code, I received a message from our DevOps team: “Hey, we’re getting issues with service C too.” This one was a bit more complex as it relied on Service A for authentication. I quickly jumped into Service C’s logs and found that Nginx was still timing out requests to Service B.

It dawned on me that there might be an issue in the way we were handling retries and timeouts across services. I opened up the 12-factor app documentation, reminding myself of best practices for dealing with service-to-service communication. After a few iterations, I managed to tweak the retry logic and reduced the timeout values.

Just as I was about to take a break from my marathon coding session, I received an alert: “Service D is down!” This one was unexpected as it hadn’t been used in a while. Checking the logs revealed that Service A was still routing requests to Service D despite its absence in our config files. It turned out we had forgotten to remove a stale entry.

After fixing this last issue, I sat back and took stock of what I’d done today: debugged network issues, optimized Go code for CPU usage, fine-tuned service-to-service communication logic, and cleaned up old entries in our configuration. Each step required different tools and a deep understanding of the systems we’re working with.

As the day wound down, I realized that while microservices have their challenges, they also offer incredible flexibility and resilience. Today’s issues were no exception—every time one service went down, it cascaded into others, but by breaking things down to their smallest components, we can isolate problems more easily.

I ended my shift feeling satisfied yet a bit exhausted. It was another day in the life of a platform engineer, full of surprises and challenges. But with tools like Kubernetes, Docker, etcd, and Nginx, I’m starting to feel like I have the right set of weapons at hand to take on whatever comes next.

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This blog post aims to capture a real workday experience as a platform engineer dealing with microservices architecture issues, using specific technologies and events from that era.