$ cat post/the-buffer-overflowed-/-we-named-the-server-badly-then-/-it-was-in-the-logs.md
the buffer overflowed / we named the server badly then / it was in the logs
Title: 2018 Year in Review: When Everything Was Possible
December 31, 2018. Another year of endless possibilities and frustrating trade-offs. As I look back at the year that was, I can’t help but reflect on the tech industry’s relentless push towards automation and the constant stream of new tools trying to make our lives easier—or at least more productive.
The “serverless” hype hit its peak this year, with everyone from startups to established enterprises eager to embrace the promise of infinite scalability without the pain of ops. But serverless isn’t always as straightforward as it seems. I spent a lot of time debugging functions that mysteriously failed and trying to find out why they were slow or even outright broken. The reality is, under the hood, it’s still just infrastructure with an abstraction layer—and sometimes that abstraction can be more trouble than it’s worth.
In my day-to-day work, Kubernetes continued its march towards becoming the default for container orchestration. We migrated a few of our services to Kubernetes clusters and while it was exciting, the setup and management were far from simple. Helm, Istio, Envoy, and all those other tools added layers of complexity that seemed unnecessary at times. But as the year progressed, I began to see how they could help us manage and secure our containerized applications more effectively.
Platform engineering conversations started gaining momentum too. The idea of a platform team building out shared infrastructure for developers was becoming mainstream. We debated whether our platform should be monolithic or microservices-driven, and ultimately decided on a hybrid approach. It was a compromise, but one that allowed us to scale while maintaining the agility we needed.
One day, I found myself arguing with a colleague about the merits of Postgres versus MongoDB. “Postgres is just an old-fashioned RDBMS,” he said dismissively. But as we delved into our growing user data, I saw the value in using a robust SQL database. We ended up going hybrid—some tables for historical analytics, others for real-time tracking—and it worked surprisingly well.
Security concerns were at the forefront of everyone’s mind too. The Quora User Data Compromised story hit hard. We had to scramble to implement better security practices and audit our systems more closely. It was a sobering reminder that no matter how much we automate, human error can still lead to major issues.
Platform as Code (PaaS) tools like Terraform 0.x started gaining traction, but they were still a bit rough around the edges. We used them for some projects but found ourselves often having to write custom scripts and workarounds to get things done. GitOps was starting to take shape, promising consistency across environments, but it felt more like an aspirational goal than something we could achieve in practice.
Debugging network issues became a bit of a specialty this year. We started using Envoy for service mesh and Prometheus + Grafana for monitoring, which helped us catch performance bottlenecks earlier. But sometimes, the root cause was still elusive—like that one day when every request to our API timed out, only to find out it was because of a misconfigured DNS record.
Looking back on 2018, I can see how much tech evolved and how many new tools emerged. But more than anything, I remember the constant tension between embracing change and maintaining stability. It’s a delicate balance, and one that will likely continue to challenge us in the years to come.
As we say goodbye to 2018 and hello to 2019, let’s be mindful of how much technology can improve our lives while not losing sight of the basics—like good old-fashioned debugging. Here’s to another year of learning, growing, and pushing the boundaries of what’s possible in tech.
Happy New Year!