Operations | Monitoring | ITSM | DevOps | Cloud

Choosing the wrong metric type in Prometheus can lead to inaccurate dashboards, false positives in alerting, and missed indicators of system failure. Gauge metrics are intended for tracking values that can go up and down, such as memory usage, queue depth, or the number of active connections. Unlike counters, which only increment (or reset on restart), gauges reflect the current state of a resource at scrape time.

As Site Reliability Engineers (SREs) managing global infrastructure, we face unique challenges when serving users in mainland China. The Great Firewall of China creates a complex web of technical obstacles that can render even the most robust international websites slow, unreliable, or completely inaccessible to Chinese users.

The Prometheus CloudWatch exporter pulls AWS CloudWatch metrics into your Prometheus setup, giving you a unified view of your infrastructure alongside application metrics. If you're already running Prometheus and need visibility into AWS services like EC2, RDS, or Lambda, this exporter handles the integration without forcing you to switch monitoring stacks.

Message queues quietly take care of a lot—buffering workloads, smoothing traffic spikes, and keeping services connected. But they don’t always get much attention until something feels off. Amazon SQS offers a solid set of metrics to help you understand how your queues are doing, whether you’re scaling well or nearing limits. This blog breaks down the key SQS metrics: where to find them, what they mean, and how to respond when things start to shift.

Your Node.js app runs fine on your machine. But inside Docker? You start getting weird crashes—ENOSPC: no space left on device. Chrome headless tests fail out of nowhere. PostgreSQL throws shared memory errors under load. The problem? It’s probably /dev/shm, the shared memory volume Docker sets up by default. Most containers get just 64MB of space here.

Your checkout API just started throwing 500s during peak traffic. You SSH into production, tail logs across six microservices, and realize the database timeout buried in service's logs is causing cascade failures. Two hours later, you've fixed it, but you're thinking: "There has to be a better way." There is. Log monitoring tools centralize logs from your entire stack, making debugging systematic instead of archaeological.

Running apps in production? You need visibility fast. Traditional logging gives you scattered events. Prometheus gives you structured, queryable data that scales. In this guide, we’ll break down how to use Prometheus for logging-style observability, where it fits in your stack, and how to plug it into tools like Grafana or your cloud-native setup.

When a container starts consuming excessive memory or becomes unresponsive, you need a way to shut it down. The two primary options — docker stop and docker kill,both terminate containers, but they operate differently and have different implications. The key difference: docker stop sends SIGTERM for a graceful shutdown, then escalates to SIGKILL if the process doesn’t exit in time. docker kill skips straight to SIGKILL, terminating the container immediately.

Your server's been logging everything—it’s just easy to overlook until something breaks. Every incoming request, database call, or auth check ends up in your access logs. They’re not flashy, but they quietly document every interaction your system handles. For developers, they’re often the most reliable starting point when things go wrong. In this blog, we'll take a look at what an access log is, its format, types, and a few best practices.

When troubleshooting with the Elastic Stack, Kibana is often the interface you’ll rely on to query and visualize logs. It doesn’t change the data—it just makes it searchable and a bit easier to work with under pressure. If you’re investigating an outage, tracking performance issues, or trying to correlate events across services, Kibana’s log exploration tools can speed up the process, assuming they’re configured and used well.