Operations | Monitoring | ITSM | DevOps | Cloud

Launch Week Day 1: Introducing Discover Services Picture this: It's 2 AM, alerts are firing, and you're staring at a dashboard trying to figure out which service is causing the cascade of failures. Your service map is a six-month-old Miro board, and you have no idea what's actually talking to what in production right now. If you've been there, you're not alone. In fast-moving teams, new services get deployed faster than you can track them.

Your application is under constant pressure to deliver low latency, high reliability, and a smooth user experience isn’t optional. When performance drops, every second matters. Application Performance Monitoring (APM) gives you the visibility to spot issues before your users feel the impact. It also helps you understand what’s happening inside your stack, so you can track resource usage, pinpoint bottlenecks, and keep things running at peak performance.

Your log format defines how your application records events. The structure you choose shapes how logs get parsed, indexed, and queried. It affects how quickly you can debug issues, build alerts, or control storage usage. In this guide, we'll take a look at the log formats developers typically use, the essential fields to include, and what trade-offs to consider before locking down a format for your system.

A PostgreSQL setup that performed well with 10,000 users starts to show strain at 100,000. Queries that once returned in under 50ms now take over 2 seconds. The connection pool regularly hits its limit during peak usage, leading to timeouts and degraded performance. This blog focuses on practical ways to reduce query latency by 50–80% and increase throughput for high-concurrency environments.

Application metrics are structured, quantifiable signals that reflect how your software behaves in production. They capture key aspects of performance, response times, error rates, throughput, and resource usage, giving you a real-time view into the health of your system. Tracking the right metrics helps detect regressions early, surface latent issues before they impact users, and guide optimization decisions based on hard data, not guesswork.

Your Jaeger setup is running. Traces are coming in, and the UI is helping you spot slow services or debug broken flows. But just like any part of your observability stack, Jaeger needs some basic monitoring to stay reliable. If the collector starts queueing spans or the agent runs out of buffer, it can lead to dropped traces, sometimes without any obvious sign in the UI. This blog focuses on the operational side of Jaeger.

As distributed systems scale, maintaining manual instrumentation across services quickly becomes unsustainable. The OTel Injector addresses this by automatically attaching OpenTelemetry instrumentation to applications, no code changes needed. This blog covers how the OTel Injector works, how it integrates with Linux environments, and how to set it up for consistent telemetry across your stack.

Grafana Loki is up and running, log ingestion looks healthy, and dashboards are rendering without issues. But when you query logs from a few weeks ago, the data's missing. This is a recurring problem for many teams using Loki in production: while the system handles short-term log visibility well, it often lacks the retention guarantees developers expect for historical analysis and incident review.

When you export OpenTelemetry metrics to Prometheus, resource fields like service.name or deployment.environment don’t show up as metric labels. Prometheus drops them. To use them in queries, you’d have to join with target_info: This makes filtering and grouping more difficult than necessary. Prometheus 3.0 changes that. It supports resource attribute promotion—automatically converting OpenTelemetry resource fields into Prometheus labels.

Deploying a new service shouldn’t break dashboards. But it happens, usually because metric names or labels aren’t consistent across teams. You end up with traces that don’t link, metrics that don’t align, and queries that take hours to debug, not because the system is complex, but because the telemetry is fragmented. OTel Weaver addresses this by enforcing OpenTelemetry semantic conventions at the source.