Operations | Monitoring | ITSM | DevOps | Cloud

MongoDB’s strength of flexible schema and fast iteration can also hide costly queries until they surface as user-facing latency, replica lag, or spiky CPU. A handful of slow operations can impact the cache, starve other workloads, and cascade into timeouts across services. Monitoring slow queries gives you an early warning system for index gaps and query-plan regressions introduced by code deploys, schema changes, or shifting data shapes.

Your application is only as reliable as the infrastructure it runs on. Most commonly, that means Kubernetes is doing the job by managing fleets of containers, scaling services on demand, and keeping workloads distributed across nodes. Traditional dashboards weren’t built to scale with this reality. They give you snapshots of raw metrics. They don’t scale to multi-cluster environments. They don’t map relationships between resources.

When we started building Logs in Sentry we had one goal: make them useful for real debugging, not just another high-volume text storage. This meant making them "trace connected" from day one. This let us ensure they were tightly connected to the actions and performance happening in your application, right where developers already go to investigate errors, performance, and latency issues. Now, Logs is out of beta and generally available to everyone.

Imagine a developer at a fast-growing company. A customer support agent reports a critical issue: a user's recent order is stuck in a "pending" state. The agent provides a customer ID and a request ID. The developer's typical process is a familiar, painful dance: This process is slow, tedious, and prone to human error. The Mean Time to Resolution (MTTR) is measured in hours, not minutes, and it's a huge drain on engineering resources.

From manual downloads to automated artifact management.