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Which DevOps problem-solving tools help you debug complex issues?
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When you're facing a complex production issue, what DevOps problem-solving tools do you reach for first?
I'm talking about debugging tools, log analysis platforms, performance monitoring solutions, or anything else that helps you understand what's going wrong.
What's your typical troubleshooting workflow? Do you have a standard set of tools or does it depend on the type of issue? And how do you document your findings so the same issues don't keep recurring?
For DevOps problem-solving tools, my go-tos are: Elasticsearch/Kibana for log analysis, Prometheus/Grafana for metrics, and Jaeger for distributed tracing. Together they give you visibility from logs to traces.
My troubleshooting workflow: 1) Check metrics for anomalies, 2) Search logs for errors around the anomaly time, 3) Use tracing to follow request flow through services, 4) Reproduce in a test environment if needed.
We also use Chaos Engineering tools like Chaos Mesh to proactively find weaknesses. Better to cause a controlled failure and learn from it than be surprised by a production incident.
All findings go into postmortems stored in Jira. We tag issues with categories (networking, database, etc.) so we can spot patterns over time.
When debugging complex issues, I start with the three pillars" of observability: metrics, logs, and traces. For metrics, we use Datadog. For logs, we use the ELK stack. For traces, we use OpenTelemetry with Jaeger.
One DevOps problem-solving tool that's underrated: packet capture. When network issues are suspected, tcpdump and Wireshark can reveal problems that higher-level tools miss.
We also use "debug containers" - lightweight containers with networking tools installed that we can deploy alongside production containers for troubleshooting. Much safer than giving engineers direct access to production systems.
Documentation happens in our runbooks. Every time we solve a new type of issue, we add it to the appropriate runbook with steps and examples.
My favorite DevOps problem-solving tools are the ones that help me understand system behavior. We use Honeycomb for structured logging and event analysis. Their query language lets you ask questions like show me all requests from user X that failed in the last hour" which is incredibly powerful for debugging.
For performance issues, we use py-spy for Python profiling and async-profiler for Java. Sometimes you need to look at the code level to understand why something is slow.
We also invested in training our team on these tools. It's not enough to have the tools - people need to know how to use them effectively. We do monthly "debugging dojos" where we work through real (sanitized) production issues together.
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