If you’ve ever stared at a crashing container wondering what broke—without a clue where to start—you’re not alone.
You’re probably here because you’re tired of guessing what went wrong. Maybe your logs are vague, your docker ps looks fine, and restarting doesn’t help. It feels like a dead end, and you’re burning time trying random fixes.
That’s exactly why I built this framework.
This article lays out a clear, step-by-step method for container debugging—no guesswork, no fluff. Just a logical path to identify issues like startup failures, strange crashes, or mysterious network breakdowns.
We’ve distilled this from years of diagnosing and fixing production-level container issues. The techniques here aren’t theoretical—they’re proven, modern, and repeatable.
By the end, you’ll know exactly how to break down a failure, identify the root cause, and move forward without spinning your wheels.
It’s the systematic approach every containerized application deserves.
Step 1: The First Question – Is the Container Running?
Let’s start with the obvious—but often overlooked—first step in any container debugging session: is your container actually running?
Some developers jump straight into rewriting configuration files or reinstalling packages (cue facepalm), without checking whether the container is even active.
Use this command first:
docker ps
This lists only running containers. If your container isn’t here, don’t panic—use:
docker ps -a
This shows all containers, including stopped ones.
Now, time to decode what you see. Common statuses include:
- Up: Your container is running—this isn’t the problem (probably).
- Exited: It ran… and then something happened. You’re looking at your prime suspect.
- Created: It was made, but never started.
If it says Exited, go straight to:
docker logs [container_id]
The logs will tell you why the container stopped—maybe it couldn’t connect to a database, or perhaps the entrypoint script is misfiring (been there).
Look for exit codes like:
Exited (1): Generic error. Think of it as the container’s way of saying “something went wrong, but I’m not telling you what.”Exited (137): Killed due to Out-of-Memory (hint: maybe your container tried to process a 4GB AI model on 512MB RAM).
Pro tip: If you’re getting code 137 a lot, try running the container with a memory limit and adjust accordingly.
Before making any fixes, check these basics. Trust us—this five-minute check often saves hours of guesswork.
Step 2: Inspecting the Scene – Checking Configuration and State
Let’s be honest—most container issues aren’t about code. They’re about configuration mistakes that sneak past us until everything breaks (usually at 2 a.m., right before a deadline). This is where docker inspect becomes your best friend, or at least your most reliable snitch.
Some folks argue that logs and container outputs should be enough. “Just run it and see,” they say, like that’s foolproof. I disagree. Debugging based on behavior alone is like diagnosing car trouble just by revving the engine. You need to pop the hood—and docker inspect is how you pop that hood.
Here’s what I focus on first:
- Environment Variables: Look inside the JSON output to confirm everything expected is set—and that nothing sensitive is hard-coded. (Nothing like seeing
DB_PASSWORD=123456to ruin your morning.) - Volume Mounts: Is the container really seeing the data you think it is? Check both source and target paths. Use permissions (
ls -la) once inside to make sure it’s not a read-only disaster zone. - Network Settings: Identifying which network the container connects to—and validating the IP address—is foundational. A container with no network is a container talking to itself (which is… poetic, but useless).
Pro Tip: Pipe docker inspect into a tool like jq for filtering key fields. It’ll save your eyes—and your patience.
For real-time clarity, use the interactive shell with:
docker exec -it [container_id] /bin/sh
This command is crucial for container debugging. Once inside, immediately scan:
- File system map (
ls -la) - Running processes (
ps aux)
You’d be amazed how often an app crashes silently, but a rogue process gives it away.
Step 3: Network Troubleshooting – From ‘Connection Refused’ to Resolution
Let’s clear something up: the infamous “connection refused” error in Docker containers doesn’t always mean your app is broken. More often, it just means your container isn’t reachable—like calling someone who never turned their phone on.
Is Anyone Listening?
Start by verifying whether the application inside your container is even listening for connections. Run netstat -tuln inside the container. You’re looking for the expected port (say, 8080) to be in a listening state. If you don’t see it? That service probably isn’t running—or it’s bound to the wrong interface like 127.0.0.1 (which blocks external access).
Host-to-Container Connectivity
People often forget to map container ports correctly. That tiny detail creates big headaches. If you didn’t run your container with -p HOST_PORT:CONTAINER_PORT, it’s basically invisible to the host system. Use docker port [container_id] to see which ports are actually exposed.
Pro tip: Always double-check your docker run command for port publishing flags. You’ll save yourself hours.
Container-to-Container Connectivity
If containers need to talk to each other, they must be on the same Docker network. Use docker network inspect [network_name] to confirm. Then, jump into another container and test with ping or curl. It’s a lot like matchmaking—nothing connects if they’re in totally different circles.
Container-to-External World
Trying to hit the internet? Sometimes DNS issues sneak in. Check /etc/resolv.conf inside the container to see what DNS servers are set. Then try a basic curl google.com test to verify outbound access.
When in doubt, use container debugging once before changing your network strategy. Also, if you’re tackling more complex issues like failed mobile app services, explore troubleshooting mobile app crashes using crash logs and analytics for further insight.
Connecting the dots takes more than just code—it takes knowing what questions to ask.
Step 4: Performance Bottlenecks – Memory, CPU, and I/O
Performance issues in containers can sneak up fast. One minute everything looks green—the next, your app is crawling. Let’s break down how to spot the common culprits using built-in tools and a bit of container debugging.
Start with docker stats. It’s a real-time dashboard showing each container’s CPU, memory, and network usage. Think of it as your mission control. If one container sticks out like a sore thumb in resource usage—it probably is.
Now, memory. If your container exits with Exit Code 137, say hello to the OOM Killer. That’s Linux’s ruthless way of saying, “You’ve had too much—get out.” Adjust memory limits in your Docker config if you see this happening often.
CPU can be tricky. Even if usage seems low, your app may be throttled due to set limits—especially on shared infrastructure. Throttling equals delay (without any warning signs unless you’re looking).
Pro tip: Disk I/O issues are stealthy. Slow volume mounts—especially on macOS—can kill performance. Use iostat or Docker’s --log-level=debug to catch these in action.
Understanding all three—memory, CPU, and I/O—gives you the visibility to fix what matters.
From Reactive to Proactive Troubleshooting
You came here because container errors can feel like a dead end—cryptic logs, broken deployments, and no clear path forward.
Now, that’s changed.
You’ve got a four-step system that makes sense of the chaos: Check Status → Inspect State → Test Network → Monitor Resources. This is your playbook for turning guesswork into repeatable clarity.
Container debugging is no longer a black box. When you know exactly where to look, the problem stops being mysterious—and starts becoming fixable.
Here’s what to do next: Bookmark this process. Use it every time your container throws a curveball. Then level up—automate parts of the framework to solve issues earlier and faster.
We built this method from real-world failures, and we know it works. It’s already saving time and preventing outages for teams like yours.
Don’t wait for the next failure to slow you down. Start fixing issues before they happen.