The Verification That Almost Wasn't: A Docker Compose Status Check in the Heat of Performance Debugging

The Message

[assistant] [bash] cd /home/theuser/gw/test-cluster && sleep 10 && FGW_DATA_DIR=/data/fgw2 docker compose ps 2>&1
NAME                      IMAGE                                COMMAND                  SERVICE    CREATED             STATUS                       PORTS
test-cluster-kuri-1-1     fgw:local                            "sh -c 'set -a && . …"   kuri-1     24 seconds ago      Up 13 seconds                0.0.0.0:7001->7001/tcp, [::]:7001->7001/tcp
test-cluster-kuri-2-1     fgw:local                            "sh -c 'set -a && . …"   kuri-2     24 seconds ago      Up 13 seconds                0....

At first glance, this appears to be a trivial status check — a developer running docker compose ps after a ten-second sleep to confirm containers are running. But in the context of the broader debugging session, this single command represents a critical inflection point: the moment when a multi-hour investigation into false data corruption, a complete rewrite of the database write path, and a frustrating battle with Docker container lifecycle management all converged into a single confirmation that the system was finally ready for the next round of testing.

The Road to This Message

To understand why this message exists, we must trace the debugging odyssey that preceded it. The session began with an alarming discovery: during S3 load testing, the system reported "verify errors" — read-after-write checksum mismatches that suggested data corruption. This triggered a deep investigation into the YCQL write path, the YugabyteDB-backed object index that tracks object placement in the distributed S3 architecture.

The assistant's first move was to improve the loadtest tool's error classification, distinguishing between actual checksum mismatches (true corruption) and context deadline exceeded errors (timeouts at the end of test runs). This revealed that no actual corruption was occurring — the "verify errors" were artifacts of the test harness using the same context deadline for verification reads. This finding shifted the focus from a corruption bug hunt to a performance optimization mission.

The assistant then implemented a CQLBatcher — a write-batching layer in the database/cqldb package that collects individual CQL INSERT calls and flushes them in batches of up to 15,000 entries, with 8 worker goroutines and exponential backoff retries. This batcher was integrated into the ObjectIndexCql.Put() method, which is the core write path for S3 object metadata. The batcher was designed to reduce database contention and improve throughput under high concurrency — a forward-looking optimization even though the existing system was passing verification tests.

The Restart That Didn't Work

When the user requested "Restart with changes, test at 10/100/1000 parallel," the assistant began what should have been a straightforward process: rebuild the binary, restart the containers, run the tests. But this is where the session reveals a subtle but important class of infrastructure bug.

The assistant initially tried to restart the kuri processes using pkill, only to discover that the processes were running inside Docker containers and couldn't be killed directly. After being reminded by the user that the cluster runs in docker-compose, the assistant found the test-cluster directory and rebuilt the Docker image (fgw:local). The first restart attempt used docker compose restart kuri-1 kuri-2 s3-proxy, which appeared to succeed — the containers were marked as "Restarting" and then "Started."

But when the assistant ran the first load test at 10 workers, all writes failed. Something was fundamentally wrong. Checking docker compose ps revealed the problem: the kuri nodes weren't even listed, and the s3-proxy was still using an old image hash (sha256:542637...). The docker compose restart command had merely restarted the existing containers with their old images — it did not pick up the newly built fgw:local image because Docker Compose caches the image reference from when the container was first created.

This is a classic Docker Compose pitfall: restart reuses the existing container definition, while up --force-recreate recreates containers and can pick up new images. The assistant corrected this with docker compose up -d --force-recreate kuri-1 kuri-2 s3-proxy, which forced container recreation.

Why This Message Matters

Message 1114 is the verification step after that correction. The assistant runs sleep 10 to give the containers time to initialize, then runs docker compose ps to confirm that both kuri-1 and kuri-2 are running with the fgw:local image. The output shows:

Both nodes are up: kuri-1 and kuri-2 both show "Up 13 seconds"
Correct image: Both use fgw:local (the newly built image with the CQL batcher)
Ports are mapped: 7001 and 7002 are properly forwarded This message is the green light before proceeding to the load tests at 10, 100, and 1000 workers. Without this verification, the assistant would have run load tests against misconfigured or non-functional containers, wasting time and generating confusing error messages.

Input Knowledge Required

To fully understand this message, the reader needs knowledge of:

Docker Compose lifecycle management: Understanding the difference between restart, up, up --force-recreate, and how image caching works. The critical insight is that restart reuses the existing container definition (including the old image), while up --force-recreate creates new containers that can reference a newly built image.
The distributed S3 architecture: The test cluster consists of two Kuri storage nodes (kuri-1, kuri-2), an S3 frontend proxy, and a YugabyteDB database. The kuri nodes are the storage backends that handle object placement and retrieval.
The CQL batcher context: The assistant had just implemented a write-batching layer in the YCQL database path, and this was the first deployment of that code. The verification confirms the batcher code is now running in the containers.
The false corruption investigation: The entire restart chain was triggered by the need to test the batcher after confirming that the earlier "corruption" was actually timeout misclassification.

Output Knowledge Created

This message creates several pieces of actionable knowledge:

The cluster is healthy: Both kuri nodes are running with the new code. This is the foundation for all subsequent load testing.
The Docker image build and deployment pipeline works: The fgw:local image was successfully built and deployed to both containers. The --force-recreate strategy for picking up new images is validated.
The configuration is correct: The containers start successfully with the sh -c 'set -a && . …' command, which sources environment variables from the per-node settings files. This confirms that the configuration generation scripts are producing valid output.
Port mapping is correct: Ports 7001 and 7002 are properly mapped, which is essential for the S3 proxy to route requests to the correct storage node.

Assumptions and Potential Mistakes

The assistant made several assumptions in this message:

Ten seconds is enough startup time: The sleep 10 assumes the containers will be healthy within 10 seconds. This is reasonable for Go binaries on modern hardware, but in a slower environment or with more complex initialization, this could be insufficient.
docker compose ps is sufficient verification: The command only shows container status (Up/Down), not application-level health. A container can be "Up" but still crash-looping or failing its initialization. The assistant implicitly trusts that "Up 13 seconds" means the application is functional.
The fgw:local image is correct: The assistant assumes the newly built image contains all the changes, including the CQL batcher. There's no explicit verification that the batcher code is actually executing — no log check or metric confirmation at this point.
The earlier mistake is fully corrected: The assistant assumes that the --force-recreate approach fully resolves the image caching issue. However, if the Docker Compose file uses image: fgw:local without a build section, Docker Compose won't automatically rebuild — the assistant had to manually run docker build first, then rely on --force-recreate to pick up the locally tagged image.

The Thinking Process

The reasoning visible in this message is methodical and defensive. After the failed first restart attempt (where containers appeared to restart but were actually using old images), the assistant has learned a lesson about Docker Compose behavior. The sequence shows:

Awareness of the problem: The assistant recognized that restart wasn't sufficient because the containers were using old images.
Corrective action: Used up -d --force-recreate to force container recreation with the new image.
Delayed verification: Added sleep 10 before checking status, acknowledging that containers need time to initialize.
Explicit confirmation: Ran docker compose ps to visually confirm both nodes are up with the correct image. This is classic defensive debugging: after a mistake, add extra verification steps to ensure the correction actually worked before proceeding to the next phase.

Conclusion

Message 1114 is a small but essential piece of infrastructure debugging craftsmanship. It represents the moment when a complex, multi-threaded investigation into data corruption, database performance, and container orchestration converges into a single point of verification. The assistant's methodical approach — recognizing the Docker Compose image caching pitfall, correcting it with --force-recreate, and then explicitly verifying the result — demonstrates the kind of systematic thinking that separates a successful debugging session from a frustrating spiral of false starts. This status check isn't just about listing containers; it's about confirming that the foundation is solid before building the next layer of performance testing.