The Second Restart: Debugging Round-Robin Routing in a Distributed S3 Proxy

A Single Command That Caps a Debugging Arc

In the middle of a lengthy debugging session for a horizontally scalable S3 storage system, a developer executes a single command:

cd /home/theuser/gw/test-cluster && FGW_DATA_DIR=/data/fgw2 docker compose up -d --force-recreate s3-proxy 2>&1

The output scrolls by — containers are running, the s3-proxy is being recreated, YugabyteDB is healthy, the database initializer runs. On its surface, this is just another Docker Compose restart. But this message, message 871 in the conversation, is the culmination of a subtle and instructive debugging journey. It represents the second attempt to restart the S3 frontend proxy after a logging instrumentation fix, and it only exists because the first attempt revealed a deeper configuration problem. To understand why this single command was written, we must trace the chain of reasoning that led to it.

The Problem: Traffic That Only Flows One Way

The story begins with a user observation at message 849: "There is traffic visible on Frontend Proxies table, but all of it goes only to kuri-1, none to kuri-2." The S3 frontend proxy is supposed to distribute write requests across two Kuri storage nodes using round-robin selection, but all traffic is landing on a single node. This is a critical issue for a system designed to be horizontally scalable — if the round-robin isn't working, the entire scaling architecture is compromised.

The assistant's response is immediate and methodical. Rather than guessing, the assistant begins by examining the routing logic itself. It greps for "round.?robin|backend|routing" and finds 100 matches, then reads the backend pool implementation in backend_pool.go. The code looks correct — the SelectRoundRobin() method uses atomic counters and appears to properly cycle through available backends. Next, the assistant checks whether both backends are actually healthy by running health checks from inside the s3-proxy container. Both kuri-1 and kuri-2 respond with "OK." The environment variable FGW_BACKEND_NODES confirms both nodes are configured: kuri-1:http://kuri-1:8078,kuri-2:http://kuri-2:8078. Everything looks correct on paper, yet the behavior is wrong.

Adding Instrumentation: The First Edit

At this point, the assistant makes a critical decision: add info-level logging to the round-robin selection code in server.go. The reasoning is sound — if the round-robin logic is correct but the behavior is wrong, the developer needs visibility into what's actually happening at runtime. Which backend is being selected? Is the selection changing between requests? Without logging, the proxy is a black box.

The edit is applied, the Docker image is rebuilt (message 861), and the first restart is attempted (message 862). But something goes wrong. The command docker compose up -d --force-recreate s3-proxy unexpectedly recreates not just the s3-proxy container, but also kuri-1 and kuri-2. This happens because --force-recreate with a specific service can still trigger recreation of dependent services depending on the compose configuration. The output shows "Container test-cluster-kuri-2-1 Recreate" and "Container test-cluster-kuri-1-1 Recreate" alongside the s3-proxy recreation. This is a side effect the assistant may not have fully anticipated.

The Logs That Never Came

After the first restart, the assistant generates test traffic (message 863) and checks the logs (message 864). The result is puzzling: the new logging messages are nowhere to be seen. The logs only show the startup messages: "S3 Frontend Proxy started on :8078 (node: proxy-1)" and "Backend nodes: 2 configured." The round-robin selection logs are absent.

This triggers a new investigation. The assistant checks the log level configuration (message 865-868) and discovers the root cause: the environment variable RIBS_LOGLEVEL=ribs=info only configures the ribs logger to info level. The s3frontend package uses a different logger named gw/s3frontend/backend, which is completely unconfigured. The go-log framework used by this project defaults unconfigured loggers to a level that may suppress info messages, or the logging infrastructure may not be initialized for this logger at all. The assistant's edit added logging calls using the log variable from backend_pool.go, but that logger was never wired into the environment configuration.

This is a classic debugging pitfall: the instrumentation you add is only useful if the logging infrastructure is properly configured to emit it. The assistant had assumed that adding log.Infof(...) calls would automatically produce output, but the logging framework requires explicit configuration for each logger name.## The Second Restart: A Targeted Correction

This brings us to message 871 — the second restart. After discovering that the logging configuration is the problem, the assistant reads the docker-compose.yml file (message 869) and edits it (message 870) to add the frontend logger to the environment configuration. Then, message 871 executes the restart command again.

But this time, something is different. The output shows only the s3-proxy being recreated — kuri-1 and kuri-2 remain "Running" without being recreated. This is likely because the containers are now in a stable state from the previous restart, or because the assistant has learned from the first attempt and used a more targeted approach. The output reads:

Container test-cluster-yugabyte-1 Running 
Container test-cluster-kuri-2-1 Running 
Container test-cluster-kuri-1-1 Running 
Container test-cluster-s3-proxy-1 Recreate 
Container test-cluster-s3-proxy-1 Recreated

The db-init container also starts, which is a normal part of the startup sequence. This restart is cleaner and more focused — it only touches the service that actually changed.

Assumptions and Their Consequences

Several assumptions are visible in this debugging arc. First, the assistant assumed that the round-robin logic was correct based on reading the source code. This was a reasonable assumption — the code looked correct, both backends were healthy, and the configuration was right. But the assumption that "correct code produces correct behavior" was insufficient; the system needed runtime observability to confirm.

Second, the assistant assumed that adding log.Infof() calls would automatically produce visible log output. This assumption failed because the logging framework requires explicit configuration per-logger. The gw/s3frontend/backend logger was never configured in the environment, so its output was silently suppressed. This is a subtle but important lesson about the go-log framework used in this project: logger names must be explicitly enabled, and the RIBS_LOGLEVEL variable only covers the ribs family of loggers.

Third, the assistant assumed that --force-recreate s3-proxy would only recreate the s3-proxy container. In the first restart, it also triggered recreation of the Kuri nodes, which was an unexpected side effect. This may have been caused by Docker Compose's dependency resolution or the container state at that moment. The second restart avoided this issue, either because the containers were already in a consistent state or because the assistant's approach was different.

The Thinking Process: A Methodical Debugging Approach

The thinking process visible in this sequence is exemplary of systematic debugging. The assistant follows a clear pattern:

Observe the symptom: Traffic only goes to kuri-1, not kuri-2.
Examine the code: Read the round-robin implementation and confirm it looks correct.
Verify the configuration: Check that both backends are configured and healthy.
Add instrumentation: Insert logging to gain runtime visibility.
Rebuild and deploy: Rebuild the Docker image and restart the service.
Check the logs: Verify that the instrumentation is producing output.
Diagnose the instrumentation failure: Discover that the logger isn't configured.
Fix the configuration: Edit docker-compose.yml to add the logger.
Redeploy: Execute the second restart (message 871). This is classic "debug the debug" — the first attempt at instrumentation failed, requiring a meta-debugging step to fix the logging infrastructure itself. The assistant doesn't give up or guess; it systematically traces the chain from symptom → code → configuration → instrumentation → logging infrastructure.

Input Knowledge Required

To understand this message, the reader needs knowledge of several domains. Docker Compose basics are essential — understanding up -d --force-recreate, container states (Running, Recreate, Recreated, Healthy), and the concept of service dependencies. The go-log logging framework from the ipfs/go-log library is relevant, particularly the pattern of configuring loggers by name via environment variables. Knowledge of the S3 frontend proxy architecture — that it's a stateless layer that routes to Kuri backends using round-robin — is necessary to understand why the traffic distribution matters. Finally, understanding the project's build pipeline (Docker build with multi-stage builds, cached layers, image naming) helps contextualize the rebuild step.

Output Knowledge Created

This message creates several kinds of knowledge. Most immediately, it produces a restarted s3-proxy container with the new logging configuration, which will allow the assistant to finally see which backends are being selected. It also demonstrates the correct procedure for restarting a single service in this test cluster without disturbing the other containers. The message documents the state of the cluster at a specific point in time — all containers running, YugabyteDB healthy, db-init completing successfully. For someone reading the conversation log, this message serves as a milestone: "this is where the logging fix was deployed."

Mistakes and Lessons

The primary mistake in this arc was the incomplete logging configuration. The assistant added logging calls without ensuring the logging framework would emit them. This is a common oversight — developers often assume that adding a log statement is sufficient, but in systems with configurable logging levels and logger namespaces, the plumbing must be complete. The lesson is: when adding instrumentation, always verify that the output channel is configured to deliver the messages.

A secondary issue was the unexpected container recreation in the first restart. While not necessarily a mistake (the containers did restart successfully), it highlights the complexity of Docker Compose's --force-recreate behavior. A more cautious approach might have used docker compose restart s3-proxy instead, which would avoid recreating dependencies.

Conclusion

Message 871 appears to be a mundane Docker Compose command, but it represents the second attempt in a careful debugging process. It is the product of a chain of reasoning that began with a user's observation about uneven traffic distribution, proceeded through code reading, health checks, instrumentation, and logging configuration diagnosis, and culminated in a targeted restart. The message itself is the punchline of a debugging joke that took twenty messages to set up. In the context of building a horizontally scalable system, getting round-robin routing to work correctly is not optional — it is the entire point of the architecture. This single restart command, unremarkable in isolation, carries the weight of that architectural requirement.