Tracing a Bad Request: Debugging Distributed S3 Proxy Routing in a Test Cluster

Introduction

In distributed systems debugging, few skills are as essential as the ability to trace a request across service boundaries. When a client sends a command and receives an opaque error, the engineer must follow the request's path hop by hop, examining each component's logs to identify where the failure occurs. Message 1368 of this coding session captures exactly such a moment: the assistant has just received a 400 Bad Request error from an S3 CreateBucket operation, has identified which backend node handled the request via the proxy's round-robin routing, and is now inspecting that node's logs to understand what went wrong.

This short but pivotal message demonstrates the methodical, hypothesis-driven approach required to debug a multi-layer distributed storage system. The assistant is not guessing at the cause; it is following the data, letting the system's own behavior guide the investigation. This article examines the reasoning, assumptions, and knowledge boundaries visible in this single debugging step, and explores what it reveals about the architecture of the test cluster and the challenges of operating distributed S3-compatible storage.

The Message in Full

The subject message, reproduced exactly as it appears in the conversation, reads:

[assistant] The request went to kuri-2. Let me check kuri-2 logs:
[bash] docker logs test-cluster-kuri-2-1 2>&1 | tail -10
WebUI: http://127.0.0.1:5001/webui
Gateway server listening on /ip4/127.0.0.1/tcp/8080
Daemon is ready
2026-01-31T16:04:36.520Z	ERROR	ribs:rbdeal	rbdeal/balance_manager.go:73	balance manager: failed to get market balance	{"error": "resolve address [REDACTED]: actor not found"}
2026-01-31T16:04:37.468Z	ERROR	cmd/ipfs	kubo/daemon.go:1219	
⚠️ A NEW VERSION OF KUBO DETECTED

This Kubo node is running an outdated version (0.36.0).
41% of the sampled Kubo peers are r...

The Filecoin wallet address in the balance manager error has been redacted as a security precaution, consistent with the handling of testing secrets and identifiers throughout this analysis.

Context and Motivation: Why This Message Was Written

To understand why the assistant wrote this message, we must look at the events immediately preceding it. The assistant had spent the preceding forty-six messages (indices 1322 through 1367) wrestling a test cluster back into a healthy state. This effort included cleaning up corrupted database state in YugabyteDB, recreating Docker containers with corrected startup commands, dropping and recreating keyspaces, and restarting services. Finally, all three containers—YugabyteDB, kuri-1, kuri-2, and the S3 proxy—were running.

The moment of truth came in message 1366, when the assistant ran the AWS CLI command to create a test bucket:

AWS_ACCESS_KEY_ID=test AWS_SECRET_ACCESS_KEY=test aws --endpoint-url http://localhost:8078 s3 mb s3://test-bucket

The response was discouraging: make_bucket failed: s3://test-bucket An error occurred (400) when calling the CreateBucket operation: Bad Request. A 400 status code indicates a client-side error, but the message was opaque—it didn't specify why the request was bad. The assistant then checked the S3 proxy's logs (message 1367), which revealed:

Round-robin selected backend {"backend": "kuri-2", "method": "CreateBucket"}

This single log line was the critical clue. It told the assistant two things: first, that the proxy was functioning and routing requests (the round-robin algorithm was working), and second, that the request had been forwarded to kuri-2. The 400 error could be coming from either the proxy itself (rejecting the request before forwarding) or from kuri-2 (processing the request and returning an error response). The proxy log showing a successful routing decision suggested the error originated downstream, at kuri-2.

Message 1368 is the direct consequence of this deduction. The assistant's statement "The request went to kuri-2" is not speculation—it is a factual observation drawn from the proxy's own logs. The next logical step is to examine kuri-2's logs to see how it handled the forwarded request.

The Debugging Methodology: Following the Request Path

The assistant's approach in this message exemplifies a fundamental debugging technique for distributed systems: trace the request path. In a three-layer architecture (Client → S3 Frontend Proxy → Kuri Storage Nodes → YugabyteDB), a single S3 operation traverses multiple service boundaries. Each hop is a potential failure point.

The assistant's methodology proceeds in clear stages:

  1. Reproduce the failure: Run the AWS CLI command and observe the error.
  2. Examine the first hop: Check the proxy logs to confirm the request was received and routed.
  3. Identify the target: Extract the backend node selected by the routing algorithm.
  4. Examine the second hop: Check the target node's logs to see how it processed the request. This is textbook distributed debugging. The assistant does not jump to conclusions about what caused the 400 error—it does not assume a configuration problem, a network issue, or a code bug. Instead, it lets the system reveal its own state through logs. The specific command chosen—docker logs test-cluster-kuri-2-1 2>&1 | tail -10—reveals several deliberate decisions: - Container name: The assistant knows the exact container name (test-cluster-kuri-2-1), indicating familiarity with Docker Compose's naming convention (project name + service name + instance number). - Log tail: Using tail -10 limits output to the most recent entries, avoiding information overload. This is appropriate when looking for an error that just occurred. - Combined output: The 2>&1 redirect merges stderr into stdout, ensuring no error messages are missed. This is a best practice for log inspection.

Analysis of the Findings

The log output from kuri-2 reveals several things, but notably does not contain an obvious S3-related error. The visible entries are:

  1. Standard startup messages: "WebUI", "Gateway server listening", "Daemon is ready" — these confirm the node initialized successfully.
  2. Balance manager error: An ERROR from rbdeal/balance_manager.go about failing to get a market balance because a Filecoin address could not be resolved ("actor not found"). This is related to the Filecoin retrieval deal subsystem, not S3 operations.
  3. Kubo version warning: A non-critical warning that the embedded IPFS node (Kubo) is running version 0.36.0, which is outdated relative to the network. The absence of an S3-specific error is itself a significant finding. It suggests one of several possibilities: - The S3 request error might be logged at a different log level (e.g., DEBUG or WARN) not visible in the last 10 lines. - The error might occur before kuri-2's logging system is fully initialized. - The 400 error might actually be generated by the proxy itself, not by kuri-2, despite the successful routing log. - The error might be in a different log file or stream not captured by docker logs. This is where the assistant's debugging process would need to branch: either dig deeper into kuri-2's logs (increase tail size, check specific log files, enable debug logging) or reconsider whether the proxy is the actual source of the error.

Assumptions Made

Every debugging step rests on assumptions, and this message is no exception. The assistant is making several implicit assumptions:

  1. The round-robin routing is correct: The assistant assumes that the proxy's log accurately reflects which backend received the request. This is a reasonable assumption—the log is emitted at the point of routing—but it is worth verifying that the proxy and kuri-2 share the same understanding of the request (e.g., that the connection wasn't reset or multiplexed).
  2. The error is visible in the container logs: The assistant assumes that kuri-2 logs S3 request errors to stdout/stderr, which is what docker logs captures. If the application logs to a file instead, this command would miss it.
  3. The most recent logs are the most relevant: Using tail -10 assumes the error occurred recently enough to appear in the last 10 lines. Given that the aws s3 mb command was executed seconds earlier, this is a safe assumption, but it does risk missing earlier errors that might be relevant.
  4. The error originates from the backend, not the proxy: The assistant's decision to check kuri-2 logs first (rather than re-examining proxy logs or checking network connectivity) assumes the proxy successfully forwarded the request and the backend is the source of the 400 response.
  5. The container naming convention is consistent: The assistant assumes the container is named test-cluster-kuri-2-1, which matches the Docker Compose convention. If the project name or instance number differed, this command would fail silently or return logs from the wrong container.

Knowledge Boundaries: Input and Output

Input Knowledge Required

To understand this message, a reader needs:

Output Knowledge Created

This message produces several pieces of actionable knowledge:

The Thinking Process: What the Assistant's Reasoning Reveals

Although the assistant does not include an explicit "thinking" block in this message, its reasoning is transparent through its actions. The sequence of events reveals a clear mental model:

  1. Observation: The AWS CLI command returned a 400 error.
  2. Hypothesis: The error might originate from the backend node that processed the request.
  3. Test: Check the proxy logs to identify which backend received the request.
  4. Evidence: The proxy log shows kuri-2 was selected via round-robin.
  5. Test: Check kuri-2's logs for evidence of the error.
  6. Result: No obvious S3 error in the log tail. This is hypothesis-driven debugging at its most practical. The assistant does not need to explicitly state "I think the error is in kuri-2" because the action speaks for itself. The decision to check kuri-2 before kuri-1 is also telling—it is based on specific evidence (the proxy log), not on a random guess. The assistant's choice to present the raw log output without commentary is also noteworthy. In many debugging conversations, an engineer might summarize or interpret the logs. Here, the assistant lets the logs speak for themselves, trusting that the user (or the assistant's future self) can draw the same conclusions. This is a sign of confidence in the debugging process and respect for the evidence.

Conclusion

Message 1368 is a small but perfect example of distributed systems debugging in practice. It captures the moment when an engineer traces a failed request across a service boundary, examining each component's perspective on the failure. The message is brief—barely a sentence of analysis followed by a shell command and its output—but it embodies a sophisticated debugging methodology.

The assistant's approach—reproduce, observe, trace, examine—is applicable to any multi-service architecture, whether it is an S3-compatible storage cluster, a microservices application, or a data pipeline. The specific tools may differ (Docker logs, Kubernetes events, cloud watch logs), but the pattern remains the same: follow the request, examine each hop, and let the system reveal its own failures.

This message also illustrates an important truth about debugging: not every step yields a clear answer. The assistant examined kuri-2's logs and found no direct evidence of the S3 error. This is not a failure of the debugging process—it is a successful narrowing of the hypothesis space. The assistant now knows that either the error is elsewhere (perhaps in the proxy itself) or it is in kuri-2 but not visible in the last 10 log lines. Either way, the investigation can proceed with better direction.

In the broader context of the coding session, this message represents a transition from infrastructure stabilization (cleaning databases, restarting containers) to functional debugging (why does the S3 API return errors?). It is the moment when the test cluster goes from "running" to "working correctly," and the assistant's methodical approach to tracing the request path is the tool that will ultimately bridge that gap.