When the Proxy Bends: Diagnosing Connection Resets at the Edge of S3 Throughput

Introduction

In the high-stakes world of distributed storage systems, few moments are as revealing as the boundary between "working" and "breaking." Message 1175 of this coding session captures exactly such a boundary — a single observation from a load-testing run that would catalyze a critical infrastructure decision. The assistant, having just successfully deployed a horizontally scalable S3 architecture with stateless frontend proxies, Kuri storage nodes, and YugabyteDB metadata storage, is systematically stress-testing the system. After a clean run at 10 concurrent workers, the jump to 100 workers produces a cascade of "connection reset by peer" errors. This message is the moment the assistant notices the pattern and pivots from functional testing to performance diagnosis.

The Message

The complete text of the subject message is:

[assistant] Many "connection reset by peer" errors at 100 workers. This indicates the system is getting overloaded. Let me check the final results: [bash] tail -60 /home/theuser/.local/share/opencode/tool-output/tool_c14abc0fe001IQ6KDPAGZImLbg [W49] VERIFY READ ERROR loadtest/w49/obj504-1769873325970483601: execute request: Get "http://localhost:8078/loadtest/loadtest/w49/obj504-1769873325970483601": read tcp [::1]:42784->[::1]:8078: read: connection reset by peer [W73] VERIFY READ ERROR loadtest/w73/obj518-1769873325984809124: execute request: Get "http://localhost:8078/loadtest/loadtest/w73/obj518-1769873325984809124": read tcp [::1]:43210->[::1]:8078: read: connection reset by peer [W67] VERIFY READ ERROR loadtest/w67/obj530-176987...

On its surface, this is a brief diagnostic utterance — three lines of commentary followed by a command invocation and its output. But within the arc of the session, it represents a critical inflection point: the moment the assistant shifts from verifying that the system works to understanding why it breaks under load.

The Reasoning and Motivation

The assistant's motivation is straightforward but important: it is conducting a graduated load test to characterize the system's throughput ceiling. The session's broader context reveals a meticulous testing methodology. Earlier, at message 1171, the assistant ran a 10-worker test that completed successfully, establishing a baseline. Message 1174 escalated to 100 workers, and message 1175 is the diagnostic follow-up.

The phrase "Many 'connection reset by peer' errors at 100 workers" is not merely an observation — it is a classification. The assistant is distinguishing between different failure modes. Earlier in the session (as documented in the chunk summary), the team had been chasing what appeared to be data corruption, only to discover that the "verify errors" were actually context deadline timeouts at the end of test runs. Now, with a different error signature — TCP-level connection resets rather than timeout or checksum-mismatch failures — the assistant is recognizing a new category of problem. This is the thinking of an experienced systems engineer: errors are not all alike, and their kind tells you where to look.

The motivation to "check the final results" by tailing the output file reveals the assistant's systematic approach. Rather than reacting to a single error message, it wants to see the aggregate pattern — how many workers experienced resets, whether the errors cluster at a particular phase of the test, and what the overall throughput and error rate look like. This is diagnostic discipline: one connection reset is a transient; a pattern of resets across multiple workers is a structural problem.

The Diagnostic Thinking Process

The message reveals a clear chain of reasoning, even in its brevity:

1. Observation: At 100 concurrent workers, the load test produces "connection reset by peer" errors during the verify-read phase.
2. Hypothesis: "This indicates the system is getting overloaded." The assistant tentatively attributes the resets to capacity exhaustion somewhere in the request path.
3. Evidence gathering: The assistant tails the output file to see the full extent of the errors and to look for patterns — which workers are affected, whether the errors are clustered, and what the final aggregate statistics look like. The choice of tail -60 is telling. The assistant wants the last 60 lines of the output, which would contain the tail-end summary statistics and the most recent errors. This suggests the assistant is looking for the conclusion of the test run — the final throughput numbers, the total error count, and the distribution of failures. Notably, the assistant does not immediately jump to a conclusion about which component is overloaded. The connection reset could originate from: - The Docker userland proxy (which translates container ports to host ports) - The S3 frontend proxy itself (running out of worker goroutines or file descriptors) - The Kuri storage nodes (database connection pool exhaustion) - The operating system's TCP stack (ephemeral port exhaustion, socket backlog limits) The assistant's restraint in not prematurely narrowing the diagnosis is itself a methodological choice. By first gathering the full data, the assistant preserves the ability to recognize unexpected patterns.

Assumptions Embedded in the Message

Every diagnostic message carries assumptions, and this one is no exception:

Assumption 1: "Connection reset by peer" at 100 workers indicates overload. This is the most natural interpretation, but it is not the only possibility. Connection resets can also occur due to firewall timeouts, keepalive misconfigurations, or application-level crashes that cause the kernel to send RST packets. The assistant is implicitly assuming a capacity-related cause, which is reasonable given the load test context but is still an assumption that must be validated.

Assumption 2: The bottleneck is in the system being tested, not the test tool itself. The load test tool (ritool) could itself be the source of connection resets if it exhausts local resources. The assistant's framing assumes the errors reflect server-side behavior.

Assumption 3: 100 workers is a meaningful stress level. The assistant has chosen 100 as the next step after 10, representing an order-of-magnitude increase. This assumes the system should handle at least 100 concurrent connections, which is a modest target for a distributed S3 system.

Assumption 4: The errors are reproducible and not transient. By investigating the pattern rather than dismissing individual resets, the assistant assumes the errors represent a systematic issue rather than random network noise.

What This Message Requires the Reader to Know

To fully understand this message, one needs:

• Knowledge of the system architecture: The three-layer design (S3 proxy on port 8078 → Kuri storage nodes → YugabyteDB) is essential context. The connection reset at the proxy port suggests the bottleneck is at or before the proxy layer.
• Understanding of the load testing methodology: The test creates objects, writes them via PUT requests, and then verifies them via GET requests. The "VERIFY READ ERROR" prefix indicates these failures occurred during the verification phase, after writes completed.
• Familiarity with Docker networking: The localhost:8078 endpoint goes through Docker's port mapping, which by default uses a userland proxy that can become a bottleneck under high concurrency. This specific detail becomes critical in the subsequent fix.
• Awareness of the earlier false-corruption investigation: The team had just concluded that earlier "verify errors" were timeouts rather than actual data corruption. This message represents a new, distinct error pattern that demands separate investigation.

What This Message Creates

The message produces several forms of knowledge:

Immediate diagnostic data: The output confirms that connection resets are widespread across multiple workers (W49, W73, W67, and presumably many more in the truncated output). The errors all reference the same localhost:8078 endpoint, narrowing the focus to the proxy layer.

A clear next-step direction: The observation that errors occur at 100 workers but not at 10 workers establishes a throughput threshold. The assistant now knows that the system's breaking point lies somewhere between 10 and 100 concurrent connections, and the failure mode is TCP-level connection resets rather than application-level errors.

A hypothesis to test: "System overload" is a broad hypothesis that can be refined. The assistant will go on to identify the Docker userland proxy as the specific bottleneck and will resolve it by switching to host networking — a decision that flows directly from this message's observation.

Documentation of system behavior under load: For anyone reading the session log, this message captures the system's performance ceiling before optimization. The connection reset pattern becomes a baseline against which future improvements can be measured.

The Broader Narrative Arc

This message sits at a crucial juncture in the session. The chunk summary tells us that the assistant will go on to:

1. Implement a CQLBatcher to optimize the YCQL write path
2. Switch Docker networking to host mode to eliminate the userland proxy bottleneck
3. Achieve throughput scaling to ~334 MB/s at 100 workers The connection resets observed in message 1175 are the direct catalyst for the Docker networking change. Without this diagnostic observation, the team might have attributed the throughput ceiling to database contention or application logic, spending optimization effort in the wrong place. The message's value lies not in its length but in its precision: it correctly identifies the layer at which the bottleneck occurs (the network/proxy layer) by reading the error signature.

Conclusion

Message 1175 is a masterclass in concise, targeted diagnostic communication. In just three sentences and a command invocation, the assistant accomplishes several things: it reports an observation, formulates a hypothesis, and initiates evidence gathering. The message is simultaneously a status update and a debugging action — it tells the collaborator what was found and what is being done about it.

The "connection reset by peer" errors at 100 workers are not just a problem to be fixed; they are a signal about the system's architecture. The Docker userland proxy, invisible during low-concurrency testing, reveals itself as a bottleneck only when pushed. This is the nature of distributed systems engineering: the architecture's weakest link often announces itself not through obvious failures but through subtle changes in error patterns as load increases. The assistant's ability to read those signals — to distinguish between corruption, timeout, and connection-reset failure modes — is the skill that drives the session forward.

In the end, this brief message is a reminder that debugging is not about finding what is broken but about understanding what the system is telling you. The connection resets spoke, and the assistant listened.