Reading the Tea Leaves: Diagnosing Load Distribution in a Distributed S3 Proxy

The Message

[assistant] The logs show:
1. **Both backends are being used** - requests going to both kuri_01 (10.1.232.83) and kuri_02 (10.1.232.84)
2. The "context canceled" errors are just test timeout (when loadtest ends abruptly)
3. Proxy has 2 backend nodes configured

Let me find the actual proxy config:
[bash] ssh 10.1.232.82 "find /data /etc /opt -name '*.yaml' -o -name '*.yml' -o -name 'settings.env' 2>/dev/null | xargs grep -l 'kuri\|backend\|8079' 2>/dev/null | head -5"

The Context: A Distributed Storage System Takes Shape

This message, message 2099 in a sprawling coding session, arrives at a moment of transition. The assistant and user have just completed deploying a three-node QA cluster for FGW (Filecoin Gateway), a horizontally scalable S3-compatible storage system built on top of the Filecoin decentralized storage network. The architecture is layered: a stateless S3 proxy frontend on a "head" node (10.1.232.82) routes requests to two Kuri storage backend nodes (10.1.232.83 and 10.1.232.84), which in turn store metadata and object data in a shared YugabyteDB instance.

The previous messages tell a story of rapid iteration. The S3 proxy was deployed via Ansible, tested with curl commands, and then subjected to a load test that wrote and read thousands of objects. The load test completed successfully with zero data corruption—a significant validation of the system's correctness. But a new question emerged: was the load being distributed evenly across the two backend nodes?

The assistant checked Prometheus metrics on both Kuri nodes and discovered an imbalance. Kuri_01 was handling approximately 62% of PUT requests and 65% of GET requests, while Kuri_02 handled the remaining 38% and 35% respectively. This uneven distribution raised a natural engineering concern. Was the routing algorithm biased? Was one node being favored by the hash-based routing scheme? Was there a configuration issue?

Message 2099 is the assistant's attempt to answer those questions.

Why This Message Was Written: The Reasoning and Motivation

The immediate trigger for this message was the assistant's prior investigation. After running the load test, the assistant checked metrics on both Kuri nodes and found the 62/38 split. The natural next step was to look at the proxy's logs and configuration to understand why the distribution was uneven. Message 2099 is the opening move in that diagnostic process.

But the deeper motivation is more interesting. This message represents a shift in the assistant's investigative strategy. Up to this point, the assistant had been verifying that the system worked at all—that objects could be written and read, that the proxy could route to both backends, that the load test completed without corruption. Those are binary, pass/fail checks. Now the assistant is moving into a more nuanced mode of inquiry: characterizing the quality of the system's behavior. The question is no longer "does it work?" but "how well does it work?" and "is it working as designed?"

This shift is significant because it reflects a maturing understanding of what matters in a distributed system. Correctness is the foundation, but once correctness is established, operational characteristics like load distribution, fairness, and predictability become paramount. The assistant is thinking like a production engineer, not just a developer.

The message also reveals the assistant's desire to close the loop on the investigation. The metrics showed an imbalance, but metrics alone don't explain the cause. The assistant needs to connect the observed behavior to the configuration that drives it. That's why the message ends with a command to find the proxy configuration files—the assistant needs to see the actual routing configuration to understand whether the imbalance is expected, configurable, or a bug.

How Decisions Were Made: The Diagnostic Approach

The decision-making in this message is subtle but important. The assistant makes three interpretive claims about the log output, then decides on a next action.

First, the assistant interprets the log evidence: "Both backends are being used." This is a confirmation that the proxy's routing is functional—it's not stuck sending all traffic to a single node. This is important because it rules out the simplest failure mode: a misconfigured backend list or a network partition isolating one node.

Second, the assistant explains away the "context canceled" errors: "just test timeout (when loadtest ends abruptly)." This is a diagnostic judgment call. The assistant could have interpreted these errors as a serious problem—perhaps the proxy was canceling requests mid-flight, or the backends were timing out. Instead, the assistant correctly identifies them as artifacts of the load test's shutdown sequence. When the load test finishes its 15-second duration, it terminates all in-flight requests, which produces "context canceled" errors in the proxy logs. These are harmless.

Third, the assistant notes that the "Proxy has 2 backend nodes configured." This confirms that the configuration is correct at a high level—the proxy knows about both Kuri nodes.

Having made these three observations, the assistant decides that the next step is to find the actual configuration files. The decision to search for YAML, YML, and settings.env files containing "kuri", "backend", or "8079" is a targeted diagnostic move. The assistant knows that the proxy's routing behavior—including how it selects which backend to send a request to—is determined by configuration. By finding and reading the config files, the assistant hopes to understand the routing algorithm and determine whether the observed imbalance is expected or problematic.

Assumptions Made by the Assistant

This message rests on several assumptions, some explicit and some implicit.

The most visible assumption is that the "context canceled" errors are benign test artifacts. This is a reasonable assumption given the context—the load test ran for a fixed duration and then terminated abruptly—but it's not proven. There could be a scenario where the proxy is canceling requests prematurely due to a misconfigured timeout or a backend that's too slow. The assistant is implicitly trusting that the load test's behavior is the cause, rather than a system defect.

The assistant also assumes that the proxy's routing configuration is stored in a file that can be found by searching for common patterns. This is a reasonable assumption for a system deployed via Ansible with standardized configuration paths, but it's not guaranteed. The proxy might use environment variables, a database-backed configuration, or a compiled-in default. The search command reflects the assistant's mental model of how the system is structured.

A deeper assumption is that understanding the configuration will explain the load distribution imbalance. The assistant is implicitly assuming that the routing algorithm is deterministic and configuration-driven—that if the config says "round-robin" or "hash-based" or "random," the observed distribution will match the expected distribution. This assumption might not hold if there are other factors at play, such as network latency differences, CPU load on the backends, or the proxy's internal connection pooling behavior.

The assistant also assumes that the proxy's logs are the right place to look for routing information. The command to check logs (in the previous message) and the command to find config files (in this message) both reflect an assumption that the proxy's behavior is observable through these channels. If the proxy doesn't log routing decisions, or if the configuration is embedded in the binary, the assistant's approach won't yield answers.

Mistakes or Incorrect Assumptions

The most notable potential mistake in this message is the assumption that the configuration files will be easy to find. The command find /data /etc /opt -name '*.yaml' -o -name '*.yml' -o -name 'settings.env' searches three directories, but the proxy's configuration might be stored elsewhere. The Ansible deployment earlier in the session created settings files in /data/fgw/config/ and /data/fgw/s3-proxy/, but the assistant's search doesn't explicitly include /data/fgw/—it includes /data broadly, which should catch subdirectories, but the -maxdepth flag isn't set, so the search could be slow or miss files if the directory tree is deep.

More subtly, the assistant may be making an incorrect assumption about what to look for in the configuration. The search patterns "kuri", "backend", and "8079" are reasonable keywords for routing configuration, but the proxy might use different terminology. If the routing configuration uses terms like "upstream", "target", "peer", or "shard", the grep patterns won't match.

There's also a potential mistake in the interpretive framing. The assistant states that "Both backends are being used" as if this is a conclusion, but the evidence for this claim isn't presented in the message itself. The assistant is presumably drawing on the log output from the previous command (msg 2098), which showed routing to both backends. But the message doesn't quote the relevant log lines, so the reader has to trust the assistant's interpretation.

The "context canceled" explanation is also worth scrutinizing. While it's likely correct that these errors are test artifacts, the assistant doesn't verify this by, say, running a longer test and checking whether the errors disappear. The explanation is plausible but unconfirmed.

Input Knowledge Required

To understand this message, a reader needs significant context about the system being built.

First, they need to understand the architecture: a stateless S3 proxy frontend that routes requests to multiple Kuri storage backend nodes. The proxy is the entry point for S3 API calls, and it's responsible for distributing requests across the backends. The backends handle the actual storage operations and communicate with YugabyteDB for metadata.

Second, they need to know the network topology: the head node at 10.1.232.82 runs the proxy and YugabyteDB, while kuri_01 at 10.1.232.83 and kuri_02 at 10.1.232.84 run the Kuri storage service. The proxy listens on port 8078, and the backends listen on port 8079 for S3 requests.

Third, they need to understand the recent history: a load test was run that produced uneven load distribution (62/38), and the assistant is now investigating why. The "context canceled" errors appeared in the proxy logs during that test.

Fourth, they need familiarity with the tooling: Ansible for deployment, systemd for service management, journalctl for log viewing, Prometheus for metrics, and the find and xargs commands for file searching.

Without this context, the message would seem like a cryptic series of observations followed by a file search. With the context, it becomes a clear diagnostic step in a larger investigation.

Output Knowledge Created

This message creates several pieces of knowledge, though none of them are definitive conclusions.

First, it establishes that both backends are receiving traffic. This confirms that the proxy's routing is functional at a basic level—it's not a single-node system in disguise.

Second, it provides a plausible explanation for the "context canceled" errors, reducing the likelihood that they indicate a real problem. This is valuable because it prevents a wasted investigation into a non-issue.

Third, it creates a plan for the next diagnostic step: find and examine the proxy configuration files. The command output (which would come after this message) would reveal where the configuration is stored and potentially what routing algorithm is in use.

Fourth, and perhaps most importantly, it documents the assistant's reasoning process. The three numbered observations serve as a mini-analysis that connects the log evidence to the assistant's conclusions. This makes the assistant's thinking transparent and allows the user to follow along and correct any misinterpretations.

The knowledge created here is intermediate—it's not the final answer to the load distribution question, but it's a necessary stepping stone. The assistant is building a chain of reasoning: the logs show both backends are used, the errors are benign, the configuration is correct at a high level, so the next step is to examine the configuration in detail. Each step narrows the search space and brings the investigation closer to a root cause.

The Thinking Process Visible in the Message

The structure of this message reveals the assistant's thinking process with unusual clarity. The three numbered points are not just a summary—they're a reasoning chain.

Point 1 ("Both backends are being used") establishes a baseline fact. The assistant is checking the most basic hypothesis: is the proxy actually routing to both nodes? If only one node were receiving traffic, the investigation would take a different direction (checking backend connectivity, configuration, or network routing). By confirming that both nodes are active, the assistant rules out the simplest failure modes.

Point 2 ("The 'context canceled' errors are just test timeout") addresses a potential distraction. The assistant anticipates that the user might see these errors and worry about them. By providing a clear, plausible explanation, the assistant preempts a side investigation and keeps the focus on the main question. This is a sign of experienced debugging—knowing which signals to pay attention to and which to dismiss.

Point 3 ("Proxy has 2 backend nodes configured") confirms that the configuration matches expectations. If the proxy thought it had only one backend, or if it had three configured, that would explain the observed behavior. By confirming that the configuration is correct, the assistant narrows the possible causes.

The final command—searching for configuration files—is the logical next step. The assistant has ruled out the simple explanations and now needs to examine the routing algorithm itself. The choice of search terms ("kuri", "backend", "8079") shows that the assistant has a mental model of what the configuration should contain and is using that model to guide the search.

What's particularly interesting is what the assistant doesn't do. The assistant doesn't immediately jump to conclusions about the routing algorithm. It doesn't assume that the imbalance is a bug or that the configuration is wrong. Instead, it methodically gathers evidence, rules out distractions, and plans the next investigation step. This is disciplined debugging.

Conclusion

Message 2099 is a small but revealing moment in a larger engineering effort. It captures the transition from verifying correctness to characterizing performance, from "does it work?" to "how well does it work?" The assistant's reasoning is methodical: confirm the basics, explain away noise, verify configuration, then dig deeper. The message is a snapshot of an engineer thinking through a problem in real time, making judgments about what matters and what doesn't, and planning the next move.

The investigation it begins—into the proxy's routing configuration and load distribution—is the kind of work that separates a prototype from a production system. The system works, but making it work well requires understanding the details. This message is the first step in that journey.