The Moment of Architectural Clarity: Diagnosing Traffic Imbalance in a Distributed S3 Cluster

The Message

Assistant: You're right — the loadtest only hit kuri1 directly. In a proper setup, traffic should be distributed. Let me check if we can run against both nodes or if there's a load balancing issue:

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``bash ssh 10.1.232.83 "sudo journalctl -u kuri --since '2 min ago' --no-pager 2>&1 | grep -E 'PUT|GET|S3' | tail -10" ``

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`` Jan 31 22:33:11 fgw-ribs1 kuri[17863]: 2026-01-31T22:33:11.594Z ERROR gw/s3 s3/server.go:100 Error handling HTTP PUT request {"URL": "/loadtest/loadtest/w0/obj796-1769898791576787757", "error": "error putting object: failed to update index for loadtest/loadtest/w0/obj796-1769898791576787757: inserting s3 object: context canceled"} ``

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``bash ssh 10.1.232.84 "sudo journalctl -u kuri --since '2 min ago' --no-pager 2>&1 | grep -E 'PUT|GET|S3' | tail -10" ``

This message, appearing at a critical juncture in a complex distributed systems deployment session, represents far more than a routine diagnostic check. It is the moment when an architectural assumption collides with operational reality. The assistant has just run a load test against a freshly deployed QA cluster for the FGW (Filecoin Gateway) distributed S3 storage system, and the user has pointed out a glaring anomaly: only one of the two storage nodes received any traffic. This message is the assistant's acknowledgment of that problem and the beginning of a diagnostic journey that would ultimately lead to a fundamental architectural correction.

Why This Message Was Written: The User's Critical Observation

The immediate trigger for this message was the user's observation in the preceding turn: "Only kuri1 getting traffic seems wrong." This single sentence exposed a flaw in the assistant's testing methodology. The assistant had just run the ritool loadtest command pointed directly at http://10.1.232.83:8079 — the S3 endpoint of kuri1, one of the two storage nodes. The load test completed successfully from a functional perspective (objects were written and read), but the user immediately recognized that something was architecturally off.

The assistant's response — "You're right — the loadtest only hit kuri1 directly. In a proper setup, traffic should be distributed" — is an immediate concession that carries significant weight. It signals that the assistant understands the architectural ideal (traffic distribution across nodes) but has been operating in a mode that violates that ideal. The message is written to validate the user's concern, investigate the extent of the problem, and gather evidence to inform the next corrective action.

The deeper motivation for this message is the assistant's recognition that the current deployment architecture is incomplete. The system was designed with a three-layer hierarchy: S3 proxy frontends → Kuri storage nodes → YugabyteDB. But in the haste to get something running, the assistant had been testing against the Kuri nodes directly, bypassing the proxy layer entirely. This message represents the moment when that shortcut becomes untenable.

Assumptions Made: Direct Node Access in a Distributed System

Several assumptions underpin the assistant's actions leading up to this message, and the message itself reveals their inadequacy. The most significant assumption was that pointing a load test at a single Kuri node's S3 endpoint would constitute a valid test of the distributed system. This assumption conflates "the system works on one node" with "the distributed system works correctly." In a properly functioning distributed S3 cluster, object placement is determined by a routing algorithm that considers which node "owns" a given object based on its key or metadata. By testing against a single node, the assistant was only validating that node's local blockstore and S3 handler, not the cluster's ability to serve objects from any node.

A second, subtler assumption was that the FGW_BACKEND_NODES environment variable, which the assistant had recently added to both nodes, would somehow cause traffic to be distributed. This variable was added to make the Cluster Topology API render correctly in the Web UI, but it does not, by itself, create a load-balancing frontend. The assistant appears to have conflated topology awareness (knowing about other nodes) with traffic routing (directing requests to the correct node).

A third assumption was that the "context canceled" errors visible in kuri1's logs were the primary problem to investigate. In reality, those errors — "failed to update index for ... inserting s3 object: context canceled" — are a secondary symptom of the deeper architectural issue. When a Kuri node receives an S3 PUT request directly, it attempts to write the object to its local blockstore and update the global index in YugabyteDB. If the database operation times out or is canceled (perhaps due to connection pool exhaustion or transaction conflicts under concurrent load), the PUT fails. But the more fundamental problem is that kuri2 never even saw the request.

Mistakes and Incorrect Assumptions

The most significant mistake visible in this message is the architectural bypass. The assistant had previously designed and built an S3 proxy frontend component (the s3-proxy binary) specifically to handle request routing across backend nodes. Yet when it came time to test, the assistant pointed the load test directly at a Kuri node rather than deploying and using the proxy. This is a classic deployment pitfall: the component that handles cross-cutting concerns (routing, load balancing, request distribution) is often the last thing to be deployed because it seems like "overhead" when you just want to see if the basic storage works.

The "context canceled" errors in the log output reveal another mistake: the assistant had not properly characterized the system's behavior under concurrent load. The load test ran with 5 concurrent workers for 30 seconds, and the errors suggest that the YugabyteDB index updates were failing under this modest load. This could indicate insufficient database connection pooling, missing retry logic, or transaction conflicts — all of which would need to be addressed before the system could handle real traffic.

A more subtle error is visible in the diagnostic approach itself. The assistant checks logs on both nodes using journalctl with a --since '2 min ago' filter. This is reasonable for a quick check, but it assumes that the relevant log entries would appear within that window. If the load test ran longer ago than 2 minutes, or if log rotation had already occurred, the evidence could be misleading. More importantly, the assistant checks for PUT|GET|S3 patterns but does not check for connection-level events or HTTP request counts at the network layer, which would have provided a more complete picture of traffic distribution.

Input Knowledge Required

To fully understand this message, one needs knowledge of several domains. First, an understanding of distributed S3 architectures — specifically the concept of stateless frontend proxies that route requests to backend storage nodes based on object metadata. Without this context, the significance of "only kuri1 getting traffic" is lost.

Second, familiarity with the FGW system's component architecture is essential. The system uses Kuri nodes as storage backends that each maintain a local blockstore (a content-addressed storage for data blocks), while metadata about object locations is stored in a shared YugabyteDB cluster. The S3 proxy frontend is responsible for reading the metadata to determine which backend node holds a given object, then proxying the request to that node.

Third, knowledge of the deployment topology matters. The QA cluster spans three physical nodes: a head node (10.1.232.82) running YugabyteDB, and two Kuri nodes (10.1.232.83 and 10.1.232.84). The load test was run from the development machine (not shown in this message but evident from the context), and the S3 endpoint was accessed over the local network.

Fourth, understanding the tooling is necessary. The ritool loadtest command is a custom-built load testing utility that generates random objects of configurable sizes, writes them to S3, and optionally reads them back for verification. It uses concurrent workers and can be configured with read/write ratios. The journalctl command with --since and --no-pager flags is standard Linux systemd log inspection.

Output Knowledge Created

This message produces several important outputs. The most immediate is the confirmation that traffic is indeed not being distributed: kuri1 shows S3 activity (including errors), while kuri2 shows none (the empty output from the second journalctl command). This transforms the user's suspicion into documented evidence.

The error messages themselves are valuable diagnostic output. The "context canceled" errors during PUT operations point to a problem in the database interaction layer. The error chain — "error putting object: failed to update index for ... inserting s3 object: context canceled" — reveals the exact code path where the failure occurs: the S3 handler receives a PUT request, attempts to write the object to the local blockstore, then tries to update the S3 object index in YugabyteDB, and the database operation is canceled.

Perhaps most importantly, this message creates the knowledge that the deployment is incomplete. The assistant acknowledges that "in a proper setup, traffic should be distributed," which implicitly acknowledges that the current setup is not proper. This creates a clear action item: deploy the S3 proxy frontend and route traffic through it rather than directly to individual Kuri nodes.

The message also establishes a diagnostic methodology: comparing logs across nodes to identify traffic imbalances. This pattern — check node A, check node B, compare — is a fundamental distributed systems debugging technique that the assistant demonstrates effectively.

The Thinking Process: From Symptom to Diagnosis

The reasoning visible in this message follows a clear arc. It begins with acknowledgment and agreement ("You're right"), which establishes shared understanding with the user. The assistant then articulates the architectural ideal ("in a proper setup, traffic should be distributed"), which frames the problem in terms of the gap between current and desired states.

The next step is investigative: "Let me check if we can run against both nodes or if there's a load balancing issue." This reveals that the assistant is considering two possible explanations. One is a configuration problem — perhaps the load test tool can be pointed at multiple endpoints. The other is a load balancing issue — perhaps the system should be distributing traffic automatically but isn't configured to do so.

The diagnostic commands reveal the assistant's thinking about what evidence would be relevant. Checking for PUT|GET|S3 patterns in the logs targets the S3 request layer specifically. The assistant is looking for evidence of requests arriving at each node, not for system health metrics or performance data. This is appropriate for the immediate question: "Is traffic reaching both nodes?"

The choice of tail -10 limits the output to the most recent entries, suggesting the assistant expects a manageable number of relevant log lines. The fact that kuri1 shows multiple error lines (truncated in the message) while kuri2 shows nothing confirms the traffic imbalance decisively.

What is not visible in the thinking is consideration of alternative explanations. Could the load test be working correctly but kuri2's logs not capturing the events due to log level configuration? Could the objects be routing to kuri1 because of consistent hashing that happens to place all test objects on that node? The assistant does not explore these possibilities, instead accepting the straightforward interpretation that traffic is not reaching kuri2.

Broader Implications for Distributed System Testing

This message illustrates a pattern that recurs throughout distributed systems development: the tendency to test components in isolation rather than testing the system as a whole. The assistant had built and tested individual pieces — the Kuri storage nodes, the YugabyteDB cluster, the S3 proxy — but the integration test (the load test) bypassed the critical routing layer.

The message also highlights the importance of user vigilance in distributed systems debugging. The user's observation — "Only kuri1 getting traffic seems wrong" — might seem obvious in retrospect, but it required understanding the architectural intent and comparing it against observed behavior. In complex deployments, such observations are often the difference between a system that "works" and a system that works correctly.

The "context canceled" errors hint at another distributed systems challenge: the difficulty of distinguishing between transient failures and systemic problems. Under concurrent load, database transactions can fail for many reasons — connection timeouts, deadlock detection, transaction conflicts. The assistant does not yet know whether these errors are a symptom of the traffic imbalance (perhaps kuri1 is overwhelmed by all the traffic) or an independent problem with the database interaction layer.

Conclusion

This message, seemingly a simple diagnostic check, is in fact a pivotal moment in the deployment session. It represents the transition from "the system is running" to "the system is running correctly." The assistant acknowledges the architectural gap, gathers evidence to confirm the problem, and sets the stage for the corrective action that follows: deploying the S3 proxy frontend on the head node and routing all traffic through it.

The message teaches a valuable lesson about distributed systems testing: a system that functions on a single node is not yet a distributed system. The proxy layer, which handles request routing and load distribution, is not optional infrastructure — it is the essential component that transforms a collection of independent storage nodes into a coherent cluster. The assistant's willingness to acknowledge the mistake and investigate systematically, rather than defending the approach, is a model of effective debugging practice.