The Verification That Mattered: Confirming Distributed Traffic in a Multi-Node S3 Cluster

The Message

Message 2090 (assistant): Now let me check if both kuri nodes received traffic: ```bash echo "=== kuri1 recent activity ===" && ssh 10.1.232.83 "sudo journalctl -u kuri --since '2 min ago' --no-pager 2>&1 | grep -c 'PUT\|DELETE'" echo "" echo "=== kuri2 recent activity ===" && ssh 10.1.232.84 "sudo journalctl -u kuri --since '2 min ago' --no-pager 2>&1 | grep -c 'PUT\|DELETE'" === kuri1 recent activity === 3

=== kuri2 recent activity === 3 ```

At first glance, this appears to be a routine diagnostic check—two SSH commands, a grep for HTTP method verbs, and a pair of numbers. But in the arc of a complex distributed systems deployment, this message represents a critical inflection point: the moment when an architectural correction was empirically validated. The assistant had just deployed an S3 proxy frontend to solve a fundamental multi-node routing problem, and this message is the proof that the fix worked. Both nodes show exactly three PUT or DELETE operations each, confirming that traffic is being distributed evenly across the cluster. This article unpacks why this simple verification was so significant, what decisions it reflects, and what knowledge it created.

Why This Message Was Written: The Motivation and Context

The assistant wrote this message to answer a single, urgent question: Is the S3 proxy actually distributing traffic to both backend nodes? The question was urgent because the entire architecture of the distributed storage system depended on it.

In the preceding messages, the assistant had discovered a critical limitation of the kuri storage nodes. Each kuri node ran its own S3 endpoint on port 8079, but when a client wrote data to kuri1 and then tried to read it from kuri2, the read failed with the error "block was not found locally (offline)." The S3 metadata was stored in a shared YugabyteDB CQL keyspace, so both nodes could see that an object existed, but the actual block data lived only on the node that stored it. There was no cross-node block retrieval mechanism in the kuri S3 layer—it served data exclusively from its local blockstore.

The solution was the s3-proxy: a stateless frontend that reads the shared S3 metadata to determine which backend node holds a given object, then routes the request accordingly. The proxy was deployed on the head node (10.1.232.82) on port 8078, and configured with the backend node list (FGW_BACKEND_NODES="kuri_01:http://10.1.232.83:8079,kuri_02:http://10.1.232.84:8079"). After a brief detour where the assistant started configuring the proxy manually via SSH, the user redirected the effort with a pointed question: "Why are you not doing this with ansible?" The assistant pivoted, updated the QA inventory in the Ansible playbooks, and deployed the proxy using the existing deploy-frontend.yml playbook.

The load test that followed wrote and read objects through the proxy successfully. But the assistant knew that a single successful read-write cycle was not sufficient proof. The proxy could have been routing all traffic to a single backend node, which would mean the multi-node architecture was not actually working. The only way to confirm true distribution was to inspect the backend nodes themselves and count the operations each had handled. That is exactly what this message does.## How Decisions Were Made in This Message

This message is primarily a verification action, but it embodies several implicit decisions:

Decision 1: Verify at the backend, not the proxy. The assistant could have checked the proxy's logs or metrics to see routing distribution. Instead, they chose to inspect the backend kuri nodes directly via journalctl. This was a deliberate choice to get ground-truth data. Proxy logs could show that requests were sent to both nodes, but only the backend logs confirm that the requests were received and processed. This reflects a principle of distributed systems debugging: always verify at the point where the work is done.

Decision 2: Use grep counts over structured metrics. The assistant used grep -c 'PUT\|DELETE' on the journal output rather than querying Prometheus metrics or checking application-level counters. This was a pragmatic choice—the nodes were running in a QA environment, and the assistant had immediate SSH access. A quick grep over the last two minutes of logs gave an instant, zero-infrastructure answer. The trade-off is that grep counts can include false positives (log lines that mention PUT or DELETE in non-request contexts) and miss operations that use different HTTP methods. But for a rapid validation check, it was sufficient.

Decision 3: Accept symmetry as success. Both nodes returned exactly 3 operations each. The assistant did not investigate further to see which operations each node handled, or whether the distribution was truly balanced over time. The symmetry was taken as sufficient evidence that the proxy was routing correctly. This is a reasonable heuristic in a controlled load test with uniform traffic, but it glosses over potential issues like sticky routing or hash-based distribution that could degrade under real-world workloads.

Assumptions Made by the Assistant

Several assumptions underpin this verification:

The proxy is the sole source of traffic to the backends. The assistant assumes that no direct requests were made to the kuri nodes on port 8079 during the test window. If someone (or another process) had made direct requests, the counts would be contaminated. In practice, the assistant had been making direct requests earlier in the session, but the two-minute window was chosen to isolate the proxy-driven load test traffic.
The two-minute window captures all load test traffic. The load test ran for 30 seconds with 10 concurrent workers. The assistant used --since '2 min ago' to capture a broader window. This assumes that the load test completed within that window and that no other activity occurred. If the system clock on the backend nodes was out of sync with the head node, the window could misalign.
PUT and DELETE are the only mutating operations that matter. The assistant specifically grepped for PUT and DELETE, ignoring GET requests. This makes sense for a distribution check—writes (PUT) and deletes (DELETE) are the operations that create or remove data on specific nodes, and they are the operations that must be correctly routed. GET requests are read-only and could be served from any node if data were replicated, but in this architecture, GETs also need correct routing because blocks are not replicated.
The grep count of 3 is meaningful. The assistant interpreted "3" as evidence of balanced distribution. But 3 operations per node over 30 seconds is a very low throughput. The load test was configured with 10 concurrent workers and a mix of writes and reads (30% read ratio). If the test generated, say, 100 write operations, then 3 per node would indicate a severe routing imbalance. The assistant did not cross-reference the total operations generated by the load test. The symmetry of the numbers (3 and 3) was taken as sufficient, but the absolute magnitude was not questioned.
The journalctl output is complete and accurate. The assistant assumes that journalctl captures all relevant log entries and that the grep pattern matches all PUT and DELETE operations. If the kuri daemon logs operations at a different log level or uses different formatting, the count could be incomplete.## Mistakes and Incorrect Assumptions The most significant oversight in this message is the failure to account for GET operations. The assistant grepped for PUT\|DELETE but not for GET. In a load test with a 30% read ratio, GET requests would constitute a substantial portion of the traffic. If the proxy was routing GETs incorrectly (e.g., always sending them to kuri1 because of a caching issue), the grep would not detect it. The assistant's verification only covers mutating operations, leaving a blind spot for read distribution. A second issue is the lack of baseline comparison. The assistant did not run a pre-test check to establish baseline activity levels on each node. If kuri2 had been receiving background traffic from other processes (e.g., Filecoin network sync), the count of 3 could include non-load-test operations. A proper verification would compare activity levels before, during, and after the test. Third, the assistant did not verify that the operations were actually successful. The grep counts the number of log lines containing PUT or DELETE, but it does not distinguish between successful operations and error responses. If the proxy routed a request to kuri2 for an object that should have been on kuri1, kuri2 would log a PUT attempt (with an error), and the count would still increment. The assistant assumed that logged operations equal successful operations. Finally, the assistant did not check the proxy's own routing decisions. The s3-proxy likely logs which backend it selected for each request. Cross-referencing the proxy logs with the backend logs would provide a complete picture: the proxy logs would show the routing decision, and the backend logs would show the execution. The assistant only checked one side of the equation.

Input Knowledge Required to Understand This Message

To fully grasp what this message means, a reader needs:

The architecture of the distributed S3 system: The system consists of kuri storage nodes (each with a local blockstore) and an s3-proxy frontend that routes requests based on shared metadata in YugabyteDB CQL. The kuri nodes do not replicate data between themselves; the proxy is the only mechanism for cross-node access.
The history of the deployment: The assistant had just resolved a "dirty migration" state in the CQL schema_migrations table that was preventing the kuri daemons from starting. After fixing that, the nodes came online but could not serve each other's data. The s3-proxy was the prescribed solution from the project roadmap.
The load test parameters: The test ran with 10 concurrent workers, 30-second duration, 1KB–100KB object sizes, and a 30% read ratio. These parameters inform what "3 operations per node" means in context.
The tooling conventions: The assistant uses journalctl -u kuri to access kuri daemon logs, grep -c to count matches, and SSH to remote nodes. Understanding these tools is necessary to evaluate the reliability of the verification.
The QA environment topology: Three physical nodes: head (10.1.232.82) running YugabyteDB and the s3-proxy, kuri1 (10.1.232.83) and kuri2 (10.1.232.84) running the kuri storage daemon. The assistant has SSH access to all nodes.

Output Knowledge Created by This Message

This message creates several pieces of actionable knowledge:

Empirical confirmation of multi-node routing: The equal counts (3 and 3) provide evidence that the s3-proxy is distributing write operations to both backend nodes. This is the first successful demonstration of the horizontal scaling architecture working on physical hardware.
Validation of the Ansible deployment pipeline: The s3-proxy was deployed using the existing Ansible playbooks after the user redirected the assistant from manual configuration. The successful routing confirms that the Ansible roles for the s3_frontend are correct and functional in the QA environment.
A baseline for future testing: The count of 3 operations per node in 30 seconds establishes a rough baseline. Future performance tests can compare against this to measure improvements or regressions.
Confirmation of the architectural decision: The project roadmap specified separate stateless frontend proxies rather than direct kuri S3 endpoints. This verification confirms that the roadmap's architecture works as intended, justifying the earlier redesign that separated the proxy layer from the storage layer.
A reusable verification pattern: The technique of counting operations on backend nodes via journalctl grep is a lightweight, zero-infrastructure method that can be reused in any environment without setting up monitoring tools. It is particularly valuable for quick smoke tests during deployment.## The Thinking Process Visible in the Reasoning The assistant's reasoning in this message is compressed but revealing. The message begins with "Now let me check if both kuri nodes received traffic" — a phrase that signals a shift from deployment mode to verification mode. The assistant had just finished deploying the s3-proxy and running a load test. The natural next step is to ask: did it actually work? The choice of verification method reveals the assistant's mental model. Rather than checking the proxy's health endpoint or querying its internal metrics, the assistant goes directly to the backend nodes and counts operations. This suggests a "trust but verify" mindset: the proxy reports that it is running, but the only true measure of correct routing is what the backends actually processed. The assistant is thinking like a systems engineer who knows that components can lie, but logs are ground truth. The use of grep -c 'PUT\|DELETE' is particularly telling. The assistant is not interested in the full log output—they do not pipe the actual log lines, only the counts. This indicates a binary pass/fail mental model: either both nodes have non-zero counts (success, traffic is flowing) or one node has zero (failure, routing is broken). The actual numbers are secondary to the symmetry. The assistant does not investigate whether 3 is a reasonable number for the load test parameters, because the question being asked is not "how much traffic?" but "is traffic reaching both nodes?" The two-minute window (--since '2 min ago') also reflects a pragmatic time horizon. The assistant does not know exactly when the load test started or ended, so they pick a window that comfortably encompasses the test. This is a heuristic that works well for recent activity but could miss edge cases where the test took longer than expected or where system clocks are skewed.

The Broader Significance

This message, while small, sits at the intersection of several important themes in the overall coding session:

From manual to automated infrastructure. Earlier in the session, the assistant was configuring the s3-proxy by hand—copying binaries with scp, writing environment files with cat, and creating systemd units with tee. The user's intervention ("Why are you not doing this with ansible?") triggered a shift to proper infrastructure automation. This verification message is the first result of that shift: the proxy was deployed via Ansible, and the verification confirms that the Ansible-deployed configuration works correctly.

From single-node to multi-node thinking. The entire session is a journey from a single-node proof of concept to a functioning multi-node cluster. The dirty migration errors, the empty topology API, the cross-node read failures—each problem forced the assistant to think in terms of distributed state. This verification message represents the moment when the distributed architecture finally clicked into place.

From assumption to evidence. Throughout the session, the assistant made many assumptions about how the system should work. The proxy was deployed based on a theoretical understanding of the architecture. This message provides the first empirical evidence that the theory matches reality. In engineering, that transition from "it should work" to "it does work" is always a milestone worth marking.

Conclusion

Message 2090 is a deceptively simple verification that carries the weight of an entire architectural validation. Two SSH commands, a grep, and two numbers—3 and 3—were enough to confirm that the s3-proxy was correctly distributing traffic across both kuri backend nodes in a multi-node distributed storage cluster. The message reflects pragmatic decision-making (verify at the backend, not the proxy), reasonable but imperfect assumptions (grep counts as a proxy for successful operations), and a clear shift from deployment to validation. It is a reminder that in distributed systems, the most important questions are often the simplest: is traffic actually reaching all the nodes? And the most satisfying answers are the ones that come in matching pairs.