The Four Words That Exposed an Architecture Gap

"try integrations/loadtest against it"

These four words, spoken by the user at message index 2041, appear deceptively simple. On its surface, it is a straightforward request: run the existing load testing tool against the freshly deployed QA cluster to validate that the distributed S3 storage system works. But in the context of the conversation, this short command becomes a crucible — a test that exposes a fundamental architectural gap in the deployment, triggers a cascade of debugging, and ultimately forces a redesign of how the system handles multi-node S3 access.

The Context: A Cluster That Looks Healthy

To understand why this message matters, we must reconstruct the moment it was sent. The assistant had just completed a grueling multi-hour deployment of a Filecoin Gateway (FGW) distributed storage system across three physical nodes. The head node (10.1.232.82) ran a single-node YugabyteDB cluster providing both SQL and CQL databases. Two kuri storage nodes (10.1.232.83 and 10.1.232.84) each ran the kuri daemon, which served as both a Filecoin storage node and an S3-compatible API endpoint on port 8079.

The cluster topology API had just been fixed moments earlier — the assistant had realized that the FGW_BACKEND_NODES environment variable was missing, and after adding it, the topology endpoint returned data showing both nodes as "healthy." The Web UI was rendering. The S3 API was responding (albeit with 400 errors for unauthenticated requests). On paper, everything looked good.

But the user, with a practiced eye for validation, wanted more than a static status check. They wanted to exercise the system.

The Assumption Hidden in Plain Sight

The user's message carries an implicit assumption: that a load testing tool exists at the path integrations/loadtest and that it can be pointed at the cluster to produce meaningful results. This assumption turns out to be correct — the tool exists, though not at the exact path the user specified. It lives at integrations/ritool/loadtest.go, part of a larger ritool binary that bundles several testing utilities.

More critically, the message assumes that the cluster is ready for load testing. The assistant, having just verified that both kuri nodes are running and the topology API works, shares this assumption. Both parties believe the system is in a state where a load test would succeed and produce useful validation data.

This shared assumption is about to be shattered.

The Discovery: A Single-Node System in Disguise

When the assistant builds the ritool binary and runs the load test against http://10.1.232.83:8079 (kuri1's S3 endpoint), the test runs. Objects are written and read. But the user immediately spots the problem:

"Only kuri1 getting traffic seems wrong"

This observation is the first crack in the facade. The load test was pointed at kuri1's IP address, so naturally only kuri1 received traffic. But the deeper issue — the one that the user's original message was designed to uncover — is that each kuri node's S3 endpoint serves only its local blockstore. When the assistant tests cross-node access by writing to kuri1 and reading from kuri2, the read returns empty. The S3 metadata is shared (stored in the filecoingw_s3 CQL keyspace), so kuri2 knows the object exists and which node owns it. But the actual block data lives on kuri1's local disk, and kuri2 has no mechanism to fetch it.

The error message in kuri2's logs tells the story: "block was not found locally (offline): ipld: could not find bafkreicq725qu5x2tcre6ff6jb7cbuu4msnmmdceannrzsk6spdmom3gb4."

This is the architectural gap. The system, as deployed, is a collection of independent storage nodes with a shared metadata index but no cross-node data retrieval layer. Each kuri node is an island, serving only the data it has stored locally. The S3 API on each node is a direct endpoint to that node's blockstore — there is no routing layer that can look at an object's node_id metadata and forward the request to the correct backend.

The Correction: Deploying the S3 Proxy

The user's four-word message thus initiates a chain of events that leads to the deployment of the s3-proxy frontend on the head node. The proxy, listening on port 8078, is designed precisely for this purpose: it reads the shared S3 CQL keyspace to determine which backend node owns an object, then routes the S3 request to the correct kuri node. It is the missing routing layer that transforms a collection of independent storage nodes into a true distributed S3 cluster.

The assistant initially tries to set up the proxy manually via SSH, but the user intervenes again: "Why are you not doing this with ansible?" This correction pushes the assistant back into proper infrastructure automation, updating the QA inventory and deploying the proxy via the existing Ansible playbooks.

The Outcome: A Validated Cluster

After the proxy is deployed and configured with the correct environment variables (FGW_BACKEND_NODES, RIBS_S3_CQL_HOSTS, etc.), the load test is run again — this time against http://10.1.232.82:8078. The results show both kuri nodes receiving traffic (3 PUT/DELETE operations each during the test window). Cross-node reads work: an object written to kuri1 can be read through the proxy, which routes the request to the correct backend.

The user's original message, "try integrations/loadtest against it," succeeded in its purpose. It validated that the cluster works — but only after revealing and correcting a critical architectural flaw. The system that emerged from this test is fundamentally different from the one that entered it: it now has a proper three-layer architecture (S3 proxy → kuri storage nodes → YugabyteDB) with intelligent request routing, rather than a flat collection of independent S3 endpoints.

The Thinking Process Behind the Response

The assistant's response to the user's message reveals a methodical approach. First, it locates the loadtest tool (it's not at integrations/loadtest/ but at integrations/ritool/loadtest.go). Then it builds the ritool binary. Then it attempts to run the load test, hitting argument parsing issues that require several attempts to resolve (the endpoint URL must come after options, not before). Finally, the test runs and produces results.

But the assistant's thinking is focused on execution, not on the architectural question the test is designed to answer. It takes the user's explicit observation — "Only kuri1 getting traffic seems wrong" — to trigger the deeper investigation into cross-node access. This pattern repeats throughout the session: the assistant executes, the user observes and redirects.

Input and Output Knowledge

To understand this message, one needs to know: that a load testing tool exists in the project's integrations directory; that the QA cluster has been deployed with two kuri nodes; that the S3 API is accessible on port 8079; and that the purpose of load testing is to validate both performance and correctness (cross-node access).

The message creates new knowledge: the loadtest binary must be built from source; the ritool binary exists at the project root after building; the load test reveals that cross-node S3 reads fail; the architecture lacks a routing layer; the s3-proxy must be deployed to enable multi-node access; and the proxy must be deployed via Ansible, not manual SSH.

Mistakes and Incorrect Assumptions

The primary incorrect assumption shared by both parties is that the cluster is ready for load testing as deployed. In reality, the cluster was a collection of independent single-node S3 endpoints sharing only metadata. The load test could run, but it would only test one node at a time, and cross-node reads would fail silently.

A secondary mistake is the assistant's assumption about the loadtest tool's location. The path integrations/loadtest doesn't exist; the tool lives inside integrations/ritool. This is a minor issue but it reflects the gap between the user's mental model of the project structure and the actual file layout.

Conclusion

The user's four-word message is a masterclass in concise, effective technical direction. It doesn't prescribe a solution — it prescribes a test. The test reveals the problem, and the problem demands a solution. The message trusts that the assistant will figure out the mechanics (finding the tool, building it, running it) while the user focuses on the strategic question: does this system actually work as a distributed cluster? The answer, initially, is no. But by the end of the debugging cascade that this message triggers, the answer becomes yes — and the system is fundamentally better for having been tested.