Verifying Load Distribution in a Distributed S3 Storage Cluster

The Message

All services are running. The load distribution check shows only 5 objects total (3 on kuri_01, 2 on kuri_02) - this is from a previous small test, not the loadtest data.

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Let me run a fresh loadtest to verify distribution with more data:

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```bash cd /home/theuser/gw && ./ritool loadtest run --concurrency 5 --duration 15s http://10.1.232.82:8078 2>&1 ═══════════════════════════════════════════════════════════════ S3 LOAD TEST ═══════════════════════════════════════════════════════════════

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Endpoint: http://10.1.232.82:8078 Bucket: loadtest Concurrency:... ```

This message, sent by the assistant during a complex distributed systems deployment session, appears deceptively simple at first glance. It is a single command—a load test invocation—preceded by a brief status update. Yet this message sits at a critical juncture in the deployment of a horizontally scalable S3-compatible storage system built on top of Filecoin. It represents a moment of validation: the assistant has just finished deploying a three-tier architecture (S3 proxy → Kuri storage nodes → YugabyteDB), has confirmed that all services are running, and is now attempting to prove that the system actually distributes data correctly across multiple storage backends. The brevity of the message belies the weight of what it is trying to accomplish.

Context: The State of the Cluster

To understand why this message was written, one must appreciate the deployment journey that preceded it. The assistant and user had been iterating on a distributed storage system called FGW (Filecoin Gateway) that provides an S3-compatible API backed by Filecoin's decentralized storage network. The architecture follows a well-established pattern for horizontally scalable systems: stateless frontend proxies that accept S3 API calls and route them to backend storage nodes, which in turn store data in a shared database (YugabyteDB) while also managing Filecoin deal-making and retrieval.

The QA test environment consisted of three physical nodes:

Why This Message Was Written: The Reasoning and Motivation

The assistant's motivation for writing this message stems from a fundamental requirement of distributed systems: verification. It is not enough to deploy services and see them show "active (running)" in systemd status. A distributed system must be observed under load to confirm that its routing, data distribution, and coordination mechanisms actually work as designed.

The trigger was the previous load distribution check. The assistant had queried the shared CQL keyspace (filecoingw_s3) and found only 5 objects total—3 on kuri_01 and 2 on kuri_02. This was suspiciously small. The assistant correctly identified that these 5 objects came from earlier ad-hoc testing (manual curl PUT commands and a very small previous test), not from the more substantial load test that had been run earlier. That earlier load test (with 10 concurrent workers over 30 seconds, generating ~345 writes/sec and ~147 reads/sec) should have produced hundreds or thousands of objects. The fact that only 5 objects appeared in the database suggested either:

  1. The load test objects had been cleaned up or expired
  2. The load test was writing to a different bucket or keyspace
  3. The CQL query was only sampling a subset of data
  4. The load test results were not being persisted in the expected way Rather than speculating, the assistant made a pragmatic decision: run a fresh, targeted load test with known parameters, then immediately check the resulting distribution. This is a classic debugging technique—control the variables, observe the outcome, draw conclusions.

The Decision-Making Process Visible in the Message

The message reveals several implicit decisions:

Choice of test parameters: The assistant selected --concurrency 5 --duration 15s. Compared to the earlier load test (10 concurrency, 30 seconds), this is a lighter, shorter test. This is a deliberate choice. The goal is not to stress-test the system or measure throughput—that was already done. The goal is to generate enough objects to make distribution visible. Five concurrent workers over 15 seconds, even at modest throughput, should produce dozens to hundreds of objects—more than enough to see whether both nodes are receiving traffic.

Choice of endpoint: The test targets http://10.1.232.82:8078, which is the S3 proxy on the head node, not the individual Kuri nodes directly. This is critical. The assistant is testing the routing layer, not the storage nodes themselves. If the proxy is misconfigured and sends all traffic to a single backend, the test will reveal that. If the proxy correctly distributes traffic, both nodes should show activity.

Choice of tool: The assistant uses ritool loadtest run, a custom tool built as part of the FGW project. This tool presumably handles S3 PUT and GET operations, verifies data integrity (the earlier test reported "0 data corruption" with R-A-W verification), and provides structured output. Using the project's own testing tool ensures that the test exercises the same API paths that real clients would use.

The decision to check first, then act: The message begins with "All services are running." This is not just a status report—it is a precondition check. Before running any test, the assistant confirms that the infrastructure is healthy. This prevents wasted effort debugging test failures that are actually deployment failures.

Assumptions Embedded in This Message

Every message in a debugging session carries assumptions, and this one is no exception:

Assumption 1: The load test will generate enough objects to measure distribution. The assistant assumes that 5 concurrent workers over 15 seconds will produce a statistically meaningful number of objects. This is reasonable given the earlier test's throughput of ~345 writes/sec, but it depends on the current system load, network conditions, and whether any configuration changes have affected performance.

Assumption 2: The S3 proxy's routing logic is deterministic and observable. The assistant assumes that if the proxy distributes requests, the resulting objects will be visibly split across both nodes' CQL entries. This assumes that the routing hash (likely based on object key or bucket+key) produces a roughly even split under random key generation.

Assumption 3: The previous 5 objects were indeed from earlier manual testing. This is almost certainly correct—the object counts (3 and 2) match the pattern of a few manual curl PUT commands—but it is an assumption nonetheless. It is possible that the load test objects were stored in a different keyspace or were cleaned up by a garbage collection process.

Assumption 4: The CQL database is the authoritative source for object location. The assistant plans to query the shared filecoingw_s3 keyspace to count objects per node. This assumes that the CQL schema correctly records which node owns each object, and that the query will return accurate counts.

Input Knowledge Required to Understand This Message

A reader needs significant context to grasp the full meaning of this message:

  1. Architecture knowledge: Understanding that the S3 proxy is a stateless routing layer that distributes requests across multiple Kuri storage nodes, each with its own database but sharing a CQL keyspace for routing metadata.
  2. Deployment topology: Knowing the three-node setup, which services run where, and which ports serve which purposes.
  3. Previous debugging history: Awareness that earlier attempts to query object distribution using CQL GROUP BY failed due to YugabyteDB limitations, requiring a different approach (counting objects in application code).
  4. Tool familiarity: Understanding what ritool loadtest run does—that it generates random objects, writes them via S3 PUT, optionally reads them back, and verifies integrity.
  5. The concept of "distribution" in distributed storage: Recognizing that evenly distributing data across nodes is essential for horizontal scalability, and that a system that routes all traffic to one node is not actually distributed.

Output Knowledge Created by This Message

This message creates several forms of knowledge:

Immediate output: The load test is initiated. The truncated output shows the test header (endpoint URL, bucket name, concurrency level). The actual results (throughput, object counts, distribution) are not shown in this message but would appear in subsequent output.

Methodological knowledge: The assistant demonstrates a pattern for verifying distributed system behavior: (1) check service health, (2) assess existing data for sufficiency, (3) generate fresh data with controlled parameters, (4) query the data store to measure distribution. This pattern is reusable for any similar verification task.

Negative knowledge: The assistant implicitly rules out several explanations for the low object count. The 5 objects are not from the load test; they are from earlier manual testing. This means the load test either wrote to a different location, its objects were cleaned up, or the query was incomplete.

Architectural validation: By running the test against the proxy endpoint rather than individual Kuri nodes, the assistant is validating the entire routing chain. A successful test would confirm that the proxy configuration (FGW_BACKEND_NODES) is correct and that both backends are reachable and accepting writes.

Potential Issues and Incorrect Assumptions

While the message is logically sound, there are potential pitfalls:

The load test may not produce visible distribution if the bucket doesn't exist. The test specifies Bucket: loadtest. If this bucket hasn't been created, the S3 proxy may reject the writes or create it implicitly (depending on configuration). The assistant assumes the bucket exists or will be auto-created.

The 15-second duration may be too short. If the system experiences cold-start latency (e.g., connection pooling, query plan caching), the first few seconds may have lower throughput, reducing the number of objects generated.

The concurrency of 5 may not stress the routing logic enough. If the proxy uses a connection-based or session-based routing strategy (rather than per-request routing), a low number of concurrent connections might all land on the same backend. The earlier test with concurrency 10 may have triggered distribution that concurrency 5 does not.

The truncated output is itself a limitation. We see the test header but not the results. The article must acknowledge that the message captures the decision to test rather than the test results.

The Thinking Process: A Window into Systematic Debugging

The assistant's thinking, visible in the message's structure, reveals a methodical approach to systems verification:

  1. Check health ("All services are running"): Establish that the infrastructure is operational before proceeding.
  2. Assess existing data ("only 5 objects total"): Determine whether existing data is sufficient to answer the question.
  3. Interpret the data ("this is from a previous small test, not the loadtest data"): Correctly attribute the data to its source, avoiding false conclusions.
  4. Design a targeted experiment ("run a fresh loadtest to verify distribution with more data"): Generate new data under controlled conditions.
  5. Execute the experiment: Run the load test with parameters chosen to produce meaningful results. This is textbook debugging methodology: observe, hypothesize, test, conclude. The assistant resists the temptation to draw conclusions from insufficient data (5 objects) and instead creates a situation where sufficient data will exist.

Conclusion

This message, for all its brevity, captures a pivotal moment in the deployment of a distributed storage system. It is the moment when infrastructure becomes observable, when configuration becomes behavior, when the abstract promise of horizontal scalability meets the concrete reality of data on disk. The assistant's decision to run a fresh load test rather than speculate about the meaning of 5 objects reflects a disciplined approach to systems engineering: measure, don't guess. Whether the test ultimately confirms even distribution or reveals a routing imbalance, the act of running it is itself a validation of the debugging process. The message stands as a small but instructive example of how to verify distributed systems: check that everything is running, recognize when you don't have enough data, and then go generate the data you need.