Reading the Source: How a Single read Command Uncovered the Root Cause of Uneven Traffic Distribution in a Distributed S3 Proxy

Introduction

In the course of debugging a horizontally scalable S3-compatible storage system, a seemingly simple user observation triggered a deep investigation into the routing logic of a stateless frontend proxy. The user reported: "There is traffic visible on Frontend Proxies table, but all of it goes only to kuri-1, none to kuri-2." This single sentence set off a chain of diagnostic steps, the first of which—and the subject of this article—was the assistant's decision to read the source file backend_pool.go. At first glance, this appears to be a trivial operation: a developer reading a file. But in the context of a complex debugging session, this read command represents a deliberate investigative pivot, a moment of hypothesis formation, and the beginning of a deep-dive into the inner workings of the round-robin load balancing mechanism.

The Context: What Was Happening

The system under development is a three-layer horizontally scalable S3 architecture. At the top sits a stateless S3 frontend proxy (listening on port 8078) that receives all client requests. Below it sit multiple Kuri storage nodes—independent storage servers that each maintain their own RIBS keyspace in a shared YugabyteDB cluster. The proxy is supposed to distribute incoming write requests across the available Kuri backends using a round-robin algorithm, while read requests are routed to the correct backend based on metadata stored in a shared S3 objects table.

Just prior to this message, the assistant had successfully deployed the full test cluster, verified that all services were running, generated test traffic, and confirmed that the monitoring RPC endpoints (ClusterTopology, RequestThroughput, IOThroughput, LatencyDistribution, ActiveRequests) were all returning data. The cluster appeared healthy. Both kuri-1 and kuri-2 were running. Both were listed in the backend configuration. Yet the monitoring UI showed that all traffic was hitting only kuri-1.

The user's observation was precise and actionable. It told the assistant something the automated health checks had missed: the round-robin distribution was not actually distributing. This is a classic systems debugging moment—the system reports "all green" but the user sees asymmetric behavior.

The Message Itself: A Deliberate Read

The target message (index 851) is:

[assistant] [read] /home/theuser/gw/server/s3frontend/backend_pool.go
<file>
00001| package s3frontend
00002| 
00003| import (
00004|     "context"
00005|     "fmt"
00006|     "net/http"
00007|     "strings"
00008|     "sync"
00009|     "sync/atomic"
00010|     "time"
00011| 
00012|     logging "github.com/ipfs/go-log"
00013| )
00014| 
00015| var log = logging.Logger("gw/s3frontend/backend")
00016| 
00017| // Backend represents a Kuri storage node
00018| type Backend struct {
00019|     id      string
00020|     baseURL string
00021|     client  *http.Client
00022|     healthy atomic.Bool
00023| }
0...

The file is truncated—only the first 23 lines are shown in the conversation data—but the critical structural elements are already visible. The Backend struct, the log variable, the imports. This is the assistant looking at the foundational data structure of the backend pool.

What makes this message significant is not the content of the file itself, but the act of reading it. The assistant had just run a grep for "round.?robin|backend|routing" and found matches across multiple files. Rather than jumping to conclusions or making assumptions about how the round-robin worked, the assistant went straight to the source file that defines the backend abstraction. This is a disciplined debugging approach: understand the data structures before trying to trace the control flow.

The Reasoning and Motivation

Why did the assistant read this particular file? The chain of reasoning can be reconstructed from the surrounding messages:

  1. The user's observation (msg 849) established the symptom: uneven traffic distribution.
  2. The assistant's initial hypothesis (msg 850) was that this was "expected based on how the cluster is currently configured"—a tentative framing that acknowledged the possibility of a configuration issue rather than a code bug.
  3. The grep results showed that backendPool and SelectRoundRobin were central to the routing logic, with references in server.go and backend_pool.go.
  4. The read of backend_pool.go was the next logical step: understand how backends are registered, how health is tracked, and how the pool manages its list of available nodes. The assistant was operating under a specific set of assumptions at this point. It assumed that the round-robin logic was correctly implemented in principle, but that something was preventing it from working in practice. Possible culprits included: a configuration issue where only one backend was being registered, a health-check failure that was marking kuri-2 as unhealthy, a race condition in the atomic health flag, or a bug in the SelectRoundRobin method itself.

What the Assistant Learned from This Read

Even from the truncated view, several important facts were immediately visible:

  1. The Backend struct uses an atomic.Bool for the healthy field. This tells the assistant that health status is tracked atomically, likely updated by a background health-check goroutine. If kuri-2 was being marked unhealthy by the health checker, the round-robin would skip it.
  2. The package-level logger is gw/s3frontend/backend. This is significant because the assistant later discovered that the log level was set to info and the logger wasn't configured in the environment variables, which explained why debug-level round-robin selections weren't appearing in the logs.
  3. The imports reveal dependencies on sync, sync/atomic, context, fmt, net/http, strings, and time—all typical for a connection pool implementation. The presence of sync and sync/atomic suggests careful concurrency handling.
  4. The baseURL field in the Backend struct stores the URL of the Kuri node. The assistant would later verify that both kuri-1 and kuri-2 had correct URLs in the environment variable FGW_BACKEND_NODES.

The Deeper Investigation That Followed

This read was just the first step. After examining backend_pool.go, the assistant went on to:

Assumptions and Misconceptions

Several assumptions were in play during this message:

  1. The assistant assumed that the round-robin was the likely failure point. This was a reasonable first hypothesis, but it turned out to be incorrect. The round-robin was working fine.
  2. The assistant assumed that the backend pool's health-check mechanism was relevant. This led to checking health endpoints on both nodes, which confirmed both were healthy.
  3. The assistant initially assumed that the log level was sufficient to see routing decisions. Only later did it discover that the gw/s3frontend/backend logger wasn't configured in the RIBS_LOGLEVEL environment variable, which only set the ribs logger to info.
  4. A subtle misconception was that the ClusterTopology endpoint, which was served by kuri-1's web UI, would somehow aggregate data from all nodes. The code review in message 875 revealed that it only filled in stats for the local node, leaving remote nodes with zero values.

Input Knowledge Required

To fully understand this message, a reader needs:

Output Knowledge Created

This message created knowledge about:

  1. The backend pool's data model: How Kuri nodes are represented as Backend structs with atomic health tracking.
  2. The logging infrastructure: The package-level logger name gw/s3frontend/backend would later be crucial for configuring verbose logging.
  3. The health-check mechanism: The healthy atomic.Bool field indicated that health is tracked per-backend and likely updated by a goroutine.
  4. The concurrency model: The use of sync.Mutex (imported but not shown in the truncated view) and atomic operations suggested careful thread safety.

Conclusion

The read command in message 851 is a textbook example of disciplined debugging. When faced with a user report of asymmetric traffic distribution, the assistant didn't guess or jump to conclusions. It went to the source—literally. By reading the backend_pool.go file, the assistant established a mental model of how the backend pool works, which informed every subsequent diagnostic step. This single read operation was the foundation for the entire investigation that followed, ultimately leading to the discovery that the problem wasn't in the routing logic at all, but in the metrics aggregation layer. It's a reminder that in complex distributed systems, the most effective debugging tool is often the simplest: reading the code.