Chunk 3.0
## Summary: Horizontally Scalable S3 Architecture for Filecoin Gateway ### Project Overview We're building a horizontally scalable S3-compatible storage system where multiple Kuri storage nodes operate independently while sharing object routing metadata via YugabyteDB. ### Architecture ``` ┌─────────────────────────────────────────────────────────────┐ │ S3 Frontend Proxy (stateless, scalable) │ │ :8078 │ └─────────────────────────┬───────────────────────────────────┘ │ ┌───────────────┼───────────────┐ ▼ ▼ ▼ ┌─────────────────┐ ┌─────────────────┐ │ Kuri Node 1 │ │ Kuri Node 2 │ (independent storage) │ RIBS keyspace: │ │ RIBS keyspace: │ │ filecoingw_kuri1│ │ filecoingw_kuri2│ │ :7001 │ │ :7002 │ └────────┬────────┘ └────────┬────────┘ │ │ └─────────┬─────────┘ ▼ ┌─────────────────────────────────────────────────────────────┐ │ Shared S3 Keyspace │ │ filecoingw_s3 │ │ (S3Objects table with node_id for routing) │ └─────────────────────────────────────────────────────────────┘ ``` ### Test Cluster Ports - `:8078` - S3 API (Frontend Proxy) - `:9010` - Web UI for kuri-1 (via nginx) - `:9011` - Web UI for kuri-2 (via nginx) - `:7001` - kuri-1 LocalWeb (CAR files) - `:7002` - kuri-2 LocalWeb (CAR files) ### What Was Completed #### 1. Core S3 Frontend Proxy (`server/s3frontend/`) - Stateless proxy that routes requests to Kuri backends - Round-robin writes, YCQL lookup for reads - Health checks, multipart upload coordination #### 2. Fixed Critical Bugs - **HTTP route conflict**: Go 1.22 ServeMux pattern conflicts between `HEAD /` and `GET /healthz` - fixed with custom handler in `server/s3/fx.go` - **JSON case mismatch**: Frontend expected camelCase, backend sent PascalCase - added JSON tags to all structs in `iface/iface_ribs.go` - **Missing S3 tables**: Added `node_id` column to S3Objects table in db-init #### 3. Cluster Monitoring Implementation **Backend files modified:** - `iface/iface_ribs.go` - Added JSON tags to all cluster monitoring structs (ClusterTopology, ProxyInfo, StorageNodeInfo, ThroughputHistory, IOThroughputHistory, LatencyDistribution, ErrorRates, ActiveRequests, ClusterEvent) - `iface/iface_rbs.go` - Added `IOThroughput()` method to RBSDiag interface - `rbstor/cluster_metrics.go` - Real-time metrics collection (request counts, latency, I/O bytes, events) - `rbstor/diag.go` - ClusterTopology returns live stats (storageUsed, groupsCount, requestsPerSecond) - `integrations/web/rpc.go` - Added IOThroughput RPC method - `server/s3/server.go` - Added `responseRecorder` to track bytes, metrics recording in handlers **Frontend files modified:** - `integrations/web/ribswebapp/src/components/ClusterTopology.js` - Visual distinction between S3 Frontend (blue) and Kuri Storage (green) nodes - `integrations/web/ribswebapp/src/components/IOThroughputChart.js` - NEW: I/O bytes/sec chart - `integrations/web/ribswebapp/src/components/LatencyDistributionChart.js` - Changed SLA→SLO at 350ms - `integrations/web/ribswebapp/src/routes/Cluster.js` - Added IOThroughput chart, improved layout - `integrations/web/ribswebapp/src/routes/Cluster.css` - New 2-column layout with charts grid #### 4. Test Cluster (`test-cluster/`) - `docker-compose.yml` - YugabyteDB + 2 Kuri nodes + S3 proxy + nginx webui - `gen-config.sh` - Generates per-node configs and nginx.conf - Nginx proxies both kuri UIs on :9010 and :9011 ### Current State / What's Happening Now The React frontend was rebuilt with `npm run build` but the Docker image hasn't been rebuilt yet. Need to: 1. **Rebuild Docker image**: `docker build -t fgw:local .` 2. **Restart cluster**: `cd test-cluster && FGW_DATA_DIR=/data/fgw2 docker compose up -d --force-recreate` ### What Needs To Be Done Next 1. Deploy the updated frontend (rebuild docker, restart containers) 2. Verify the new layout appears (topology + active requests side-by-side, 2x2 chart grid) 3. Verify I/O Throughput chart appears and shows data 4. Verify SLO label at 350ms on latency chart 5. Test that storage nodes table shows live stats (storage used, groups, req/sec) ### Key Commands ```bash # Rebuild frontend cd /home/theuser/gw/integrations/web/ribswebapp && npm run build # Rebuild docker image cd /home/theuser/gw && docker build -t fgw:local . # Restart cluster cd /home/theuser/gw/test-cluster && FGW_DATA_DIR=/data/fgw2 docker compose up -d --force-recreate # Test RPC echo '{"jsonrpc":"2.0","method":"RIBS.IOThroughput","params":["5m"],"id":1}' | websocat ws://localhost:9010/rpc/v0 # Generate test traffic for i in {1..10}; do dd if=/dev/urandom bs=10K count=1 2>/dev/null | curl -s -X PUT --data-binary @- http://localhost:8078/test/file$i.bin; done ``` ### Known Issues - `rbstor/rbs.go:41` has LSP error `ribsCfg.NodeID undefined` - this is a pre-existing issue not related to our changes - nginx webui container sometimes needs restart after kuri nodes restart to pick up correct routing
The Optimization Pipeline: From 700 MB/s to 85 GB/s in a Distributed S3 Load Test
Message Articles
- The Architecture Document That Built Itself: A Moment of Consolidation in Distributed Systems Development
- The Art of the Green Light: Analyzing a Single-Phrase Delegation in a Distributed Systems Coding Session
- The Docker Build That Deployed a Cluster Dashboard
- The Deployment Moment: Bridging Development and Verification in a Distributed S3 Architecture
- The Moment of Verification: A Case Study in Distributed Systems Debugging
- The Moment of Discovery: How a Missing Container Revealed Configuration Fault Lines in a Distributed S3 Cluster
- The Missing Table That Wasn't: A Case Study in Debugging Assumptions
- The Quiet Restart: A Single Command in the Debugging Trenches
- The Silent Failure: Reading Between the Lines of a Docker Compose Status Check
- The Moment of Debugging: When a Missing Table Hypothesis Meets a Missing Directory
- The Missing Directory: A Debugging Pivot in Distributed Systems Engineering
- The Diagnostic Pivot: How a Simple Directory Listing Unraveled a Cluster Deployment Failure
- Reading the Blueprint: How a Simple File Read Unraveled a Cluster Deployment Failure
- The Falsified Hypothesis: A Debugging Pivot in Distributed Systems
- The Quietest Debugging Move: When a Single Grep Defines a Debugging Session
- The Diagnostic Moment: Unraveling a Container Startup Failure in a Distributed S3 Cluster
- The Diagnostic Pivot: How a Single Hypothesis Check Unraveled a Cluster Startup Failure
- Debugging Distributed Database Initialization in a Multi-Node S3 Cluster
- The Diagnostic Query: Tracing a Container's Refusal to Start Through Database Tables
- The Debugging Gaze: A Single Database Query in a Distributed Systems Investigation
- The Debugging Detective: Tracing a Missing Database Table in a Distributed S3 Architecture
- The Art of the Targeted Read: Debugging a Database Schema Mismatch in a Distributed S3 Cluster
- Tracing the Temp Table: A Debugging Deep Dive into Database Schema Mismatches
- The Temp Table That Wasn't: A Debugging Pivot in Distributed Systems
- The Migration Check: A Pivot Point in Debugging a Distributed Storage Cluster
- The Pragmatic Restart: Debugging a Distributed Storage Cluster's Startup Failure
- The Moment of Resolution: A Docker Log Check That Confirms a Cluster Is Alive
- The Quiet Triumph: When a Container Finally Starts
- The Verification That Closes the Loop: Analyzing a Single Health Check in a Distributed Systems Debugging Session
- The Zero-Data Verification: A Pivotal Monitoring Checkpoint in Distributed Systems Testing
- The Moment the Cluster Breathes: From Debugging to Data in a Distributed S3 Test
- The Quiet Verification: How Ten Successful Downloads Confirmed a Three-Layer Architecture
- The Moment of Proof: Verifying Live Metrics in a Distributed S3 Architecture
- The Moment of Verification: Probing a Distributed Cluster's Topology After Debugging
- The Moment of Verification: When an RPC Method Goes Missing
- Verifying Latency Distribution in a Distributed S3 Architecture: A Moment of Validation
- The Final Check: Verifying the ActiveRequests Endpoint in a Distributed S3 Monitoring System
- The Art of the Misnamed Method: Debugging RPC Endpoint Names in a Distributed S3 Cluster
- The Moment of Correction: Tracing an RPC Method Name Through Code and Cluster
- The Moment of Alignment: Verifying Frontend-Backend Contract in a Distributed S3 Monitoring System
- The Moment of Verification: A Deployment Summary in Distributed Systems Engineering
- The Silent Asymmetry: When Horizontal Scalability Fails at the First Test
- The Moment of Misdirection: Debugging a Phantom Routing Bug in a Distributed S3 Proxy
- Reading the Source: How a Single `read` Command Uncovered the Root Cause of Uneven Traffic Distribution in a Distributed S3 Proxy
- The Diagnostic Pivot: From Code Analysis to Runtime Investigation in a Distributed S3 Debugging Session
- The Art of Debugging Distributed Systems: A Single Health Check That Changed Everything
- The Configuration That Hid the Bug: A Debugging Crossroads in Distributed S3 Architecture
- The Debugging Pivot: When the Config Looks Correct But the System Behaves Wrong
- The Silence of the Logs: How a Missing Print Statement Exposed a Deeper Observability Problem in a Distributed S3 Proxy
- The Debugging Pivot: When Log Levels Reveal the Gap Between Theory and Practice
- The Art of Debugging Distributed Systems: Tracing a Missing Backend in an S3 Proxy Cluster
- Debugging Round-Robin Routing in a Distributed S3 Proxy: A Code-Level Investigation
- The Instrumentation Instinct: How Adding Logging Revealed a Deeper Architectural Bug in a Distributed S3 Proxy
- The Instrumentation Rebuild: Adding Visibility to a Silent Round-Robin
- The Moment of Deployment: Debugging Round-Robin Through Container Recreation
- Instrumenting for Insight: Debugging Round-Robin Routing in a Distributed S3 Proxy
- The Silence of the Logs: A Debugging Dead End in Distributed Systems
- The Silence of the Logs: A Debugging Meta-Moment in Distributed Systems Development
- The Logger That Wasn't: A Debugging Micro-Turn in Distributed Systems Instrumentation
- The Missing Logger: A Micro-Diagnostic in Distributed Systems Debugging
- The Silent Logger: Debugging Visibility in a Distributed S3 Proxy
- The Logger That Wasn't Configured: A Case Study in Distributed Debugging
- The Invisible Log: Debugging a Round-Robin Routing Bug Through Logger Configuration
- The Second Restart: Debugging Round-Robin Routing in a Distributed S3 Proxy
- The Hidden Weight of a Single Bash Command: Debugging Round-Robin Routing in a Distributed S3 Proxy
- The Silent Logger: A Case Study in Distributed Systems Debugging
- The Moment of Diagnostic Clarity: When the Data Path Works but the Observability Path Doesn't
- The Moment of Insight: Diagnosing a Metrics Aggregation Bug in a Distributed S3 Architecture
- The Moment of Diagnosis: Discovering the Local-Only Stats Problem in a Distributed S3 Cluster
- The Invisible Node: Diagnosing a Distributed Metrics Blind Spot
- The Diagnostic Read: Uncovering the Architecture of Cross-Node Metrics in a Distributed S3 System
- The Pivot Point: Cross-Node Stats Aggregation in a Distributed S3 Architecture
- The Diagnostic That Revealed an Interface Gap
- The Pivot Point: How a Simple `grep` Uncovered an Architectural Assumption in Distributed Systems Monitoring
- Reading the Interface: A Pivotal Moment of Debugging in Distributed Systems
- The Moment the Interface Didn't Have NodeID
- The Invisible Edit: A Microcosm of Debugging Distributed Systems
- The Moment of Discovery: Tracing a Distributed Systems Bug Through Code Reading
- The Unused Import: A Microcosm of Distributed Systems Debugging
- The Moment the Cluster Became Observable: Debugging Cross-Node Metrics Aggregation
- The Moment of Realization: Tracing a Distributed Systems Debugging Insight
- The Moment a Debugging Hypothesis Collides with Reality: Analyzing a Cluster Stats Probe
- The Hidden Danger of Catch-All Routes: Debugging a Stats Endpoint in a Distributed S3 Proxy
- The Subtle Art of Go ServeMux Routing: A Case Study in Debugging Distributed Systems
- The Critical Restart: Applying Distributed Monitoring to a Horizontally Scalable S3 Cluster
- The Moment of Proof: Validating a Cluster Monitoring Endpoint in a Distributed S3 Architecture
- The Verification That Changes Everything: Cross-Node Stats Communication in a Distributed S3 Architecture
- The Moment of Validation: Cross-Node Communication in a Distributed S3 Architecture
- The Moment of Verification: Querying a Distributed Cluster Topology
- The Proxy That Wasn't: A Debugging Epiphany in Distributed Systems Topology
- The Moment of Discovery: Debugging a Phantom Port Swap in a Distributed S3 Cluster
- The Phantom Proxy Swap: Debugging a Cluster Topology Mismatch in a Distributed S3 Architecture
- The Debugging Microscope: A Single Log Check in a Distributed S3 Architecture
- The Art of Systematic Debugging: Tracing a Swapped Identity in a Distributed S3 Cluster
- The Smoking Gun: How Two Curl Commands Exposed a Silent Nginx Routing Swap
- The Case of the Swapped Ports: A Debugging Epiphany in Distributed Systems Monitoring
- The One-Line Fix That Restored Order: Debugging a Port Swap in a Distributed S3 Cluster
- The Moment of Verification: Debugging DNS Caching in a Distributed S3 Cluster
- Closing the Visibility Gap: How Cross-Node Stats Aggregation Fixed a Distributed Monitoring Blind Spot
- The Art of the Checkpoint: Why "Make Commits for Changes Made So Far" Matters More Than It Seems
- The Commit That Almost Wasn't: A Study in Developer Discipline
- The Art of the Logical Commit: How One Developer Organized a Debugging Marathon into Coherent History
- The Commit That Fixed a Thousand Cuts: JSON Case Mismatch in a Distributed S3 Monitoring Dashboard
- The Art of the Checkpoint: How a Single Todo Update Reveals the Hidden Structure of AI-Assisted Software Engineering
- The Anatomy of a Commit: How One Git Message Captures an Hour of Distributed Systems Engineering
- The Meta-Cognitive Commit Log: Tracking Progress Through Structured Task Management
- The Commit That Made Cluster Monitoring Real: How a Single `/api/stats` Endpoint Transformed Distributed Visibility
- The Silent Orchestrator: How a Simple Todo Update Reveals the Discipline of Incremental Commit Strategy
- The Commit That Tied It All Together: Round-Robin Logging, Route Conflicts, and Metrics Recording in a Distributed S3 Architecture
- The Invisible Architecture: How a Todo List Update Reveals the Discipline of Incremental Commit Crafting
- The Final Commit: Enabling Observability in a Distributed S3 Test Cluster
- The Silent Scaffold: How a Todo-List Update Reveals the Architecture of Incremental Software Engineering
- The Commit That Made the Architecture Visible: Visual Distinction and Terminology Precision in a Distributed S3 System
- The Commit Checklist: How a Todo List Update Marks the Boundary Between Building and Verifying
- The Moment of Verification: Why `git status` Matters After 22 Commits
- The Verification Commit: Reading the Git Log as a Development Ritual
- The Art of the Commit Summary: How Six Logical Commits Captured a Distributed Systems Debugging Marathon
- The Loadtest Specification: Defining Quality Gates for a Distributed S3 Architecture
- The Deliberate Architect: How One Message Embodies the Discipline of Codebase Exploration
- Reading the Blueprint: How Pattern Recognition Drives Code Generation in Distributed Systems Engineering
- The Art of Reconnaissance: Understanding a Codebase One Glob at a Time
- The Quiet Reconnaissance: How a Simple `ls -la` Unlocked a Load Testing Utility
- The First Draft: Building a Load Test Utility for S3
- The Debugging Microcosm: What a Single Failed Edit Reveals About Iterative Development
- The Diagnostic Read: How Examining Existing Code Patterns Rescued a Load Test Utility
- The Moment of Insight: Debugging API Version Mismatches in a Distributed S3 Load Test Utility
- The Art of Iterative Debugging: When an Edit Doesn't Quite Land
- The Iterative Debugging Dance: When API Version Mismatches Bite in Go
- The Art of Iterative Debugging: A Single Edit in the Loadtest Utility
- The Invisible Weight of a Single Line: Debugging API Mismatches in a Loadtest Utility
- The Moment of Integration: Registering a Loadtest Command in ritool's Main Entry Point
- The Build Verification Step: Why a Single Command Matters in Software Engineering
- The Moment of Verification: A Load Test Utility Meets Its First Smoke Test
- When Arguments Collide: A Case Study in CLI Argument Ordering During Load Test Development
- Debugging CLI Argument Parsing: A Load Test Utility Comes to Life
- The Diagnostic Pivot: Analyzing a Load Test Probe in a Distributed S3 System
- Debugging Multipart Upload Failures: A Diagnostic Pivot in Load Testing
- The Moment of Truth: When a Load Test Reveals More Than Expected
- The Hidden `.gitignore`: A Moment of Discovery in the S3 Loadtest Development
- The Art of the Force-Add: When Gitignore Blocks a Perfectly Good Commit
- The Commit That Almost Wasn't: A Git Log as a Closing Ritual
- The Loadtest Utility: Closing the Loop on S3 Performance Validation
- The Optimization Imperative: Deconstructing a Performance-Focused Directive in Distributed Systems Development
- The Checkpoint Before Optimization: A Git Status as a Strategic Pivot
- The Art of Planning Under Pressure: A Todo List as a Decision Artifact
- The Diagnostic Grep: How a Single Search Command Launched a Performance Optimization Pipeline
- The Art of Narrowing the Search: A Precision Grep in Performance Optimization
- Precision in Optimization: Tracing the Buffer-Pool Refactoring in a Distributed S3 Architecture
- The Art of Scope: A Performance Optimization Decision in Distributed S3 Architecture
- The Art of Systematic Optimization: Tracing I/O Paths in a Distributed S3 Architecture
- The Diagnostic Read: Tracing Performance Bottlenecks Through Source Code
- The Art of the Targeted Grep: Tracing Data Flow in a Distributed S3 Architecture
- The Art of Reconnaissance: A Single Glob Command That Unlocks Performance Optimization
- The Quiet Diagnostic: How a Single Grep Command Shaped an Optimization Pipeline
- The Anatomy of a Systematic Optimization: Reading the S3 Handlers
- The Art of Reading Code: A Pivotal Investigation in S3 Proxy Optimization
- The Art of Tracing Data Flow: A Single Search Command in a Distributed S3 Optimization Effort
- The Art of the Diagnostic Grep: Tracing an S3 Optimization Through a Single Command
- The Data-Gathering Grep: A Pivotal Step in Systematic Code Optimization
- The Pause Before Optimization: A Case Study in Engineering Reconnaissance
- The Pivot Point: From Investigation to Optimization in a Distributed S3 Proxy
- The Half-Applied Optimization: A Case Study in Incremental Refactoring
- The Moment Before the Edit: Reading Code to Optimize an S3 Proxy's Data Path
- The Silent Performance Fix: When a One-Line Edit Reveals Deeper Architectural Thinking
- The Quiet Gatekeeper: Why a Single Build Command Reveals the Soul of Engineering Discipline
- The Pivot Point: Tracking Progress in a Multi-Stage Optimization Pipeline
- The Art of the Transition: Reading Before Rewriting in Performance Optimization
- The Pivot Point: How a Single Read-and-Plan Message Unlocked 85 GB/s Data Generation for S3 Load Testing
- The Thin Status Update: When a Single Line Carries a Heavy Edit
- The Art of the Targeted Edit: Adding a Shard-Based Data Generator to Eliminate Random Number Bottlenecks
- The Moment of Integration: Reading Code to Connect a Performance Optimization
- The Diagnostic Interlude: When an Edit Introduces Undefined Variables in an Optimization Pipeline
- The Reading Pause: A Microcosm of Iterative Software Development
- The Silent Diagnostic: Understanding Iterative Refinement Through a Single LSP Error
- The Art of the Surgical Fix: Reading Before Editing in a High-Performance Loadtest Refactor
- The Moment of Convergence: How a Single Edit Fixed a Load Test Refactoring
- The Compile Check: A Pivotal Moment in Performance Optimization
- The Moment of Creation: Analyzing a Test File's Birth and Its Immediate Flaw
- The Debugging Dance: Fixing a Go Import Error in a Benchmark Test
- The Invisible Edit: How a Single Line Fix Reveals the Iterative Soul of Performance Engineering
- The Verification Pivot: Why Running Tests Matters as Much as Writing Them
- When the Output Ends Abruptly: Reading Between the Lines of a Benchmark Run
- The Moment of Validation: Interpreting Benchmark Results in a Distributed Systems Optimization
- The Moment Before Feedback: A `git status` That Tells a Story
- The Art of the Thoughtful Commit: Buffer-Pool Optimization in an S3 Frontend Proxy
- The Commit That Killed the Bottleneck: Optimizing Load Test Data Generation with Shard-Based Randomness
- The Verification Commit: Why a Simple `git log` Tells a Deeper Story
- The Closing Signal: When a Todo List Marks the End of an Optimization Sprint
- The Optimization Summary: When a Coding Session Culminates in Two Targeted Commits
- When 700 MB/s Is "Extremely Slow": A Lesson in Performance Expectations
- Profiling the Bottleneck: A Performance Investigation in Go Benchmarking
- The Moment the Bottleneck Broke: Profiling a Performance Mystery at 700 MB/s
- The MD5 That Wasn't Supposed to Be There: A Case Study in Performance Profiling
- Precision Optimization: Diagnosing and Eliminating Bottlenecks in a Go Load Test Data Generator
- When the Benchmark Lies: Diagnosing a Hidden MD5 Bottleneck in Load Test Data Generation
- The Diagnostic Read: Uncovering Hidden Bottlenecks in Benchmark Code
- The Small Fix That Completed a Performance Optimization: Debugging a Duplicate Loop in Go Benchmark Tests
- The Moment the Bottleneck Broke: Profiling a Data Generator from 700 MB/s to 85 GB/s