From Planning to Execution: The Architecture of a Multi-Milestone Distributed Systems Implementation

Introduction

In the life of any complex software project, there are phases that feel like a single, continuous push — days or weeks of focused execution toward a known goal. And then there are the transitions: the moments when one phase ends, the next is uncertain, and the team must decide not just what to build but how to decide what to build. This article examines one such extended transition in the development of the Filecoin Gateway (FGW), a horizontally scalable S3-compatible distributed storage system built on IPFS, Filecoin, and YugabyteDB. The chunk of the coding session analyzed here spans the arc from the completion of a validated Ansible deployment infrastructure through the research, planning, and initial execution of three ambitious future milestones — and it reveals something profound about how disciplined engineering teams navigate the gap between foundation and feature.

The story of this chunk is not a story about a single bug fix or a single feature. It is a story about architectural decision-making under uncertainty, about the deliberate transition from execution to research and back to execution, and about the invisible infrastructure — the planning documents, the design constraints, the configuration schemas, the todo lists — that makes complex distributed systems possible. Over the course of dozens of messages, the assistant and user together built a complete deployment pipeline, researched state-of-the-art approaches for enterprise monitoring, predictive caching, and garbage collection, synthesized those findings into a 1003-line execution plan, made four critical design decisions, and then executed the first phase of that plan — creating metrics files, building a trace correlation system, and implementing an Adaptive Replacement Cache — all while testing incrementally at every step.

Part I: The Foundation — A Deployment Infrastructure Validated

Before the transition could begin, there had to be something to transition from. The chunk opens with the successful completion and validation of a comprehensive Ansible-based deployment infrastructure for FGW clusters, culminating in commit 806c370. This was not a trivial achievement. The preceding work involved creating Ansible roles for common setup, wallet distribution, YugabyteDB initialization, Kuri storage node deployment, and S3 frontend proxy deployment; building a Docker-based multi-container test harness; and iteratively fixing a cascade of issues — systemd EnvironmentFile format problems (removing export prefixes), log level regex syntax errors, wallet dotfile handling, duplicate database migration conflicts, and a broken custom Ansible filter [1].

The test harness validated four stages: connectivity checks, YugabyteDB initialization, Kuri node deployment with health checks, and S3 frontend deployment. All passed. The deployment pipeline was repeatable, tested, and committed. This created a natural inflection point — the foundation was solid, and the question became: what comes next?

Part II: The Research Pivot — From Execution to Exploration

The user's response to this completion was not incremental polish. It was a strategic pivot. In message 1676, the user issued a directive that would reshape the entire subsequent conversation: "Plan the following work: Milestone 02: Enterprise Grade - Metrics - Log & monitoring - Backup restore - Docs - Support (support - build KB and an support AI agent); Milestone 03: Persistent Retrieval caches - Retrieval Prefetcher (per kuri node); Milestone 04: Data lifecycle - Garbage collection on Filecoin - Deal extension - Repairing process — Start agents to investigate and understand deeply the requirements, research sota approaches to open problems, write a detailed execution plan." [2]

This message is remarkable for its specificity. The user did not simply say "make the system better." They enumerated three milestones with explicit sub-components, provided algorithmic constraints (the garbage collection process "should sequentially read old sectors for compaction and use range reads on cql indexes to avoid (n log n) ops in db"), and established a methodology: research first, plan second, execute third [4]. The user also gave a critical architectural freedom: "The plan should consider existing codebase shape but it should NOT consider migration - breaking changes can be made without any compat constraints." This was a greenfield mandate within a brownfield context, signaling that the team was free to redesign components rather than being constrained by backward compatibility.

The assistant responded by launching multiple parallel research agents. Each agent investigated a specific domain: the existing Prometheus metrics infrastructure (finding 49 metrics scattered across six files), the retrieval caching architecture (a basic 512MB LRU cache), the garbage collection status (the Unlink() method literally contained panic("implement me")), the logging system (structured but only in text format), the backup requirements (wallet keys with no backup automation), and state-of-the-art approaches for each of these problems [7]. This research phase was not optional browsing — it was a systematic investigation designed to ground the execution plan in the actual codebase.

Part III: The Synthesis — From Research to a Coherent Plan

The research agents returned their findings, and the assistant's task was to synthesize ten independent investigations into a single, coherent execution blueprint. The result was message 1681, a comprehensive plan spanning three milestones, 12 weeks of implementation, and dozens of new files across the Go codebase [7].

The plan's architecture reveals a clear design philosophy. Milestone 02 (Enterprise Grade) comes first because observability is the prerequisite for everything else — you cannot tune what you cannot measure, and you cannot recover what you have not backed up. The plan called for approximately 30 new Prometheus metrics across deal pipeline, financial/balance, database operations, and S3 frontend domains; JSON structured logging with correlation IDs; automated wallet backup to configurable S3 endpoints; and a self-hosted AI support agent using Mistral/Llama via Ollama. Milestone 03 (Persistent Retrieval Caches) proposed replacing the 512MB LRU cache with an Adaptive Replacement Cache (ARC) for L1, adding a configurable L2 SSD cache (default 256GB) with SLRU eviction, implementing access tracking with decaying counters, and building DAG-aware prefetching. Milestone 04 (Data Lifecycle Management) centered on a new GroupToMultihash reverse index for O(n) garbage collection, reference counting on S3 objects, and a passive GC strategy where dead groups simply do not get their Filecoin claims extended [7].

The plan was not just a list of features. It contained concrete Go struct definitions, YAML configuration examples, Prometheus recording rules, CQL table schemas, and algorithmic pseudocode. Every component was specified in sufficient detail for immediate implementation. The ARC cache design included admission policies for the L2 tier that only admitted items accessed at least twice in L1 — a defense against cache pollution. The GC algorithm included a three-phase design with a grace period to prevent collecting blocks that were temporarily at zero references due to concurrent transactions. The backup system ranked components by criticality, with the wallet (irreplaceable Filecoin keys) at the top and offloaded groups (recoverable from Filecoin) at the bottom [7].

Part IV: The Decisions — Four Questions That Shaped the Architecture

The plan was comprehensive, but it contained open questions that required the user's judgment. The assistant distilled these into four targeted questions: LLM provider (cloud-hosted vs. self-hosted), L2 cache budget, GC strategy (passive vs. active), and backup destination [10].

The user's answers, captured in message 1684, were decisive. Self-hosted LLM (Mistral/Llama via Ollama) — signaling a preference for autonomy and data sovereignty over raw model capability. Configurable L2 cache in the hundreds of GB — emphasizing flexibility. Passive GC only — a conservative choice that minimizes risk of data loss by simply not extending claims for dead groups rather than actively deleting data. Configurable S3 endpoint (AWS or self-hosted MinIO) — again emphasizing configurability and diverse deployment environments [10].

The assistant immediately translated these answers into concrete configuration structures. The self-hosted LLM became a YAML config with provider, model, and base_url fields. The backup destination became a Go struct with six environment-variable-backed fields: BACKUP_S3_ENDPOINT, BACKUP_S3_BUCKET, BACKUP_S3_ACCESS_KEY, BACKUP_S3_SECRET_KEY, BACKUP_S3_REGION, and BACKUP_ENCRYPTION_KEY. The cache configuration became another struct with FGW_L1_CACHE_SIZE_MIB, FGW_L2_CACHE_SIZE_GB, FGW_L2_CACHE_PATH, and FGW_L2_CACHE_ENABLED [10]. This translation from abstract decision to concrete configuration is the essence of architectural design — it forces specificity and creates artifacts that can be directly implemented.

Part V: The Green Light — From Planning to Execution

With the plan documented and the decisions made, the user issued the final directive in message 1689: "execute all milestones, avoid asking questions, test incrementally as implementation progresses - unit, integration tests. Refer to milestones document as needed, generously." [16]

This was a mandate for autonomous, test-driven execution. The assistant responded immediately, creating a structured todo list with the first task — "M02: Add deal pipeline metrics (deal_metrics.go)" — marked as in_progress [16]. The pattern was set: break each milestone into discrete tasks, start with the first, test incrementally, and proceed without seeking further approval.

The assistant did not jump directly into writing code. It first read three existing files: retr_metrics.go (the existing metrics pattern), deal_tracker.go (the deal lifecycle logic), and deal_db.go (the database interaction layer) [17]. This "read before write" discipline ensured that new code would follow established conventions — the promauto.NewCounter pattern, the struct-based metrics organization, the naming conventions. The assistant was not just adding metrics; it was extending a system, and extension requires understanding the system's architecture.

Part VI: The Execution — Building Enterprise-Grade Infrastructure

The implementation of Milestone 02 proceeded methodically. The assistant created four metrics files: rbdeal/deal_metrics.go for deal pipeline metrics, rbdeal/balance_metrics.go for financial and balance tracking, database/metrics.go for database operation metrics, and server/s3frontend/metrics.go for the S3 frontend proxy [21]. Each file followed the established pattern of defining a Go struct with Prometheus counter fields initialized via promauto.

The placement of database/metrics.go in the database package rather than in rbdeal was a deliberate architectural decision. Database operations are a distinct infrastructure layer from deal pipeline logic, and placing metrics close to the code they instrument respects package boundaries and avoids import bloat [21]. This is the kind of decision that a developer makes almost without conscious thought, but it reveals an understanding of how code organization affects maintainability.

The assistant then added LogFormat and BackupConfig to the configuration system, enabling JSON structured logging and configurable backup endpoints [27]. The LogFormat option, with values "text" and "json", was a small change with transformative impact — it enabled log aggregation systems like Loki to parse structured fields, turning unstructured text into queryable observability data [7].

The most architecturally significant component of Milestone 02 was the trace package in server/trace/. This package implemented correlation ID generation, HTTP context propagation, middleware injection, structured log field formatting, and request propagation — seven functions that together form a miniature distributed tracing framework [33]. The assistant wrote the implementation and a comprehensive test suite simultaneously, then ran go test ./server/trace/... -v and watched all seven tests pass in zero seconds [33]. This was the "test incrementally" mandate in action: write a focused unit of functionality, validate it immediately, then move on.

A build verification at message 1698 confirmed that all new metrics files compiled correctly across the three affected packages [24]. The assistant had reached a natural checkpoint: the core of Milestone 02 — metrics definitions, logging configuration, trace infrastructure — was structurally complete.

Part VII: The Transition — From Enterprise Grade to Caching

With Milestone 02's core deliverables validated, the assistant updated the todo list, marking all four metrics tasks as completed, and announced the transition: "Now let me update the todo and start on the caching (Milestone 03)." [36]

This transition message is deceptively simple, but it carries enormous contextual weight. It marks the boundary between two fundamentally different kinds of work: observability infrastructure (metrics, logging, tracing) and performance optimization (caching, prefetching). The assistant's decision to move to caching before completing every detail of Milestone 02 (wallet backup implementation, Loki integration, AI support agent) reflects a breadth-first approach — getting each milestone to a structurally complete state before moving on, rather than finishing every detail of one before touching the next [36].

The first task of Milestone 03 was the ARC cache. The assistant created the rbcache directory, wrote arc.go (the ARC implementation) and arc_test.go (the test suite), and immediately ran the tests. All six tests passed: basic operations, eviction, promotion, scan resistance, ghost list adaptation (with the log line "P changed from 0 to 1" confirming the adaptive parameter was working), and update [40]. The ARC cache is a sophisticated algorithm — it maintains four lists (two LRU lists and two ghost lists) and an adaptive parameter p that dynamically balances between recency and frequency. The ghost list adaptation test is the most algorithmically distinctive: it confirms that the cache is not a static partition but an adaptive system that responds to workload patterns [40].

Part VIII: The Checkpoint — Recognizing Cognitive Limits

The chunk concludes with a remarkable meta-cognitive moment. After the ARC cache tests passed, the user sent what appears to be an empty message (index 1716). The assistant's response was not to continue implementing — it was to create a meticulously structured, 1,200-word "Detailed Prompt for Continuing Session" that inventories every file created, every test passed, every design decision made, and every next step required [42].

This response reveals sophisticated self-awareness. The assistant recognized that the conversation had accumulated hundreds of messages and was approaching the limits of its context window. Rather than continuing to build new components that might become disconnected from earlier decisions, it paused to externalize its state into a portable document — a survival strategy for maintaining coherence across the fragility of conversational context [42].

The checkpoint document includes a project overview, a detailed inventory of accomplishments organized by milestone, a file tree of all new and modified files, a prioritized task list for remaining work, a reminder of key design decisions (self-hosted LLM, passive GC, configurable S3 backup), shell commands for resuming work, and important contextual notes (breaking changes are allowed, algorithms must be O(n), GC is passive only). This is not written for a human reader — it is written for the model itself, to reconstruct its mental state after a context reset [42].

Conclusion: The Architecture of a Transition

This chunk of the coding session tells a story that is larger than any single feature or bug fix. It is a story about how disciplined engineering teams navigate the gap between foundation and feature, between execution and exploration, between planning and implementation. The arc is clear: validate the deployment infrastructure, pivot to research, synthesize findings into a coherent plan, make critical design decisions, execute incrementally with testing at every step, and recognize when cognitive limits require a checkpoint.

The patterns visible in this chunk are transferable to any complex software project. The "read before write" discipline ensures new code follows established conventions. The incremental testing mandate catches errors when they are cheapest to fix. The structured decision-making process — ask targeted questions, capture answers precisely, translate into configuration — prevents ambiguity from propagating into implementation. And the meta-cognitive checkpointing — recognizing when context is fragile and externalizing state — is a survival strategy for any long-running development effort.

The Filecoin Gateway project, at the end of this chunk, has a validated deployment pipeline, a comprehensive execution plan, four new metrics files, a distributed tracing system, an ARC cache with passing tests, and a checkpoint document that can survive a session reset. The foundation is laid. The next phase — L2 SSD cache, access tracking, prefetching, garbage collection, deal extension, repair — awaits. But the patterns established in this chunk will carry the work forward: research before implementation, testing before integration, and checkpointing before context loss.