Mapping the Cache: A Systematic Codebase Survey Before Feature Implementation

Introduction

In the fast-paced development of a distributed storage system, adding a new feature to the user interface often requires a careful understanding of what already exists beneath the surface. The message at index 2747 in this coding session captures a pivotal moment of discovery: the assistant, responding to the user's request to "show L1/L2 cache metrics" in the dashboard, pauses to conduct a thorough survey of the existing codebase before writing a single line of implementation code. This message is not about building—it is about understanding. It is a research artifact, a structured investigation that transforms scattered code into actionable knowledge.

Context and Motivation

The conversation leading up to this message had been moving at a rapid pace. The user had just committed a set of changes that included adding CIDGravity connection status to the WebUI, simplifying the Ansible deployment by removing Loki, Promtail, and AWS-based backup roles, and adding Ansible deployment instructions to the README. The commit message read: "feat: add CIDGravity status to UI and simplify Ansible." Immediately after the commit, the user issued a new directive: "UI in dashboard show L1/L2 cache metrics."

This request was the natural next step in a broader theme of operational empowerment and system observability. The team had been systematically adding visibility into critical system components—first CIDGravity connectivity, now cache performance. The L1 and L2 caches are central to the system's performance: L1 is an in-memory ARC (Adaptive Replacement Cache) that keeps frequently accessed data hot, while L2 is an SSD-backed cache that provides a larger, slightly slower tier. Understanding their hit rates, sizes, and utilization is essential for operators tuning the system and diagnosing performance issues.

The assistant's first move, however, was not to start coding. Instead, it invoked a structured research task: [task] {"description":"Find L1/L2 cache metrics","prompt":"Search the codebase for L1 and L2 cache related metrics, stats, and RPC endpoints."} This decision reveals a disciplined engineering mindset: before building, one must understand the terrain.

The Structure of the Investigation

The assistant's task prompt defined a clear scope with three search directions:

  1. Any existing cache stats/metrics in the iface package — The iface package contains the interface definitions that define the contract between system components. If cache statistics were already part of the diagnostic interface, the implementation would be simpler.
  2. Cache-related RPC endpoints in integrations/web/rpc.go — The WebUI communicates with the backend via RPC endpoints. Before adding a new endpoint for cache metrics, the assistant needed to know what already existed and whether any existing endpoint could be extended.
  3. Cache metrics in the rbcache package — This is the cache implementation itself. The assistant needed to understand what statistics the cache already tracks internally, even if they weren't yet exposed externally. This tripartite search strategy is methodical and complete. It covers the interface layer (what should exist), the transport layer (how data flows to the UI), and the implementation layer (what data actually exists). By searching all three, the assistant minimizes the risk of missing existing infrastructure that could be reused.

The Findings: A Map of Existing Cache Infrastructure

The assistant compiled its findings into a structured summary titled "Summary: L1 and L2 Cache Metrics and Stats." The first finding was in rbcache/arc.go (lines 486-512), where a CacheStats struct already existed for the L1 ARC cache:

type CacheStats struct {
    Size     int64
    Capacity int64
    T1Size   int64
    T2Size   int64
    B1Len    int
    ...

This struct tracked the current size in bytes, maximum capacity, the sizes of the T1 (recent items) and T2 (frequent items) lists, and ghost list lengths—all standard ARC algorithm metrics. The existence of this struct was significant: it meant the L1 cache already maintained detailed statistics internally. They simply weren't being exposed to the diagnostic interface or the WebUI.

Similarly, in rbcache/ssd.go, the assistant found an SSDCacheStats struct for the L2 SSD cache, with fields for size, max size, probation size, and protected size—reflecting the two-list (probation/protected) eviction strategy typical of SSD caches.

The assistant also examined rbdeal/retr_provider.go to understand how the retrieval provider stored references to both caches, and rbdeal/retr_metrics.go to see what Prometheus metrics were already being collected (cache hits, cache misses, HTTP success/failure counts, etc.).

Input Knowledge Required

To understand this message fully, one needs familiarity with several domains:

Output Knowledge Created

This message produces a clear, actionable map of the existing cache metrics infrastructure. The key insight is that both cache layers already maintain detailed internal statistics (CacheStats and SSDCacheStats), and Prometheus metrics already exist for cache hits and misses. However, these statistics are not exposed through the diagnostic interface (RIBSDiag) or through any RPC endpoint that the WebUI can call.

This gap defines the implementation work ahead: the assistant needs to:

  1. Add a CacheStats struct (or reuse the existing one) to the iface package so it becomes part of the public interface contract.
  2. Add a CacheStats() method to the RIBSDiag interface so the diagnostic layer can request cache statistics.
  3. Implement the CacheStats() method in the retrieval provider, plumbing through the existing internal statistics from the ARC and SSD caches.
  4. Add an RPC endpoint in integrations/web/rpc.go that the WebUI can call to fetch cache metrics.
  5. Update the WebUI dashboard to display the new metrics. The assistant's next message (index 2748) confirms this plan by creating a todo list with exactly these steps: "Add CacheStats struct to iface package," "Add CacheStats method to RIBSDiag interface," "Implement CacheStats in retrieval provider," "Add CacheStats RPC endpoint."

Assumptions and Potential Pitfalls

The assistant makes several assumptions in this investigation. It assumes that the existing CacheStats and SSDCacheStats structs are complete and accurate representations of the cache state. It assumes that the RIBSDiag interface is the correct place to expose cache statistics (rather than, say, a dedicated metrics endpoint). It assumes that the WebUI's existing RPC infrastructure can be extended without significant refactoring. And it assumes that the cache statistics are safe to expose—that they don't contain sensitive information or cause performance issues when queried.

One potential blind spot is concurrency safety: the cache statistics are likely read under a lock, but the act of collecting and transmitting them over RPC could introduce latency if not handled carefully. The assistant does not address this in the research message, though it may be considered during implementation.

Another assumption is that the Prometheus metrics already being collected (cacheHit, cacheMiss counters) are sufficient for the dashboard. The user asked for "L1/L2 cache metrics" specifically, which implies per-tier statistics (size, capacity, hit rate per cache) rather than aggregate counters. The assistant's findings confirm that per-tier statistics exist internally, which is good—but the Prometheus counters are aggregate, not per-tier, which may require additional work.

The Thinking Process Visible in the Message

The assistant's reasoning is visible in the structure of its investigation. It begins with a broad task definition that explicitly lists the three search directions. It then executes each search, reading the relevant files (arc.go, ssd.go, retr_provider.go, retr_metrics.go). Finally, it compiles the findings into a structured summary that highlights the key structures and their locations.

The thinking is systematic rather than opportunistic. The assistant does not simply grep for "cache" and hope for the best. It reasons about the architecture: the interface layer defines what can be exposed, the RPC layer defines how data flows, and the implementation layer defines what data exists. By searching all three, it builds a complete mental model of the system before making any changes.

This approach reflects a mature engineering practice: measure before you cut. The assistant could have jumped straight into implementation, adding an RPC endpoint and a dashboard widget, only to discover that the underlying cache statistics were incomplete or that a better interface already existed. Instead, it invested a few minutes in research, saving potentially hours of rework.

Conclusion

The message at index 2747 is a quiet but essential piece of the development process. It is not flashy—no code is written, no bugs are fixed, no features are delivered. But it embodies a discipline that separates professional engineering from hacking: the discipline of understanding the system before changing it. By systematically mapping the existing cache metrics infrastructure across interface, transport, and implementation layers, the assistant equips itself to implement the user's request efficiently and correctly. The subsequent messages show this plan being executed step by step, with the todo list from message 2748 directly following the structure laid out in this research message. In the broader narrative of the coding session, this message is the calm before the storm of implementation—a moment of clarity that makes everything that follows possible.