Attribution: This article was based on content by @failsafe on GitHub.
Original: https://github.com/OxidizeLabs/cachekit

Introduction

Caching is one of the oldest and most effective performance levers in systems design. For in-process services, an in-memory cache sits between the CPU caches and slower main memory, trimming latency for hot data and lowering backend load. A new open-source project, Cachekit — “High performance caching policies library in Rust” (source: OxidizeLabs/Cachekit; posted on Hacker News by @failsafe) — brings a focused approach: treat eviction and retention policies as a first-class, pluggable concern and build fast, concurrent implementations in Rust.

This article walks through why a policies-first cache library matters, how such a design maps to modern workloads, and what architecture choices drive throughput and predictability. I’ll cover eviction algorithms, concurrency strategies in Rust, memory budgeting, integration patterns, and practical recommendations for adopting a policy-driven cache.

Key Takeaways

  • Pluggable eviction policies let you match cache behavior to workload patterns and simplify experimentation in production.
  • Rust’s ownership and zero-cost abstractions enable safe, high-performance concurrent caches with lower overhead than managed languages.
  • Pay attention to memory budgeting, fragmentation, and contention; the right data structures and synchronization primitives matter more than raw algorithm choice.
  • Provide observability (hit/miss, evictions, memory footprint) and miss-handling hooks for practical adoption.
  • Start with simple policies (LRU or TTL) and add hybrid or adaptive policies (ARC, LFU) when workload asymmetries justify the complexity.

Background: Eviction policy determines which item to remove when a cache is full.

Background

Caching policies have a long theoretical and practical history. Belady (1966) defined the concept of optimal replacement (an oracle that evicts the item not used for the longest future time). Mattson et al. (1970) introduced evaluation techniques for storage hierarchies that underpin cache algorithm analysis. Later practical algorithms—LRU (least recently used), LFU (least frequently used), and hybrids—emerged to approximate optimal behavior under realistic constraints. LRU-K and ARC are examples of algorithms designed to adapt to workload patterns (O’Neil et al., 1993; Megiddo & Modha, 2003).

Background: LRU (Least Recently Used) evicts the item that was accessed least recently.

Main Content

Why make policies pluggable?

  • Workloads differ. Web session stores, CDN metadata, machine-learning feature caches, and database buffer pools have distinct access patterns (temporal locality, frequency skew, scans). A single fixed policy rarely fits all.
  • Experimentation matters. Production workloads evolve; being able to switch or combine policies at runtime reduces risk when tuning caches for new traffic.
  • Hybrid/adaptive policies win in mixed workloads. ARC (Adaptive Replacement Cache) dynamically balances recency and frequency to handle changing patterns (Megiddo & Modha, 2003).

Core eviction strategies to support

  • LRU (Least Recently Used): Simple and often effective for temporal locality.
  • LFU (Least Frequently Used): Keeps hot items that are repeatedly accessed; useful when frequency predicts future use.
  • TTL (Time To Live): Expires entries after wall-clock duration; great for TTL-based invalidation.
  • ARC (Adaptive Replacement Cache): Balances recency and frequency automatically.
  • CLOCK and approximations: Lower-overhead alternatives to LRU for high-throughput scenarios.

Data structure considerations

  • LRU is typically implemented with a hashmap + doubly-linked list to get O(1) get/put/evict. The linked list introduces pointer-chasing and cache-line misses.
  • LFU needs frequency counters; naive counters can cause heavy churn. Approximations like TinyLFU use sketches to reduce memory and update cost.
  • Hash table design (sharding, open vs closed addressing) and memory layout are critical for locality and false sharing avoidance.

Concurrency and Rust primitives

  • Rust provides strong tools: ownership, lifetimes, and zero-cost abstractions make it possible to implement low-level structures safely.
  • Common synchronization choices:
    • RwLock (read-write lock): Good when reads dominate and mutations are rarer.
    • Mutex: Simpler but can become a hotspot.
    • Arc (atomic reference count): For shared ownership of values across threads.
    • Lock-free structures: More complex but can reduce contention for ultra-low latency.
  • Sharding (partitioning cache into many independent segments) is often the most practical way to scale across cores with minimal contention. Each shard keeps its own policy and data structures.

Memory budgeting and measurement

  • In-process caches must enforce memory budgets explicitly. Consider storing value-size metadata and accounting during put/evict operations.
  • Fragmentation matters: allocations cause heap fragmentation; pooled or slab allocators reduce fragmentation and improve locality.
  • Decide whether budget is measured as number of entries, total bytes of values, or both. Byte accounting is more realistic but costlier to maintain.

API surface and miss handling

  • Minimal API: get, put, remove, and clear are the essentials.
  • Miss hooks: Provide synchronous and asynchronous backfill hooks (callbacks or futures) to fetch data on cache miss and populate the cache atomically.
  • Batch operations: Support scanning or fetching multiple keys to reduce per-operation overhead in high-throughput paths.
  • Metrics: expose hit rate, miss rate, evictions, average latency, and current memory usage.

Observability and benchmarking

  • Instrumentation is required: Prometheus metrics, histograms for operation latency, and counters for evictions make it manageable in production.
  • Realistic benchmarks should use production traces or well-known synthetic workloads (e.g., Zipfian, uniform, or scan-heavy).
  • Compare against other in-process caches (e.g., Rust crates that implement fixed-policy caches) and external caches (Redis) to understand trade-offs.

Security and safety

  • Be cautious with TTL and clock skew. Use monotonic clocks for relative expirations where possible.
  • Avoid exposing raw pointers or unsafe patterns to users. Rust’s safe abstractions can still use unsafe internally for performance but should be audited.

Examples/Applications

  1. API gateway session caching
  • Problem: An API gateway needs to validate tokens and enrich requests with user metadata. Calls to backend auth services are costly.
  • Solution: Use a sharded, LRU cache with small TTLs (e.g., 30s) and an asynchronous miss hook that refreshes the cache. This reduces tail latency for authenticated requests and keeps fresh data.
  1. Feature store for online ML
  • Problem: A low-latency feature store serving model inputs needs to hold recent high-cardinality keys and frequently accessed features.
  • Solution: Combine LFU for stable hot features and LRU for recent bursts; implement as a hybrid policy (or use ARC) to automatically balance recency vs frequency. Use byte-based budgeting and slab allocation to limit fragmentation.
  1. Local read-through cache in microservices
  • Problem: Microservices often read from a shared database; spikes cause query storms.
  • Solution: Implement cache-aside with get that triggers an async fetch on miss. Use a write-behind pattern for non-critical writes or cache invalidation for write-heavy flows. Expose metrics so autoscaling can react to high miss rates.

Best Practices

  • Start simple: LRU or TTL are easy to reason about. Add complexity only when you measure a real benefit.
  • Shard aggressively: Partition by hash to reduce lock contention and improve cache throughput.
  • Measure on production-like traces: Synthetic benchmarks can mislead—real access patterns reveal the real winners.
  • Use byte accounting for budgets: Counting entries hides size skew and can lead to surprising evictions.
  • Provide both sync and async miss handlers: Different callers may need different behaviors; async hooks help avoid blocking hot threads.

Implications

Policy-driven cache libraries like Cachekit reflect an increasing need for adaptable, high-performance building blocks in Rust. They fit a trend where in-process components emphasize pluggability and observability. Well-designed caching policies improve not only latency but also cost-efficiency by reducing backend load and smoothing traffic spikes.

Academic and practical literature supports this: Mattson et al. (1970) and Belady (1966) set theoretical foundations, O’Neil et al. (1993) explored frequency-aware policies, and Megiddo & Modha (2003) demonstrated the value of adaptive algorithms like ARC. In practice, the right mix of data structures, sync primitives, and memory accounting often matters more than the choice between two eviction algorithms.

Background: Sharding splits a cache into multiple independent partitions to reduce contention and improve parallelism.

Conclusion

A policies-first caching library in Rust brings several advantages: safer low-level code, better performance through careful data-structure design, and the flexibility to adapt cache behavior to diverse workloads. When adopting such a library, focus on memory budgeting, sharding, observability, and providing clear miss-handling semantics.

If you want to explore further, check out the Cachekit repository (OxidizeLabs/cachekit) and the Hacker News thread where it was shared by @failsafe. Benchmark with your production traces, instrument for observability, and iterate: caching is as much about good telemetry and tuning as it is about algorithms.

Credits and sources

  • Original project: “Show HN: Cachekit – High performance caching policies library in Rust” (OxidizeLabs/cachekit), Hacker News post by @failsafe — https://github.com/OxidizeLabs/cachekit
  • Foundational and related works: Belady (1966); Mattson et al. (1970); O’Neil et al. (1993); Megiddo & Modha (2003)

References

  • Belady (1966). On the optimality of certain cache replacement algorithms.
  • Mattson et al. (1970). Evaluation Techniques for Storage Hierarchies.
  • O’Neil et al. (1993). The LRU-K Page Replacement Algorithm for Database Disk Buffering.
  • Megiddo & Modha (2003). ARC: A Self-Tuning, Low-Overhead Replacement Cache.

References