Designing high-performance caches in Rust is a multi-disciplinary problem: data structures, memory layout, concurrency, workload modeling, and systems-level performance all matter. The points below reflect what moves the needle in practice across systems, services, and libraries.

1. Workload First, Policy Second

Cache policy only matters relative to workload.

Identify access patterns:

• Hotset-heavy traffic: skewed keys, high churn.
• Scan-heavy traffic: large working sets, weak locality.
• Mixed traffic: bursts of hot data over large cold sets.

Measure:

• Reuse distance / stack distance.
• Read/write ratio.
• Temporal vs spatial locality.

Choose policies accordingly:

• LRU: good for temporal locality, bad for scans.
• LRU-K / 2Q (roadmap): better at filtering one-off accesses.
• Clock / ARC (roadmap): lower overhead, more adaptive.

Never design a “general purpose” cache first; design for the workload you expect.

2. Memory Layout Matters More Than Algorithms

In a cache, memory layout often dominates policy.

Prefer:

• Contiguous storage (Vec, slabs, arenas).
• Index-based indirection over pointer chasing.

Avoid:

• Excessive Box, Arc, linked lists.
• HashMap lookups in hot paths if avoidable.

Techniques:

• Store metadata (recency, freq, flags) in tightly packed structs.
• Separate hot metadata from cold payloads.
• Use slab allocators for fixed-size entries.

Cache misses caused by your own data structure are as bad as upstream misses.

3. Concurrency Strategy Is Core Design, Not a Wrapper

Locking strategy shapes everything.

Options:

• Global lock: simple, often fast enough for small cores, dies under high contention.
• Sharded caches: hash key -> shard, each shard independently locked.
• Lock-free or mostly-lock-free: hard in Rust, only worth it if contention dominates.

Rust-specific notes:

• When std is available, prefer parking_lot locks over std::sync for lower overhead and better ergonomics.
• Avoid Arc<Mutex<…» in hot paths.
• Consider per-thread caches with periodic merge.
• Consider RCU-style read paths for read-heavy caches.

4. Avoid Per-Operation Allocation

Allocations kill throughput.

Pre-allocate:

• Entry pools.
• Node arrays.

Reuse:

• Free lists.
• Slabs.

Use:

• Vec with capacity management.
• Custom allocators if necessary.

Avoid:

• Creating new Arc, String, Vec per lookup.

If malloc shows up in your flamegraph, your cache is already slow.

5. Eviction Must Be Predictable and Cheap

Eviction is the critical slow path.

O(1) eviction is the goal.

Avoid unbounded tree walks or scans in eviction paths.

Maintain:

• Direct pointers/indices to eviction candidates.
• Eviction lists or clock hands.

Be careful with:

• Background eviction threads (synchronization overhead).
• Lazy cleanup that grows unbounded.

Eviction cost must be comparable to lookup cost, not orders of magnitude higher.

6. Metrics Are Not Optional

You cannot tune what you do not measure.

Track at least:

• Hit / miss rate.
• Eviction count and reason.
• Insert/update rate.
• Scan pollution rate.
• Lock contention or wait time (roadmap).

Expose:

• Lightweight counters in hot path.
• Optional detailed metrics behind feature flags.

Metrics should guide design decisions, not justify them afterward.

7. Separate Policy From Storage

Design in layers:

• Storage layer: how entries live in memory, allocation, layout, indexing.
• Policy layer: LRU, FIFO, LFU, LRU-K (roadmap: Clock/ARC/2Q, etc; see docs/policies/roadmap/README.md); only manipulates metadata and ordering.
• Integration layer: ties application objects, payloads, or IDs into cache entries.

Related docs:

• Policy summaries: docs/policies.md
• Policy implementations: docs/policies/README.md
• Policy data structures: docs/policy-ds/README.md

This makes:

• Benchmarking easier.
• Policy experimentation cheap.
• Reasoning about performance clearer.

8. Beware of “Nice” Rust APIs in Hot Paths

Ergonomics often cost performance.

Avoid in critical loops:

• Heavy generics causing code bloat.
• Trait objects for hot dispatch.
• Closures capturing state.
• Iterator chains instead of simple loops.

Prefer:

• Explicit loops.
• Concrete types.
• Monomorphized fast paths.

You can wrap fast internals in nice APIs at the edges.

9. Scans Are the Enemy of Caches

In scan-heavy workloads:

Large sequential reads destroy LRU-style caches.

Solutions:

• Scan-resistant policies (LRU-K, 2Q/ARC are roadmap).
• Explicit “scan mode” hints from the caller or workload layer.
• Bypass cache for known one-shot reads.

If you ignore scans, your cache will look great in microbenchmarks and terrible in production.

10. Benchmark Like a System, Not a Library

Do not rely on random key benchmarks.

Use:

• Zipfian distributions.
• Mixed read/write workloads.
• Scan + point lookup mixtures.
• Time-varying hot sets.

Measure:

• Throughput.
• Tail latency.
• Memory overhead.
• Eviction cost.

A cache that is 5% faster on random keys but 50% worse under scans is a bad cache.

11. Rust-Specific Pitfalls

Arc is expensive in hot paths.

Borrow checker can push you toward indirection—fight it with:

• Index-based access.
• Interior mutability only where unavoidable.

Beware of:

• Hidden clones.
• Trait object dispatch.
• Over-generic designs.

Rust can be as fast as C, but only if you design like a systems programmer, not a library author.

12. Design for Failure Modes

Ask:

• What happens under memory pressure?
• What happens when eviction cannot keep up?
• What happens under pathological access patterns?

Add:

• Backpressure or rejection when full.
• Bypass modes.
• Emergency eviction strategies.

A cache that collapses under stress is worse than no cache.

Bottom Line

High-performance caches are not about clever algorithms—they are about:

• Memory layout.
• Allocation discipline.
• Contention control.
• Eviction predictability.
• Workload realism.

In Rust, your main enemy is not safety—it is abstraction overhead and accidental allocation. Design from the metal upward, then wrap it in something pleasant to use.