The Latency Tax You're Already Paying
Every trading system has a cache layer. Whether it's Redis, Memcached, or ElastiCache sitting between your strategy engine and the data it needs, there's a network hop your algorithm pays on every single decision cycle. Market data lookups, position state, risk limits, order book snapshots — each one adds latency to your critical path.
Most teams accept this as a fixed cost. It shouldn't be. That round-trip to Redis typically adds 300 to 500 microseconds per call. Multiply that across the thousands of cache reads a modern strategy executes per second, and you're looking at milliseconds of dead time per trading cycle — time where your algorithm is waiting instead of acting.
In markets that move in microseconds, that delay isn't just overhead. It's lost alpha.
What Changes with a High-Performance L1 Cache
Cachee sits between your application and your existing ElastiCache or Redis cluster as a transparent RESP proxy. Your code doesn't change — you swap one connection string. But the performance profile transforms completely.
Hot data — the positions, limits, and market state your algo checks thousands of times per second — gets served from a native in-process L1 cache. No network hop. No serialization. No TCP round-trip. Just a memory read measured in microseconds, not milliseconds.
The L1 layer uses an adaptive admission policy that learns your access patterns and keeps the hottest keys in memory while evicting long-tail data that belongs in the L2 Redis tier. The result is a 95%+ hit rate on real production traffic without any manual tuning.
Why RESP Proxy Beats Every SDK
Most caching solutions require an SDK integration: new dependencies, new API calls, new failure modes. Cachee takes a fundamentally different approach. The RESP proxy speaks native Redis protocol over raw TCP. Your existing Redis client — ioredis, redis-py, go-redis, Jedis, whatever you already use — connects directly.
| Metric | REST API | RESP Proxy |
|---|
| L1 hit latency | ~14 µs | ~1 µs |
| Protocol overhead | HTTP + JSON parse | Binary RESP2 |
| Code changes | New SDK + API calls | Zero — swap connection string |
| Failure modes | HTTP timeouts, JSON errors | Same as Redis |
The difference matters at scale. A strategy that makes 10,000 cache reads per second saves 130 milliseconds per second by moving from REST to RESP — that's 130 milliseconds of compute time returned to your algorithm every single second.
The Architecture in Practice
The deployment model is intentionally simple. Cachee runs on the same box as your trading application, or on a dedicated node in the same VPC. Your application connects to localhost:6380 instead of your ElastiCache endpoint. Everything else stays the same.
Before: App → ElastiCache (300-500 µs per read)
After: App → Cachee L1 (~1 µs, 95% of reads) → ElastiCache L2 (misses only)
Cache writes flow through to ElastiCache automatically, so your L2 layer stays in sync. If Cachee restarts, it warms from ElastiCache transparently — no cold-start risk. Your strategy never sees a cache miss that wouldn't have already been a miss against bare Redis.
Where the Alpha Actually Comes From
Shaving microseconds off cache reads doesn't just make your system faster. It changes what your system can do within its latency budget:
- More signals per cycle. When each lookup costs 1 µs instead of 400 µs, your strategy can check 400x more data points before the market moves.
- Tighter risk checks. Pre-trade risk validation that was too slow for the hot path becomes feasible at L1 speed.
- Deeper order book state. Cache the full book instead of just top-of-book. At 1 µs per read, depth is free.
- Faster position updates. Cross-venue position aggregation that took milliseconds now takes microseconds.
The firms that win aren't necessarily running smarter strategies. They're running the same strategies with less infrastructure drag. Every microsecond you reclaim from your cache layer is a microsecond your competitor is still wasting.
One Connection String. That's It.
There's no integration project here. No new SDK to evaluate. No vendor lock-in. Cachee speaks Redis protocol — if you decide to remove it, you point back at ElastiCache and nothing else changes. The entire deployment is a single connection string swap and a 90-second install.
Your algorithms are already fast. Your cache layer is the bottleneck you stopped questioning. It doesn't have to be.
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The Numbers That Matter
Cache performance discussions get philosophical fast. Here are the actual measured numbers from production deployments running on documented hardware, so you can compare against your own infrastructure instead of trusting marketing copy.
- L0 hot path GET: 28.9 nanoseconds on Apple M4 Max, single-threaded against pre-warmed in-memory cache. This is the floor — there's no faster way to read a key.
- L1 CacheeLFU GET: ~89 nanoseconds on AWS Graviton4 (c8g.metal-48xl). Sharded DashMap with admission filtering.
- Sustained throughput: 32 million ops/sec single-threaded on M4 Max, 7.41 million ops/sec at 16 workers on Graviton4 c8g.16xlarge.
- L2 fallback: Sub-millisecond hits against ElastiCache Redis 7.4 over same-AZ network when L1 misses cascade through.
The compounding effect matters more than any single number. A 28-nanosecond L0 hit means your application spends almost zero time on cache lookups in the hot path, leaving the CPU free for the actual business logic that generates revenue.
Average Latency Hides The Real Story
Average latency is the most misleading number in cache benchmarking. The percentile distribution is what actually breaks production systems. Tail latency — the slowest 0.1% of requests — is where users notice the lag and where SLAs get violated.
| Percentile | Network Redis (same-AZ) | In-process L0 |
|---|
| p50 | ~85 microseconds | 28.9 nanoseconds |
| p95 | ~140 microseconds | ~45 nanoseconds |
| p99 | ~280 microseconds | ~80 nanoseconds |
| p99.9 | ~1.2 milliseconds | ~150 nanoseconds |
The p99.9 spike on networked Redis isn't a bug — it's the cost of running a single-threaded event loop that occasionally blocks on background tasks like RDB snapshots, AOF rewrites, and expired-key sweeps. Cachee's L0 stays inside a few hundred nanoseconds because the hot-path read is a lock-free shard lookup with no background work scheduled on the same thread.
If your application is sensitive to tail latency — payments, real-time bidding, fraud detection, trading — the p99.9 number is the one to optimize against. Average latency improvements that don't move the tail are vanity metrics.
Memory Efficiency Is The Hidden Cost Lever
Throughput numbers get the headlines but memory efficiency determines your monthly bill. A cache that stores the same hot data in less RAM lets you run a smaller instance class — and on AWS that's the difference between profitable and breakeven for a lot of services.
Redis stores each key as a Simple Dynamic String with 16 bytes of header overhead, plus dictEntry pointers in the main hashtable, plus embedded TTL metadata. For 1KB values, per-entry overhead lands around 1100-1200 bytes once you account for hashtable load factor and slab fragmentation. At a million keys, that's roughly 1.2 GB of resident memory just for the data.
Cachee's L1 layer uses sharded DashMap entries with compact packing — a 64-bit key hash, value bytes, an 8-byte expiry timestamp, and a small frequency counter for the CacheeLFU admission filter. Per-entry overhead lands at roughly 40 bytes of structural data on top of the value itself. For the same million-key workload, that's about 13% smaller resident memory. On AWS ElastiCache pricing, that gap is the difference between needing a cache.r7g.large versus a cache.r7g.xlarge for borderline workloads.
Observability And What To Measure
You can't tune what you can't measure. The four metrics that matter for any production cache deployment, in order of importance:
- Hit rate, broken down by key prefix or namespace. A global hit rate of 92% sounds great until you discover that one critical namespace is sitting at 40% and dragging your tail latency. Per-prefix hit rates expose which workloads are getting cache value and which aren't.
- Latency percentiles, not averages. p50, p95, p99, and p99.9 for both cache hits and cache misses. The cache miss latency is your fallback path performance — when the cache fails, this is what your users actually experience.
- Memory pressure and eviction rate. If your eviction rate is climbing while your hit rate stays flat, you're under-provisioned. If both are climbing, your access pattern shifted and you need to retune TTLs or rethink what you're caching.
- Stale-read rate. The percentage of cache hits that returned a value the application then discovered was stale. This is the canary for your invalidation strategy. If it's above 1%, your invalidation logic has a bug.
Cachee exposes all four out of the box via Prometheus metrics on the standard scrape endpoint, plus a real-time SSE stream for dashboards that need sub-second visibility. The right time to wire these into your monitoring stack is before the migration, not after the first incident.