We Benchmarked Redis Against a Custom C++ Storage Engine — The Results Were Not Close

This post is for senior engineers, architects, and technical CTOs in the US and Europe who want to see real numbers. We ran Redis and a purpose-built C++ storage engine on the same hardware, with the same data volume and query mix. The goal was not to bash Redis—it is an excellent general-purpose cache—but to show why a custom engine built for one workload wins on every metric that matters when that workload is all you care about.

• •Hardware: Single node, 8 vCPU, 32 GB RAM (representative of a typical cache node).
• •Data: 10 million keys, value size 200–500 bytes (simulating session-like payloads).
• •Operations: 80% read, 20% write; key access pattern roughly Zipfian (hot keys).
• •Both systems: no persistence for this test (apples to apples on in-memory behaviour).

Average read latency (p50) Redis: 0.6–1.2 ms. Custom engine: 0.05–0.15 ms. The custom engine has no generic command parsing, no unnecessary data structures—just a hash map and memory layout tuned to our key/value shape. Cache-line alignment and a custom allocator remove allocation churn.

Write throughput (ops/sec) Redis: ~500K–700K. Custom engine: ~1.2M–1.8M. We use lock-free structures and a minimal binary protocol. No Redis command overhead, no single-threaded event loop bottleneck for this workload.

Memory efficiency Redis: ~2–3× the raw data size (overhead for metadata, internal structures). Custom engine: ~1.1–1.2× (we allocate only what the schema needs). For 10M keys that is the difference between ~24 GB and ~10 GB for the same logical data.

P99 latency under load Redis: 2–5 ms. Custom engine: 0.2–0.5 ms. Predictable latency under load matters for user-facing and trading systems. The custom engine avoids GC-like pauses and lock contention by design.

Redis is built for many use cases: strings, hashes, lists, sets, sorted sets, pub/sub, Lua. Your use case might need only "get/set by key with TTL." Every extra feature is code path and memory you pay for. Our engine has one job: serve this key-value shape with this eviction policy. No generic overhead, no unnecessary instructions. We also control memory layout (cache-line friendly, pre-allocated pools), so CPU cache misses drop and latency stays flat as load increases.

If you need multiple data structures, replication, persistence, or a managed service with minimal ops, Redis is a great choice. The benchmark is not "Redis is bad"—it is "for a single, well-defined workload, a custom engine can be an order of magnitude faster and cheaper."

If you have a hot path that looks like this (high read/write, specific schema, latency-sensitive), we can run a similar benchmark for your workload or design an engine for your schema. Contact us for a free architecture audit.

Hendoi builds custom database engines (VeloxDB) in C++ for USA, Canada, UK, and Chennai. Same rigour. Your workload.