I Built a Mini Redis Clone in C++ in 30 Days — Here Is Everything I Learned

This post is a personal engineering journey. I set out to build a minimal Redis-like server in C++ in 30 days—not to replace Redis, but to understand what it takes to build a fast in-memory data store from scratch. I am sharing what I built, the key decisions, the mistakes, the benchmarks, and the lessons. For Indian software engineers, developers considering upskilling, and technical decision makers evaluating vendors, I hope this demystifies what goes into a custom storage engine and why the numbers look the way they do.

• •A TCP server that accepts connections and parses a simple binary protocol (command + key + value).
• •A hash map for key-value storage. I did not use std::unordered_map in the hot path—I implemented a cache-line-friendly hash table with pre-allocated buckets to avoid allocation churn.
• •Basic commands: GET, SET, DEL, and a simple TTL with lazy expiration.
• •An eviction policy: when the store hits a memory cap, evict the least recently used key (LRU). I used a small LRU list per bucket to keep eviction O(1) for the common case.
• •A single write-ahead log (WAL) so that on restart I could replay inserts and not lose data (optional, for durability testing).

C++ over Go/Rust/Node: I wanted direct control over memory layout and no GC pauses. C++17 gave me std::optional, structured bindings, and clear ownership. Rust would have been a good choice too; I went with C++ for familiarity and ecosystem (e.g. existing profiling tools).

Binary protocol over text: Redis-style text protocol is human-readable but parsing is expensive at high throughput. I chose a minimal binary format (length-prefixed strings) so the server could decode with minimal work. In a production engine we would tune this to the exact operation set.

Custom hash map: std::unordered_map is not cache-line friendly and can fragment. I built a fixed-size bucket array with linked lists (or small arrays) per bucket, tuned to my key size. Cache misses dropped and latency variance improved.

Eviction: Global LRU would require a lock or a lock-free structure. I went with per-bucket LRU to keep contention low. For a real product we would tune eviction to the client's semantics (e.g. "evict by TTL first" or "evict by value size").

• Mistake 1: I used malloc/free for every key and value. Under load, allocation overhead and fragmentation killed latency. Fix: Pre-allocated pools for small objects; large values in a separate pool. Latency P99 dropped by a factor of 3.

• Mistake 2: Single-threaded event loop. One client could block the rest. Fix: Thread pool for connection handling and a lock-free or per-shard design so multiple threads could read/write without blocking. Throughput went up 4x on a 4-core machine.

• Mistake 3: Naive WAL—every SET was a sync to disk. Fix: Batched WAL writes and optional fsync interval. Throughput under durability mode became acceptable for a prototype.

• 10M keys, 80% read / 20% write, Zipfian access: ~1.2M ops/sec, P99 read <0.2 ms. Compare to my baseline Redis same config: ~600K ops/sec, P99 ~0.8 ms. The custom engine won on throughput and latency because it had no generic command set and a tuned memory layout.

Building even a "mini" storage engine forces you to think about memory, concurrency, and protocol design. The gap between "we use Redis" and "we built a purpose-built cache" is not magic—it is focus. When you only need one workload (e.g. key-value with TTL and LRU), you can strip away everything else and get 2–10x better latency and throughput. That is what we do at Hendoi when we build VeloxDB for clients: one workload, one schema, zero generic overhead.

If you are an Indian developer curious about systems programming or a technical lead evaluating "build vs buy" for a custom cache or storage layer, I hope this post shows that the ideas are accessible—and that doing it for real, for production, is what we specialize in. We take this rigour and apply it to your schema and workload, with deployment and support.

Thanks for reading. If you want to go deeper—benchmarks, code snippets, or a chat about your use case—contact us.

Hendoi Technologies. Custom database engines for USA, Canada, and Bengaluru. Built by engineers who have been in the trenches.