Distributed-Cache

A lightweight, Go-based distributed in-memory cache with consistent-hashing sharding, singleflight deduplication, size-bounded LRU, protobuf communication, and read-through replication

Architecture

Client → Group.Add("🐺", "Hymeis")
└─> Cache.Add("🐺", "Hymeis")
├─ insert into in-memory LRU
└─ async fan-out to R-1 successors:
└─ for each replica in GetReplicas("🐺", R)[1:]:
HTTP POST /dcache/<group>/🐺 (SetRequest)
Client → Group.Get("🐺")
├─ LRU hit? ──▶ return "Hymeis"
└─ cache miss:
└─ singleflight.Do("🐺", fn):
└─ pickPeer("🐺") via consistent-hash
├─ peer? ──▶ peerLoad (HTTP+Protobuf) ──▶ return "Hymeis"
└─ local? ──▶ localLoad:
├─ GetterFunc → origin data
├─ Replication() (see Add flow above)
└─ return "Hymeis"

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wrk2 BenchMark Testing

Sustained a constant 15 000 QPS workload with wrk2, observing:

• Throughput: 14 982 req/sec (≈15 000 target)
• Mean latency: 0.868 ms
• P50 / P75 / P90: 0.86 ms / 1.15 ms / 1.44 ms
• P99: 1.94 ms (well under 10 ms SLO)
• P99.9 / P99.99: 2.40 ms / 2.86 ms
• Max observed: 4.00 ms

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Next Steps

• Application side: Maybe make a Leetcode Top K ranking system?

How to run the project

Try

bash run.sh

and check the output shown