Boosting Performance 25× and Cutting Costs 80%: Our Switch from Redis to DragonflyDB

Background and Original Redis Architecture

Our high‑traffic SaaS product used Redis as both a cache and session store. The deployment consisted of a Redis Cluster with six master shards, each having three replicas, totaling 18 nodes and about 1.2 TB of memory. The workload was roughly 70 % reads and 30 % writes, primarily hash and list operations, reaching peak throughput of 1.5 million operations per second. Monthly cloud‑hosted Redis costs were around $48,000.

┌─────────────┐
               │   Client    │
               └──────┬──────┘
                      │
               ┌──────▼───────┐
               │ Redis Cluster│
               │(6 shards, 3 repl)│
               └──────┬───────┘
                      │
               ┌──────▼───────┐
               │   Backend    │
               └──────────────┘

Redis Pain Points at Scale

Memory Overhead – Each shard runs single‑threaded; scaling throughput required adding more shards and replicas, causing exponential cost growth.

Operational Complexity – Resharding required downtime and was error‑prone; failover was not seamless.

Performance Bottlenecks – Even after tuning maxmemory‑policy, eviction strategies, and pipeline batching, latency spikes persisted under peak load.

Replication Lag – Burst writes caused replicas to fall behind the master by several seconds.

We questioned whether Redis remained suitable for our workload.

Why Choose DragonflyDB?

DragonflyDB positions itself as a drop‑in replacement for Redis and Memcached, offering:

Multithreaded Architecture – Fully utilizes modern multi‑core CPUs.

Extreme Throughput – Benchmarks show up to 25 million QPS per node.

Higher Memory Efficiency – Eliminates replication overhead and fragmentation.

Redis‑Protocol Compatibility – Requires minimal code changes.

In theory, DragonflyDB appears to be a “super‑charged” Redis.

Migration Process

We designed a three‑stage migration: shadow deployment, dual‑write, and cut‑over.

1. Shadow Deployment

Cluster Size : 3 nodes, each with 512 GB RAM.

Data Mirroring : A custom proxy replayed Redis traffic to DragonflyDB.

Verification : Compared key/value checksums and latency distributions.

2. Dual‑Write Strategy

Application code was updated to write to both Redis and DragonflyDB while reads continued from Redis.

// Pseudocode in Go
for _, client := range []RedisClient{redis, dragonfly} {
    client.Set(ctx, key, value, ttl)
}

Read operations remained on Redis.

Write operations were sent to both stores.

After two weeks of consistency checks we felt confident to switch.

3. Cut‑Over and Rollback Preparation

Redirected read traffic to DragonflyDB.

Kept Redis cluster hot as a fallback, tolerating replication lag.

Rollback plan: flip a feature flag to instantly revert reads to Redis.

Performance Testing

Using memtier_benchmark in a production‑like environment, DragonflyDB achieved up to 25 million QPS on a single node, far surpassing the original Redis cluster’s 1.5 million QPS peak.