For a long time, Redis felt like a silver bullet.
Need speed? Redis.
Need caching? Redis.
Need queues, rate-limiting, sessions, feature flags? Redis everywhere.
I’ve personally made that mistake and paid for it later at scale.
In the beginning, Redis is amazing:
Sub-millisecond reads
Simple data structures
Easy to deploy
Easy to justify
We started caching everything:
Auth sessions
API responses
Database query results
Counters
Background job states
Even business logic flags
Performance skyrocketed.
The mistake wasn’t using Redis.
The mistake was making Redis a core dependency for everything.
💥 What Broke at Scale
As traffic grew, Redis slowly turned from a helper into a single point of failure.
Here’s what started happening:
1️⃣ Redis Became Our “Second Database”
Instead of being a cache, Redis became stateful infrastructure:
Critical data existed only in Redis
Expiration bugs caused silent data loss
Cold restarts created cascading failures
At that point, Redis outages weren’t “performance issues” they were production incidents.
2️⃣ Latency Looked Great, Until Throughput Collapsed
This is something many teams miss:
Low latency ≠ High throughput
Redis can respond in microseconds, but:
Single-threaded command execution
Blocking commands (KEYS, large LRANGE, big Lua scripts)
Huge keyspaces with poor eviction policies
At scale:
P99 latency exploded
CPU maxed out
Network bandwidth became the bottleneck
Timeouts started propagating to APIs
Redis didn’t slow down — everything around it did.
3️⃣ We Used Redis Where the Database Was Better
Some data:
Needed strong consistency
Needed transactions
Needed relational integrity
But Redis was faster, so we used it anyway.
That led to:
Data mismatch between DB and cache
Complex cache invalidation logic
Hard-to-debug race conditions
At some point, the “performance optimization” was costing more engineering time than it saved.
🧯 The Turning Point: Redis Is a Tool, Not a Crutch
The real lesson wasn’t “Redis is bad”.
The lesson was:
Redis must be intentionally limited in responsibility.
We redefined Redis as:
A performance layer
Not a source of truth
Not a workflow engine
Not a business logic store
That mindset shift changed everything.
☁️ Hosting Redis Properly on AWS: MemoryDB vs Self-Managed
After multiple painful incidents, we moved away from self-managed Redis.
Why AWS MemoryDB?
Key reasons:
Multi-AZ with transactional durability
Redis-compatible but designed for high availability
Faster failover
No manual replication hell
Yes, it costs more.
But outages cost far more.
⚖️ Latency vs Throughput: The Tradeoff That Matters
This is the part most blogs skip.
Latency
Redis excels at single-key reads
Network placement matters more than instance size
Throughput
Sharding is mandatory at scale
Bigger instances ≠ linear throughput
Avoid large payloads
Rule I live by now:
If Redis needs vertical scaling, the design is already wrong.
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