Temporal + Redis Event Sourcing - Don't Lose Events When Shit Breaks

Here's the deal - I've been running event-driven systems in production for 3 years, and this combo is the only thing that doesn't make me want to quit programming.

The Problem with Everything Else

Event sourcing logs every change as an event. That's your audit trail and your source of truth. Sounds simple until you try to build it in production.

Every other event sourcing setup I've tried either:

• Lost events when Kafka decided to take a nap (goodbye customer orders)
• Had workflows that died mid-process and never recovered (hello manual cleanup scripts)
• Required a PhD in distributed systems just to debug why shit stopped working

Temporal keeps your workflows alive no matter what breaks. Redis Streams are fast as hell and don't require a Kafka PhD to operate. Put them together and you get event sourcing that actually works.

What This Architecture Actually Does

Event-driven architecture enables decoupled microservices to communicate through events - this is the foundation pattern we're building on.

Redis Streams store your events - every user click, payment, order update, whatever. They're basically append-only logs that Redis manages for you. No manual partitioning bullshit, no dealing with consumer group rebalancing nightmares.

Temporal workflows coordinate the business logic. When an order comes in, the workflow ensures payment processing, inventory checks, and shipping notifications all happen in the right order - even if your payment service decides to timeout for 10 minutes. The money transfer example shows this pattern in action.

This diagram shows how Temporal workflows handle compensating actions when things go wrong - critical for event sourcing systems that need to maintain consistency.

I learned this during our Black Friday clusterfuck. Our old system lost a bunch of orders when the payment service shat itself for like 10 minutes. I think it was around 800 orders? Expensive lesson. With Temporal, workflows just pause and resume when services come back up. No lost orders, no manual reconciliation scripts at 3am.

Real Benefits (Not Marketing Bullshit)

Things Don't Stay Broken: Temporal workflows retry failed operations until they work. No more "oh shit, the payment went through but we never sent the email" scenarios.

You Can Actually Debug Problems: Redis Streams keep every event with timestamps. Temporal Web UI shows you exactly where workflows are stuck. No more guessing what went wrong.

Scales Without Hiring a Platform Team: Redis consumer groups handle parallel processing. Temporal workers scale horizontally. We went from 1K to 50K daily orders without touching the architecture.

Event Replay Actually Works: Need to test new business logic? Replay events from last week. Fixed a bug? Reprocess the affected events. This saved my ass when we discovered a pricing calculation bug that affected 12K orders. The continue-as-new pattern is perfect for this.

That's the theory anyway. Now let me show you how to actually build this without losing your sanity.

Start Simple or You'll Hate Yourself

You're building a service that appends events to Redis Streams and uses Temporal workflows to process them reliably. Events go in, business logic happens through workflows, and system state gets reconstructed from the event history - that's the core pattern.

Don't try to build the perfect event-driven architecture on day one. I did that and spent 6 months over-engineering before I had a single working workflow. Start with this:

1. One Redis stream per major entity (orders, payments, users)
2. One Temporal workflow per business process
3. Simple event structure: {type: "order_created", data: {...}, timestamp: "..."}

Redis automatically handles event IDs and ordering. Don't try to be clever with custom IDs unless you enjoy debugging timeline issues at midnight. Here's the XADD documentation when you need the details.

The Patterns That Actually Work in Production

Event sourcing architecture showing how events flow from commands through storage to projections - this is the core pattern we're implementing.

Event-First Everything: Write to Redis BEFORE doing anything else. I learned this when our payment processor charged customers but we never recorded the events because the service crashed between payment and event logging. Fun conversations with customer support. This is the write-ahead log pattern applied to event sourcing.

Idempotency Keys Are Your Friend: Before processing an event, store a processing key in Redis. If it exists, skip the event. This pattern saved us when we discovered duplicate events were processing payments twice. Customers were not amused.

Batch Process or Die: Processing events one at a time is a performance nightmare. We batch 100 events per workflow activity. Reduced our Redis load by 80% and made our AWS bill 40% smaller. Use XREADGROUP with COUNT to batch read events efficiently.

What Goes Wrong and How to Fix It

Consumer Groups Get Stuck: Sometimes Redis consumer groups stop processing new events. The logs look fine, but events pile up. Solution: Check for zombie consumers that died without cleaning up. XPENDING command shows you the stuck messages.

RedisInsight makes debugging stream consumer groups way easier than command line - this view shows exactly where events are stuck.

Temporal Workers Die Mid-Event: When a worker crashes while processing events, the workflow resumes but might reprocess the same event. Always check your idempotency keys or you'll end up with duplicate side effects.

Redis Memory Explodes: Events accumulate fast. A busy e-commerce site generates 500K+ events per day. Set up event archiving or your Redis instance will OOM and take your whole system down. We learned this the expensive way during a flash sale. Use Redis persistence and memory optimization to handle this properly.

Performance Reality Check

Redis Streams are fast - I've pushed 50K events/sec on a decent server before things got sluggish. The "millions per second" marketing claims require perfect conditions and hardware I can't afford.

Redis performance degrades as data size increases - this is why event archiving matters for long-running systems.

Temporal's workflow engine design showing how activities and workflows coordinate - this is what manages your business logic reliably.

Our production setup handles 30K events/sec across 5 workflow workers. Beyond that, you start hitting Temporal's task queue limits and need to think about horizontal scaling. Plan for 20-30K events/sec per Redis instance to stay safe.

These numbers are based on real production experience, not marketing bullshit. Speaking of real experience, let me show you how this approach stacks up against the alternatives.