Scalable event collection: Handling millions

Picture this: your product just went viral on TikTok, and suddenly you're drowning in user events - clicks, views, purchases, you name it. Your monitoring dashboard looks like a Christmas tree, all red alerts and screaming metrics.

If you haven't built your event collection system to scale, you're about to have a very bad week. But here's the thing - scalability isn't just about handling viral moments. It's about building a foundation that grows with your business without constantly firefighting infrastructure issues.

The importance of scalability in event collection

Let's be real - event collection at scale is hard. When you're dealing with thousands of events per second, every inefficiency gets magnified. Your perfectly adequate system that handled 10,000 daily events? It'll completely fall apart at 10 million.

The team at Google learned this the hard way, discovering that without proper scalability, systems don't just slow down - they catastrophically fail. Data gets dropped, queries timeout, and before you know it, you're explaining to leadership why last week's analytics are missing. Not fun.

What makes this particularly challenging is that event data has this annoying habit of being bursty. You'll cruise along at baseline traffic, then boom - Black Friday hits, or your marketing campaign goes viral, and suddenly you're processing 100x your normal volume. Canva deals with billions of these events, and their engineering team will tell you that maintaining data quality while processing at that scale requires serious architectural planning.

The smart approach? Build for tomorrow's scale today. This means investing in:

• Storage mechanisms that don't choke on high throughput

• Processing pipelines that handle spikes gracefully

• Infrastructure that scales automatically (not manually at 3 AM)

Teams that nail this use techniques like sharding their data across multiple servers, implementing aggressive caching strategies, and - crucially - making everything asynchronous. Because when you're handling millions of events, synchronous processing is basically asking for trouble.

Architectural strategies for scaling event collection systems

Scaling approaches

Alright, let's talk scaling strategies. You've got three main options, and picking the wrong one will haunt you later.

Vertical scaling is the "throw money at it" approach - just buy bigger servers. It works great until you hit the ceiling of what money can buy. Plus, you're putting all your eggs in one very expensive basket.

Horizontal scaling is where things get interesting. Instead of one beefy server, you spread the load across many smaller ones. Google's distributed systems team swears by this approach because there's virtually no upper limit. Need more capacity? Just add more servers.

But the real magic happens with diagonal scaling - using both strategies together. Start with reasonably powerful machines, then scale out as needed. It's not rocket science, but you'd be surprised how many teams pick one approach and stubbornly stick with it.

Enhancing performance

Once you've figured out your scaling strategy, it's time to squeeze every bit of performance from your system.

Load balancing is non-negotiable when you're running multiple servers. Without it, you'll have some servers dying under load while others sit idle. The Google Cloud team found that proper load distribution can improve overall system capacity by 40-60%.

Here's what actually moves the needle:

• Caching: Keep hot data in memory - your database will thank you

• Database sharding: Split your data across servers based on logical boundaries

• Connection pooling: Reuse database connections instead of creating new ones

The trick with caching is knowing what to cache. User session data? Absolutely. Historical analytics that get queried constantly? You bet. Real-time event streams? Probably not.

Asynchronous processing and event sourcing

This is where things get fun (if you're into distributed systems, anyway). Asynchronous processing changes everything about how you handle events.

Instead of processing each event immediately, you dump them into a queue and process them when you can. This decoupling means your collection system stays responsive even when your processing pipeline is backed up. Netflix's engineering team credits async processing as the key to handling their massive scale.

Event sourcing takes this a step further. Rather than storing the current state, you store every event that led to that state. Think of it as keeping all your receipts instead of just your bank balance. The team behind various microservice architectures found this approach provides:

• Perfect audit trails

• The ability to replay events

• Easy debugging (just replay the events that caused the issue)

The downside? Event stores can get massive. You're storing everything, forever. But with modern storage costs, it's usually worth the trade-off for the flexibility you gain.

Leveraging cloud services and containerization

Look, I get it - not everyone wants to manage their own infrastructure. And honestly? You probably shouldn't.

Cloud services have gotten really good at the auto-scaling game. Set up your rules, and AWS or Google Cloud will spin up new instances when traffic spikes and shut them down when things calm down. No more paying for idle servers or scrambling during traffic surges.

Containerization with Docker and Kubernetes adds another layer of flexibility:

• Deploy anywhere without worrying about dependencies

• Scale individual services independently

• Roll back deployments in seconds if something breaks

And if you really want to go hands-off, serverless computing handles all the scaling for you. You just write your event processing logic, and platforms like AWS Lambda handle the rest. Just watch your bill - serverless can get expensive at scale if you're not careful.

Real-world techniques for efficient event processing

Theory is great, but let's talk about what actually works in production.

Event sourcing isn't just an architectural pattern - it's a superpower for debugging production issues. When something goes wrong (and it will), being able to replay the exact sequence of events that caused the problem is invaluable. The team at Statsig leverages this approach to handle their trillion events per day, and it's saved them countless debugging hours.

The key is storing events immutably. Once an event is written, it never changes. This gives you a reliable audit trail and lets you reconstruct system state at any point in time. Sure, it uses more storage, but storage is cheap. Engineering time isn't.

For processing efficiency, OLAP databases are game-changers. These aren't your typical transactional databases - they're built specifically for analytics workloads. What does this mean practically?

• Complex aggregations that would kill a regular database run in seconds

• Deduplication happens automatically during ingestion

• You can query billions of events without breaking a sweat

Canva's engineering team processes billions of events by combining all these techniques. They use event sourcing for reliability, async processing for scale, and OLAP databases for analytics. The result? A system that handles massive scale while staying maintainable.

The secret sauce is keeping things simple. Don't try to implement every pattern at once. Start with async processing, add event sourcing when you need better debugging, and migrate to OLAP databases when your analytics queries start timing out.

Optimizing costs and performance in high-volume event systems

Here's an uncomfortable truth: most teams overspend on event collection by 3-5x. The good news? Fixing this is usually straightforward.

Start with the low-hanging fruit - spot instances. If your event processing can handle occasional interruptions (and with proper async design, it should), spot instances cost 70-90% less than on-demand instances. Statsig runs much of their infrastructure on spots, which is part of how they keep costs reasonable while streaming those trillion daily events.

Data compression is another easy win. The Canva team found that using zstd compression reduced their storage and bandwidth costs by 60% with minimal CPU overhead. That's real money back in your pocket.

But the biggest cost saver? Deduplication and batching:

• Eliminate duplicate events before processing (users love double-clicking)

• Batch events together to reduce API calls and network overhead

• Process in bulk to minimize per-event costs

The teams at Statsig and others have shown that proper batching can improve throughput by 10-100x while dramatically reducing costs. Instead of processing each event individually, collect them for a few seconds and process in batches. Your infrastructure (and CFO) will thank you.

Don't forget to monitor everything. Set up alerts for:

• CPU and memory utilization

• Queue depths

• Processing lag

• Cost anomalies

The best optimization is preventing problems before they happen. When you see utilization creeping up, add capacity before things break. When costs spike unexpectedly, investigate immediately - it's usually a bug or misconfiguration that's easy to fix if caught early.

Closing thoughts

Building scalable event collection isn't about implementing every possible optimization or using the fanciest tech stack. It's about making smart architectural choices that give you room to grow without painting yourself into a corner.

Start simple - get async processing working, add some basic monitoring, and scale horizontally when needed. As you grow, layer in event sourcing, OLAP databases, and cloud auto-scaling. The teams handling billions or even trillions of events didn't start there - they evolved their systems as their scale demanded it.

Want to dive deeper? Check out the engineering blogs from Google, Netflix, and Canva - they're goldmines of practical scaling wisdom. Or if you're looking for a ready-made solution that handles the heavy lifting, platforms like Statsig have already solved these problems at scale.

Hope you find this useful! And remember - that 3 AM infrastructure fire you're worried about? With the right architecture, it doesn't have to happen.