Polling Is Comfortable. That Doesn't Mean It's Right.

Most engineers reach for polling first. It's easy to reason about, easy to debug, and easy to implement. Set an interval, fire a request, handle the response. Done. The problem is that "easy" and "correct" diverge pretty fast once your data starts changing at any meaningful rate.

What You're Actually Paying For With Polling

Every poll that comes back with no change is wasted work. Wasted compute on the client, wasted compute on the server, wasted bandwidth. At low frequencies, that's fine. At 500ms intervals across thousands of clients? You're generating enormous load to confirm that nothing happened.

The hidden cost isn't just infrastructure though. It's latency. Your data is stale by definition — up to one full interval old, always. If you poll every second, your worst-case staleness is just under a second. For dashboards showing price data, on-chain activity, or live match state, that matters.

Events Change the Equation

Event-driven systems flip the model. Instead of asking "has anything changed?", you get told when something does. A price moves, a block gets confirmed, a bet gets settled — the event fires and the downstream consumer reacts.

For applications where state changes are infrequent, polling is probably fine. You're not missing much. But when state changes constantly — think order books, live blockchain activity, sports scores mid-game — events reduce both latency and infrastructure cost at the same time. Those two things don't usually move together, which is part of why the shift is worth understanding.

The Practical Tradeoffs You Actually Face

Event-driven systems introduce their own complexity. You have to think about:

Ordering. Events can arrive out of order, especially across distributed producers. Polling sidesteps this because you're always fetching the latest state snapshot.

Exactly-once delivery. If your consumer crashes mid-event, do you reprocess? Do you skip? Idempotency becomes your problem to solve.

Fan-out. One event source, many consumers. You need a bus (Kafka, Kinesis, a WebSocket multiplexer) that handles backpressure without dropping events or blowing up slow consumers.

Debuggability. A polling loop is trivially inspectable. An event pipeline has state spread across producers, brokers, and consumers, and failures can be hard to reproduce.

None of these are reasons to avoid events. They're just things you take on when you adopt the model.

Where This Actually Shows Up in Practice

Crypto is a decent case study because it forces the issue. Block times are 12 seconds on Ethereum mainnet but mempool activity is continuous and unpredictable. If you're polling for pending transactions, you're either polling too fast (waste) or too slow (lag). WebSocket subscriptions to an event stream handle this cleanly — you get notified when something lands, and you process exactly that.

The same dynamic shows up in financial market data (quote updates don't arrive on a schedule), live sports (goals and scores are sparse but latency-sensitive when they do happen), and IoT sensor pipelines at any real scale.

When Polling Still Makes Sense

Polling isn't going away. It's the right choice when:

• State changes are rare and latency tolerance is high (syncing a config file, health checks, low-frequency analytics)
• The upstream system doesn't support events or webhooks
• You need a simple fallback for event pipeline failures
• You're building a quick prototype and want to iterate fast

The mistake isn't using polling — it's using it reflexively without thinking about update frequency. The moment you find yourself setting a 100ms polling interval, it's worth asking whether you're just building a worse WebSocket.

The Mental Model Shift

Polling is pull. Events are push. The right question is: who should bear the responsibility of knowing something changed?

If the data source changes unpredictably and frequently, push wins. If it changes rarely and on a schedule, pull is fine. Most architectures eventually end up with both — polling as a fallback or for low-frequency data, events for anything time-sensitive.

The key is making that choice deliberately, not by default.