2 communication patterns. 2 completely different failure modes.
One drowns your consumer. The other silently drops events you need.
Push vs Pull is the most underestimated decision in distributed systems.
Here’s the framework I wish someone handed me 5 years ago.
THE 5 DIMENSIONS THAT DECIDE
<ol>
<li>Consumer control (backpressure)</li>
</ol>
<ul>
<li>
Push: producer sets the pace. If your consumer is slow, messages pile up in its inbox or it just falls over.
</li>
<li>
Pull: consumer sets the pace. Reads when ready. Natural backpressure built in.
</li>
</ul>
A webhook hitting your Lambda at 10k/sec during a Black Friday spike will throttle you.
An SQS queue at the same rate just sits there. Your worker drains at its own speed.
<ol>
<li>Latency</li>
</ol>
<ul>
<li>
Push wins when milliseconds matter. The producer fires the instant something changes.
</li>
<li>
Pull wins when “fresh enough” is acceptable. The polling interval becomes the floor of your latency.
</li>
</ul>
Stock trade notifications → push (sub-second).
Daily report aggregations → pull (every 5 min is fine).
<ol>
<li>Failure modes</li>
</ol>
<ul>
<li>
Push fails loudly: retries, dead-letter queues, exponential backoff, thundering herds when the consumer recovers.
</li>
<li>
Pull fails quietly: the consumer just stops reading. No one knows until the lag graph screams.
</li>
</ul>
Both are survivable. Both require different dashboards.
<ol>
<li>Fan-out scaling</li>
</ol>
<ul>
<li>
Push with many consumers = producer burden multiplies. 1 event × 50 webhooks = 50 HTTP calls, 50 retry policies, 50 ways to fail.
</li>
<li>
Pull with many consumers = each one independently drains from a shared log. Kafka does this cleanly. Webhooks do not.
</li>
</ul>
This is why Kafka ate the event bus space from webhook systems the moment fan-out crossed 10 consumers.
<ol>
<li>Infrastructure cost</li>
</ol>
<ul>
<li>
Push systems cost per event (SNS: $0.50 per million, Lambda invocations, outbound HTTP).
</li>
<li>
Pull systems cost per connection (Kafka brokers running 24/7, polling CPU, idle workers).
</li>
</ul>
High volume + few consumers → pull is cheaper.
Low volume + many consumers → push is cheaper.
REAL-WORLD PICKS (AND WHY)
Messaging:
• Webhooks (push) → external integrations. Fine until your partner has a bad day.
• Kafka (pull) → internal event bus. Many consumers, replay needed, high throughput.
Observability:
• StatsD (push) → services fire metrics as events happen. Great for counters.
• Prometheus (pull) → scrapes /metrics every 15s. Self-healing when targets restart. Industry standard for a reason.
Pick PUSH when:
✓ Latency matters more than consumer stability
✓ Events are infrequent but urgent
✓ Fan-out is low (&lt; 10 consumers)
✓ Producer knows every consumer
Pick PULL when:
✓ Consumer can’t be trusted to keep up
✓ Replay/audit is a requirement
✓ Fan-out is high or unpredictable
✓ You want backpressure for free
Hybrid (most production systems): CDC pushes to Kafka, then consumers pull from Kafka. You get real-time source + independent consumer pacing.
Pull isn’t slower. It’s just honest about who’s in control.
<img src="https://media.daily.dev/image/upload/s--X_H3_TGP--/f_auto/v1776714340/ugc/content_c293bf9d-fe64-4fcf-9bff-915c9d256712?_a=BAMAMiWQ0" alt="push-vs-poll.png">
<img src="https://media.daily.dev/image/upload/s--BdGixdi1--/f_auto/v1776714342/ugc/content_e714eadf-3ca6-44d3-8d9e-1fc9921221bc?_a=BAMAMiWQ0" alt="pull.png">
<img src="https://media.daily.dev/image/upload/s--QeYSFSAH--/f_auto/v1776714346/ugc/content_33d54706-c713-45dd-b328-4d3f44a5bea9?_a=BAMAMiWQ0" alt="push.png">

Joud Awad

A practical framework comparing push vs pull communication patterns in distributed systems across five dimensions: consumer control (backpressure), latency, failure modes, fan-out scaling, and infrastructure cost. Push gives producers control and low latency but risks overwhelming consumers and multiplying retry complexity at scale. Pull gives consumers control with natural backpressure but can fail silently if consumers stop reading. Real-world examples include webhooks vs Kafka for event buses, and StatsD vs Prometheus for observability. Decision guidelines are provided for each pattern, with a note that most production systems use a hybrid approach (CDC pushing to Kafka, consumers pulling from Kafka).

Push VS Poll