A webhook pushes a notification when an event occurs, whereas polling asks for changes on a schedule. Either can support reliable integration when its contract and failure modes are designed. Selection should not be based on which sounds newer, but on source capability, timing needs, and operating cost.
How do the two patterns work?
With a webhook, the source calls a receiver when an event appears. The receiver must be available, validate the sender, and safely process redelivery. With polling, the consumer requests data since a checkpoint. It controls timing and capacity but sends requests when nothing changed and adds delay according to the interval.
These patterns differ from the synchronous-versus-asynchronous choice for the wider process. Read synchronous or asynchronous integration to separate response needs from the way changes are discovered.
What are the main comparison factors?
| Factor | Webhook | Polling |
|---|---|---|
| Latency | Usually near event time | Follows the interval |
| Idle load | Low | Requests continue |
| Consumer control | Must accept pushes | Controls the pace |
| Gap recovery | Needs redelivery or reconciliation | A checkpoint can reread |
| Source requirement | Can send events | Can query changes |
What failures are typical for webhooks?
The receiver may be offline, a success response may be lost, or the source may deliver the same event more than once. Order can change during redelivery. Use event identity, idempotent handling, contract validation, and responses that do not claim a business outcome before processing has truly completed.
A webhook request is not trusted merely because it reached the expected location. Authentication, payload validation, size limits, and observability remain necessary. Connect failure handling with the guide explaining why retries do not solve every integration error.
What failures are typical for polling?
A wrong checkpoint can miss data or reread too much. Timestamp filters are fragile when precision, time zones, or equal values are ignored. Pagination that changes during reading can duplicate or omit records. Aggressive intervals can also overload the source.
Use a stable watermark, a unique tiebreaker, controlled overlap, and deduplication. Advance progress only after a batch is safely processed. For large results, understand cursor versus offset pagination before using page numbers as a checkpoint.
When is a hybrid pattern stronger?
Many systems use a webhook as a timely signal and then retrieve detail through an API. Another pattern uses webhooks for the primary path and periodic reconciliation polling to find missed events. This combination reduces latency without treating notification as the sole source of truth.
Document whether an event contains a snapshot, a change, or merely a hint to read the source. That choice determines stale-data and ordering risk.
What checklist determines the choice?
- The source truly supports a webhook or change query.
- The business latency boundary is stated, not “real time” without a measure.
- Identity, duplicates, ordering, and checkpoints are designed.
- Source and consumer capacity have been estimated.
- Reconciliation exists to detect gaps.
- Failure modes are tested with representative data.
A visual platform such as NitroNet can help display the selected pattern, but this article claims no specific webhook or polling implementation. Begin with the contract and data lineage, then choose the pattern whose correctness is easiest to demonstrate.
Build a small proof of concept for one entity using webhooks and polling. Send identical changes and measure delay, empty requests, gaps, and recovery effort. Real comparison prevents decisions based on assumptions that push is always cheap or polling is always slow.
- Disable the receiver while five events are created and measure how each pattern discovers every change.
- Deliver one event twice and verify the destination produces no duplicate business effect.
- Modify a record during polling pagination to find omitted or repeated records.
- Remove one webhook delivery and see whether reconciliation finds the gap before a user reports it.
- Increase volume gradually and observe source load, consumer load, and observability cost.
- Test wrong credentials, malformed payloads, and invalid source identity as permanent failures.
- Measure data age at the destination instead of only latency for successful requests.
- Estimate operator work after checkpoint damage or a webhook-delivery investigation.
The result may support a hybrid: webhooks for timely signals and polling for reconciliation. Establish one source of truth and correction rule so the paths do not compete. Select from demonstrated freshness and recovery boundaries rather than request counts under ideal conditions.