The difference between webhooks and polling is easy to describe. With a webhook, a source sends a request when something happens. With polling, a consumer asks the source for updates. Choosing between them is harder because the real decision includes delivery failures, missed history, rate limits, and how quickly a user needs to see a change.
Begin with one job. Perhaps a document is being prepared by an outside service, or a subscription record needs to reach another application. Write down what the consumer needs to learn and how late that information can arrive before someone is affected. That gives the transport decision a purpose beyond choosing the more fashionable mechanism.
Neither approach removes the need for recovery. A webhook receiver can be unavailable, and a polling worker can stop running. The useful question is how the system discovers and repairs the gap after either event.
Understand what the source actually provides
Some services expose detailed event streams; others send only a notification that an object changed. A webhook might contain the new state, an object identifier, or a partial payload that requires another request. Read the provider’s contract before assuming the event contains everything needed to act.
Polling endpoints vary too. One may return records changed after a cursor, while another only lists current objects. The first can support incremental retrieval. The second may require comparison with locally stored state. A design based on an imaginary change feed will fail even if the polling code itself is sound.
Record the provider’s retention window, pagination rules, and delivery policy. If the service does not document a behavior, treat it as an unresolved question rather than filling the gap with expectations from another vendor. The source’s available interface can decide more of the architecture than the team’s initial preference.
Use webhooks when prompt notification matters
Webhooks can be a good fit when the source supports them and the consumer benefits from learning about an event soon after it occurs. A customer-facing status update may fit that pattern. The consumer avoids asking repeatedly when there is nothing new to report.
The receiver still needs an accessible endpoint, validation, and a way to hand work off for processing. GitHub’s webhook best practices provide one concrete provider’s guidance on secrets and delivery handling. Other providers have their own requirements, so apply the documentation for the source being integrated.
Keep the receipt of the event separate from the business work it triggers. A receiver that waits for a long downstream task before responding may exceed the sender’s timeout. A durable handoff can let the receiver acknowledge receipt promptly, but only if that handoff actually succeeds. Returning success before preserving the event risks losing work during a failure.
Use polling when the question is naturally periodic
Polling can be a reasonable choice for a process whose useful interval is measured in minutes or hours, especially when the source offers a reliable change cursor. A nightly reconciliation job does not necessarily need a public callback endpoint. It needs a repeatable way to fetch the right records and continue after interruption.
Choose the interval from the workload’s needs and the source’s limits. An aggressive interval may create many empty responses. A slow interval may leave a user waiting longer than the product can tolerate. Write down that tradeoff so a future maintainer understands why the schedule exists.
Store progress carefully. If a worker advances its cursor before processing the returned records, a crash can skip work. If it advances only after processing, a crash may cause records to be seen again. Design the consumer to handle that repetition, and test the boundary between processing a page and recording progress.
Compare a document-processing example
Suppose an application submits a document for conversion. The immediate request returns an identifier, and the conversion may take longer than an ordinary web request. The application needs to show the final result when it is ready. This is a hypothetical example, not a benchmark for a particular service.
With polling, the application periodically requests the job status until it reaches a terminal state. It should stop after a defined limit or move into a recoverable waiting state. With webhooks, the service notifies an endpoint when the job changes, and the application records the update. The callback still needs to be matched to the correct document.
In either model, a 202 response to the original request does not prove completion. The user interface should distinguish submitted, processing, completed, and failed states according to the service’s actual contract. Transport choice should not blur that lifecycle.
Plan for duplicates before they arrive
A webhook event may be delivered more than once depending on the provider’s delivery behavior. A polling worker may see the same record after a restart or because an overlap window is intentional. The consumer should identify the logical operation and avoid repeating harmful side effects merely because it receives another notification.
That does not mean discarding every repeated object identifier. The same object can change many times. Use the source’s event identifier, version, or other documented ordering information where available. Decide what a duplicate means for the specific action being performed.
For example, updating a local status field and sending a new customer email have different consequences when repeated. A system may safely apply the status again while needing a separate record that the email action has already been taken. Keep those decisions explicit in the consumer design.
Treat ordering as a separate question
Do not assume that arrival order is business order. Delivery paths, retries, and concurrent workers can change the order in which updates are processed. A late notification about an earlier state should not automatically overwrite a newer state.
Where the source exposes a version or sequence, document how it is used. Where it only exposes current state, a notification may be best treated as a reason to fetch the current object rather than as an instruction to apply an old payload. That adds a request but can simplify the interpretation of the result.
Time-based comparisons require care too. Timestamps can have limited precision, and local clocks are not necessarily the source of truth. Prefer the provider’s documented change-tracking mechanism over a hand-built assumption about which timestamp must be newest.
Add reconciliation where missing an event matters
A combined design can use webhooks for prompt updates and periodic polling to check for gaps. This adds operational work, so it should have a reason. It can be useful when missed changes would otherwise remain invisible for a long time.
Define what reconciliation compares. It might inspect recently changed records, verify unfinished jobs, or compare a selected set of authoritative fields. A vague scheduled task called “sync everything” is difficult to evaluate and can become expensive as the dataset grows.
Test an outage deliberately in a controlled environment. Pause the receiver or worker, create a few changes, then restore it. Check which changes are recovered automatically and which need another route. That exercise turns recovery from a diagram into observed behavior.
Make the choice with a small worksheet
For one workload, record the acceptable delay, source interfaces, replay window, duplicate handling, ordering information, and operator responsible for recovery. Add the expected volume of checks or events as a planning assumption, clearly separate from measured results.
Choose the simplest model that meets those requirements and can be operated by the team. If the source changes or the user experience becomes more time-sensitive, revisit the worksheet. The next step is to map one real job to both models and test the failure that is most likely to interrupt it.