How do serverless platforms handle scheduled tasks?

Serverless platforms handle scheduled tasks by using built-in scheduling services that trigger functions or workflows at specified times. These services act like managed cron jobs, eliminating the need for developers to set up and maintain dedicated servers or scheduling infrastructure. For example, AWS Lambda can be triggered by Amazon EventBridge using cron-like expressions, while Azure Functions uses timer triggers configured with CRON syntax. The serverless platform automatically manages the scheduling mechanism, ensuring tasks run reliably without manual intervention. This approach abstracts away infrastructure concerns, letting developers focus solely on writing the task logic.

Specific implementations vary by provider. On AWS, EventBridge allows defining rules with cron expressions (e.g., 0 10 * * ? * to run daily at 10 AM UTC) to invoke a Lambda function. Azure Functions uses a TimerTrigger binding in code, where the schedule is declared in the function configuration (e.g., "0 */5 * * * *" for every 5 minutes). Google Cloud combines Cloud Scheduler (to send HTTP requests) with Cloud Functions or Cloud Run endpoints. These integrations are typically configured via infrastructure-as-code tools like Terraform, CLI commands, or the provider’s UI. The platforms handle time zone adjustments, retries for transient failures, and event logging transparently.

Key considerations include execution limits and error handling. Serverless functions have timeout thresholds (e.g., AWS Lambda’s 15-minute maximum), so long-running tasks may require breaking into smaller steps. Providers often retry failed invocations automatically—AWS retries twice, while Azure offers configurable retry policies. Developers must design tasks to be idempotent to avoid duplicate side effects from retries. Monitoring is done through the platform’s native tools (e.g., AWS CloudWatch, Azure Monitor) to track invocation logs and errors. Cold starts can delay initial execution, which may affect time-sensitive tasks. By understanding these constraints, developers can design reliable scheduled workflows without managing underlying infrastructure.