Temporal Workflow Guidelines

Deterministic workflows in Temporal.io execute the same way every time by replaying past events, allowing recovery from crashes without losing state. Build workflows that avoid non-deterministic operations like random calls or system time checks, use activities for side effects, and leverage Temporal's built-in replay mechanism to resume exactly where execution stopped.

Saga compensation automates rollback of partial failures across multi-step processes by executing compensating transactions in reverse order. Implement it in Temporal by defining compensating activities for each step, catching failures in your workflow, and invoking compensating activities to undo changes when a step fails, ensuring atomicity in distributed systems.

Event-driven activities emit immutable domain events for every state change, generating comprehensive audit trails required for HIPAA and compliance contexts. Structure activities to emit events after successful operations, persist these events durably, and use CQRS patterns to project event data into queryable views for audit and reporting.

Yes. Temporal workflows orchestrate multi-step tenant provisioning—organization creation, DNS configuration, database setup, and invitations—with automatic error recovery. Use saga compensation to roll back partial provisioning, retryable activities with exponential backoff, and workflow versioning with patched() to safely modify workflows without corrupting in-flight executions.

Temporal provides durable execution with automatic retry, crash recovery, and visibility across long-running processes without manual intervention. Unlike message queues that require external state management, Temporal maintains workflow state, handles timeouts and compensation natively, and offers built-in testing and monitoring for complex orchestration.

Use Temporal's patched() function to gate new logic behind a version check, allowing old and new code paths to coexist during transition. Deploy the patched version, let in-flight workflows complete with old logic, then remove the patch condition in a future deploy when all older executions finish, ensuring safe evolution without data loss.