Database-Native Approach to Durable Workflows: Using Postgres as Central Orchestrator

Durable workflows represent a foundational pattern for building reliable distributed systems by regularly checkpointing program progress to persistent storage, enabling recovery from failures without losing work. Traditional implementations rely on external orchestration platforms such as Temporal, Airflow, and AWS Step Functions, which use a central coordinator to dispatch workflow steps to workers and manage state transitions across a distributed system.

A simpler architectural alternative eliminates the need for a separate orchestration server by leveraging the database itself as the coordination mechanism. In this model, application servers communicate directly with a database to execute workflows rather than routing through an external orchestrator. Workflow execution begins when a client creates an entry in a database table; application servers then poll this table to dequeue and execute workflows. As each step completes, servers checkpoint the output directly to the database. This approach reduces operational complexity by using database-native mechanisms such as locking clauses to ensure each workflow is processed by exactly one worker and database constraints to detect and prevent duplicate execution.

Postgres emerges as an ideal platform for implementing database-native durable workflows due to its widespread adoption, proven scalability, and rich feature set. The system achieves high availability because workers are fungible and can freely recover each other's state, with availability constrained only by the underlying database. Scalability is similarly determined by database performance rather than orchestrator capacity; a single Postgres instance can handle tens of thousands of workflows per second, with further expansion possible through established scaling solutions.

This architecture addresses traditionally difficult distributed systems problems—scalability, availability, observability, and security—using well-understood, production-tested Postgres capabilities rather than specialized orchestration infrastructure. By eliminating the separate orchestrator entirely, the approach reduces the number of moving parts, decreases operational overhead, and allows teams to rely on existing database expertise and tooling rather than learning specialized workflow platforms.