TL;DR
Recent discussions suggest that building durable workflows directly on Postgres can simplify architecture by removing external orchestrators. This approach leverages Postgres’s scalability, observability, and reliability features, making workflows more straightforward and robust.
A new approach to building durable workflows proposes using Postgres itself as the core orchestrator, eliminating the need for external workflow management systems. This idea, supported by recent technical discussions, highlights the potential for simpler, more reliable, and scalable workflow execution by leveraging Postgres’s native capabilities. This development could significantly impact how organizations implement reliable workflows, especially those already using Postgres at scale.
The core concept is to store all workflow state and checkpoints directly in Postgres tables, with application servers polling and updating these tables to manage workflow progress. This removes the traditional need for a separate orchestrator, which typically coordinates workflow steps and maintains state externally.
In this design, multiple application servers can work concurrently, using Postgres’s locking and integrity constraints to ensure workflows are executed exactly once and to prevent duplicate processing. If a server crashes, another server can seamlessly recover and continue from the last checkpoint, thanks to the durable storage provided by Postgres.
Supporters argue that this approach simplifies system architecture, reduces latency, and leverages Postgres’s proven scalability and high availability features. Postgres’s ability to handle tens of thousands of workflows per second and its support for replication and failover make it a compelling choice for robust workflow management.
Why It Matters
This approach matters because it could drastically reduce the complexity of building reliable, scalable workflows. By using a familiar, well-understood database as the backbone for workflow orchestration, organizations can avoid the operational overhead and potential failure points associated with external orchestrators like Temporal or Airflow. It also enhances observability, as workflow states are directly queryable via SQL, enabling real-time monitoring and analytics.
Furthermore, this method aligns with existing infrastructure investments, especially for teams already running Postgres at scale. It could lead to more resilient systems, easier security management, and cost savings by reducing the number of moving parts in a workflow system.
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Background
Traditional durable workflow systems rely on external orchestrators that manage state and execution flow, often introducing complexity and additional failure points. Recent discussions, including posts on Hacker News and technical blogs, have questioned whether such external systems are necessary when the core idea—checkpointing in a database—is simple and effective. Postgres’s maturity, scalability, and rich ecosystem make it an attractive foundation for this approach, especially as organizations seek to streamline architectures.
Historically, systems like Temporal, Airflow, and AWS Step Functions have been used, but they often require dedicated infrastructure and complex coordination. The recent proposals suggest that embedding workflow state directly into Postgres can simplify this landscape, leveraging the database’s transactional guarantees and query capabilities.
“Replacing a central orchestrator with Postgres makes durable workflows fundamentally simpler, addressing scalability, availability, and observability through well-understood database solutions.”
— Postgres advocate on Hacker News
“Using Postgres as the backbone for workflow state management leverages decades of engineering and research, making it a reliable choice for high-scale, mission-critical systems.”
— Workflow systems researcher
PostgreSQL workflow management tools
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What Remains Unclear
It is still unclear how well this approach performs under extremely high workloads or in highly distributed, multi-region deployments. Further empirical testing and real-world case studies are needed to validate scalability and operational robustness at scale.
PostgreSQL replication and failover solutions
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What’s Next
Next steps include pilot implementations and benchmarking to compare Postgres-based workflows against traditional external orchestrators. Industry adoption may grow as more organizations test this approach in production environments, and tools or frameworks supporting this model could emerge.
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Key Questions
Can Postgres handle large-scale workflows effectively?
While Postgres can scale vertically and horizontally with sharding or distributed variants, real-world performance at very high throughput levels remains to be validated through testing and case studies.
How does this approach improve observability?
Workflow states and checkpoints are stored directly in Postgres tables, allowing complex queries and real-time monitoring using standard SQL, which simplifies observability compared to external systems.
Is this approach suitable for all types of workflows?
It is most suitable for workflows that can be expressed within database transactions and do not require extremely low latency or specialized orchestration features. More complex workflows may still benefit from dedicated orchestrators.
What are potential drawbacks of using Postgres as the orchestrator?
Potential challenges include handling very high concurrency workloads, ensuring performance at scale, and adapting to specific workflow complexities that may require additional tooling.
Source: Hacker News
