Support for Parallel Execution with Rafay's Integrated GitOps Pipeline

At Rafay, we are continuously evolving our platform to deliver powerful capabilities that streamline and accelerate the software delivery lifecycle. One such enhancement is the recent update to our GitOps pipeline engine, designed to optimize execution time and flexibility — enabling a better experience for platform teams and developers alike.

Integrated Pipeline for Diverse Use Cases

Rafay provides a tightly integrated pipeline framework that supports a range of common operational use cases, including:

• System Synchronization: Use Git as the single source of truth to orchestrate controller configurations
• Application Deployment: Define and automate your app deployment process directly from version-controlled pipelines
• Approval Workflows: Insert optional approval gates to control when and how specific pipeline stages are triggered, offering an added layer of governance and compliance

This comprehensive design empowers platform teams to standardize delivery patterns while still accommodating organization-specific controls and policies.

From Sequential to Parallel Execution with DAG Support

Historically, Rafay’s GitOps pipeline executed all stages sequentially, regardless of interdependencies. While effective for simpler workflows, this model imposed time constraints for more complex operations.

With our latest update, the pipeline engine now supports Directed Acyclic Graphs (DAGs) — allowing stages to execute in parallel, wherever dependencies allow.

What Does This Look Like in Action?

Consider a pipeline with five stages: A, B, C, D, and E.

• Stages B and C are independent and can run at any time
• Stage D depends on the completion of Stage A
• Stage E depends on the completion of Stage D

With DAG-based execution:

• A, B, and C can run in parallel
• Once A completes, D is triggered
• After D finishes, E is executed

This structure ensures that the pipeline respects stage dependencies while maximizing concurrency where possible, dramatically improving overall efficiency.

DAG Visualization

Stage A

Stage D

Stage E

Stage B

Stage C

Example Execution Timeline

With sequential execution, total time could exceed 58 minutes.

With DAG-based parallelism, the pipeline can complete in approximately 28 minutes, depending on system resources — a significant performance gain.