What Is a DAG and How Can It Power a Workflow Engine?

Imagine you want to organize a set of tasks where some depend on others to finish first. For example, you can’t bake a cake before you mix the ingredients, and you can’t mix ingredients before gathering them. How do you keep track of what to do next, and in what order?

This is where a DAG, or Directed Acyclic Graph, comes in handy.

What’s a DAG?

A DAG is a way to represent tasks and their dependencies as a kind of flowchart:

• Directed means the tasks have arrows showing which task comes before another.
• Acyclic means there are no loops — you can’t end up back where you started. So, the order flows forward without getting stuck in circles.

Each task is a node, and the arrows show the order things need to happen.

Using a DAG to Build a Workflow Engine

A workflow engine manages and runs these tasks in the right order. It makes sure tasks only start when all their dependencies (the tasks before them) are done.

By representing your workflow as a DAG, you have a clear map of all tasks and what depends on what.

How Does the Workflow Engine Solve the DAG?

To decide the order to run tasks, the engine needs to traverse or walk through the DAG in a smart way. One common method is called Breadth-First Search (BFS).

What Is Breadth-First Search (BFS)?

Think of BFS like exploring your tasks level by level:

• Start with all tasks that have no dependencies (they can run immediately).
• Then move to tasks that depend only on those finished tasks.
• Then tasks depending on those, and so on.

This means the engine runs all independent tasks first, then their dependent tasks, ensuring everything happens in the right sequence.

How Does BFS Affect Task Execution Order?

By using BFS, tasks are grouped and run in layers based on their dependencies:

• Tasks on the same level don’t depend on each other and can often run in parallel, speeding up the whole process.
• Tasks on the next level wait until all tasks from the previous level are finished.