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Directed Acyclic Graph (DAG)

All Conductor workflows are directed acyclic graphs (DAGs). A directed acyclic graph (DAG) is a set of vertices where the connections are unidirectional without any repetition. DAG workflows can move only in one direction and cannot redo a step (or series of steps).

Here is a breakdown of what DAG means:

  • Graph

For DAGs, a graph refers to "a collection of vertices (or points) and edges (or lines) that indicate connections between the vertices."

a regular graph (source: wikipedia)

Imagine each vertex in the above graph as a microservice. The lines represent a dependency relation between each microservice. However, this graph is not a directed graph, as there is no direction given to each dependency.

  • Directed

In a directed graph, each connection (edge) has a direction. For example, this graph is directed:

directed graph

Each line has a direction. In the example above, Point N can proceed directly to B, but B cannot proceed directly to N.

  • Acyclic

Acyclic means without circular or cyclic paths. The example shown above contains directed cyclic graphs, such as A -> B -> D -> A. In contrast, a directed acyclic graph can only begin at one point and end at a different point (A -> B -> D).

Workflows as DAGs​

A Conductor workflow is a series of tasks that can connect in only a specific direction and cannot circle back, it is a directed acyclic graph.

Conductor Dag

The order and structure of tasks are defined in the tasks array in the workflow definition JSON (or generated by an SDK).

Can a workflow contain loops and still be a DAG?​

Yes. A workflow can include Do While tasks and still be a DAG because the loop is a simplified way to represent repeated instances of the same tasks. Each iteration creates a new task instance, so the execution path remains acyclic.