dagschemas

Dagschemas are formal representations that define the structure, constraints, and semantics of directed acyclic graphs (DAGs) used in data processing and workflow orchestration. A dagschema describes the components of a DAG, including nodes (which represent tasks or operations), edges (the dependencies between nodes), and the attributes attached to each element. It may also specify global meta-information such as version, authorship, and provenance, as well as execution and scheduling constraints.

The primary purpose of a dagschema is to enable validation, interoperability, and automation. By codifying the expected shape of a DAG, dagschemas help tooling verify acyclicity, ensure required fields are present, map resources or parameters to nodes, and enforce constraints such as retry policies or concurrency limits. They also support exchanging DAG definitions between systems or teams and generating executable workflow definitions from a single canonical representation.

Formats for dagschemas vary and there is no single universal standard. Common representations include JSON or YAML schemas that describe allowed node types, edge semantics, and parameter schemas. Some ecosystems use domain-specific languages or embedded schemas within their workflow platforms, allowing dagschemas to be translated into platform-specific DAG definitions.

Typical components of a dagschema include a node catalog with identifiers and types; an edge set describing dependencies; per-node properties such as parameters, resources, and expected outputs; and global constraints or metadata. In practice, dagschemas help governance, traceability, and reproducibility in complex data pipelines and automated workflows, though they can be challenging to maintain across evolving platforms.

See also directed acyclic graph, workflow management, data schema, data lineage.