Graphviz Pipeline DAG
The shared six-task ETL topology as a Graphviz digraph, with node and edge defaults, per-node attribute overrides, a same-rank constraint and C-style comments. Carries the identical graph to the Airflow, Argo, CWL and Mermaid fixtures.
/* The shared orders-ETL topology as a Graphviz digraph. Same six tasks and six edges as the
Airflow, Argo and CWL fixtures in this category, so a converter can be scored across formats. */
digraph orders_etl {
rankdir = LR;
labelloc = "t";
label = "orders_etl";
node [shape = box, style = "rounded,filled", fillcolor = "#eef2ff", fontname = "Helvetica"];
edge [color = "#475569"];
ingest_orders [label = "ingest orders"];
validate_orders [label = "validate orders"];
transform_orders [label = "transform orders"];
load_warehouse [label = "load warehouse"];
refresh_dashboard [label = "refresh dashboard"];
notify_owner [label = "notify owner", shape = ellipse, fillcolor = "#ecfdf5"];
ingest_orders -> validate_orders;
validate_orders -> transform_orders;
transform_orders -> load_warehouse;
transform_orders -> refresh_dashboard;
load_warehouse -> notify_owner;
refresh_dashboard -> notify_owner;
{ rank = same; load_warehouse; refresh_dashboard; }
}
Specifications
- Format
- Graphviz DOT
- Type
- digraph
- Nodes
- 6
- Edges
- 6
- Rankdir
- LR
- Has Rank Constraint
- true
- Has CComments
- true
- Topology
- ingest_orders, validate_orders, transform_orders, load_warehouse, refresh_dashboard, notify_owner
Testing contract
Expected to pass- Scenario
- Parse a DOT digraph including attribute defaults and a rank constraint subgraph
- Expected result
- Six nodes and six edges resolve, node defaults apply to every node except the two with explicit overrides, and the rank constraint is not counted as an edge
What is a .dot file?
A DOT file is a Graphviz graph described in the DOT language (nodes, edges, and attributes as plain text) which Graphviz lays out and renders to an image. It is a standard way to define directed and undirected graphs as code.
How to use this file
Use an example DOT file to test Graphviz rendering, DOT parsing, and DOT-to-SVG/PNG conversion.
How to use this file for testing
“Graphviz Pipeline DAG” is a deterministic Testaroo fixture for Graph data, Data engineering, Conversion testing. Node/edge datasets in GraphML and GEXF (directed and undirected, with attributes and weights), for testing network importers, layout tools, and graph converters.
Documented properties for this file: 6 nodes · 6 edges · Graphviz DOT. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.
Pipeline and infrastructure fixtures are inert configuration: steps reference fictional images and scripts, and nothing here executes. Run your linter, schema validator, migrator, or policy engine against them, and expect the deprecated-syntax and intentionally invalid variants to be rejected.
Generated by generation/pipelines.py. Free for any use, no attribution required, license.
Related files
- dotGraphviz Pipeline Graph with a CycleThe same topology plus one back edge, so the graph is deliberately not a DAG. A renderer must still draw it and a scheduler must reject it, which is why this is a reference fixture rather than a corrupt file.
- mmdMermaid Deployment State DiagramA Mermaid stateDiagram-v2 describing environment promotion with canary and rollback transitions, start and end pseudostates, and a note block. A second Mermaid diagram type, so a renderer is not only tested on flowcharts.
- mmdMermaid Pipeline FlowchartThe shared six-task ETL topology as a Mermaid flowchart, with mixed node shapes, a subgraph, a classDef and %% comments. The Mermaid twin of the Graphviz fixture, for scoring diagram-format converters against one known answer.
- pyAirflow DAG Definition (Operator Style)An Airflow DAG definition in the classic operator style, using only the no-op EmptyOperator so the file describes a topology and performs no work. Carries the six-task ETL graph shared across the Airflow JSON, Argo, Graphviz and Mermaid fixtures.
- pyAirflow TaskFlow API DAGA decorator-based Airflow TaskFlow DAG where dependencies are implied by function calls rather than >> operators: the shape static DAG extractors most often get wrong. Every task returns a literal, so nothing performs I/O.
- jsonAirflow-Shaped Serialized DAG (JSON)The serialized-DAG shape Airflow stores in its metadata database, as standalone JSON: per-task metadata with explicit downstream_task_ids. Carries the same six-task topology as the Python, Argo, Graphviz and Mermaid fixtures.
