Principle:Iterative Dvc Dependency Graph Validation
| Knowledge Sources | |
|---|---|
| Domains | Pipeline_Management, Graph_Theory |
| Last Updated | 2026-02-10 00:00 GMT |
Overview
Dependency graph validation is the practice of verifying that a set of data pipeline stage definitions form a valid directed acyclic graph (DAG) with no cycles, no duplicate outputs, and no overlapping output paths.
Description
Data pipelines consist of stages where each stage declares its inputs (dependencies) and outputs. These declarations implicitly define a directed graph: an edge exists from stage A to stage B when an output of B is consumed as a dependency of A. For the pipeline to be executable in a deterministic order, this graph must be a DAG -- it must contain no cycles. If stage A depends on stage B and stage B depends on stage A, no valid execution order exists.
Beyond acyclicity, the graph must satisfy additional structural constraints specific to data version control. No two stages may declare the same output path, because that would create ambiguity about which stage is responsible for producing a given file. Furthermore, no output path may be a sub-path of another output path (overlapping outputs), because the system cannot determine which stage controls the shared directory subtree. Finally, no stage definition file may reside inside an output directory, as this would create a bootstrapping paradox where the stage metadata is itself managed by the stage it defines.
Validation is performed eagerly -- before any data processing begins -- so that structural errors are caught at definition time rather than at execution time. This fail-fast approach prevents wasted computation and provides clear, actionable error messages.
Usage
Dependency graph validation should be applied whenever:
- A new stage is being added to the repository, to verify that the addition does not introduce cycles or output conflicts.
- A pipeline definition is being modified (e.g., changing dependencies or outputs), to ensure the modified graph remains valid.
- The full repository index is being rebuilt, to detect any corruption or inconsistency in the pipeline structure.
- Before pipeline execution (dvc repro), to guarantee that a valid topological execution order exists.
Theoretical Basis
Directed Acyclic Graphs (DAGs). A DAG is a directed graph G = (V, E) with no directed cycles. The defining property is that there exists at least one topological ordering of the vertices -- a linear sequence where every directed edge (u, v) has u appearing before v. A data pipeline requires this property so that stages can be executed in dependency order.
Cycle detection. Given a directed graph, cycles can be detected in O(V + E) time using depth-first search (DFS). During traversal, if a back edge is encountered -- an edge pointing to a vertex already on the current DFS stack -- a cycle exists. The standard algorithm is:
function detect_cycle(graph):
for each node in graph:
if node not visited:
if dfs_has_cycle(node, visiting_set={}, visited_set={}):
return cycle_edges
return None
function dfs_has_cycle(node, visiting, visited):
visiting.add(node)
for neighbor in graph.successors(node):
if neighbor in visiting:
return True // back edge found, cycle exists
if neighbor not in visited:
if dfs_has_cycle(neighbor, visiting, visited):
return True
visiting.remove(node)
visited.add(node)
return False
Trie-based overlap detection. To detect overlapping output paths efficiently, a trie (prefix tree) keyed on path components is constructed from all output paths. For each output, the trie is queried for both prefixes (ancestor directories that are also outputs) and subtries (descendant paths that are also outputs). This yields O(k) detection per path where k is the path depth, and O(n * k) for the entire set of outputs.
Edge construction via dependency matching. Graph edges are built by matching each stage's dependency paths against the output trie. For a dependency with path P, any output that is a prefix of P (the dependency is inside the output directory) or that P is a prefix of (the output is inside the dependency path) establishes an edge from the dependent stage to the producing stage.