Order tasks with dependency constraints using graphlib.TopologicalSorter.
What You Will Learn
TopologicalSorter performs dependency ordering for a directed acyclic graph. You provide each node and the tasks it depends on. A cycle raises CycleError on prepare() or static_order(); all dependencies must finish before their dependents become ready.
For concurrent execution, use get_ready(), done(), and is_active() after prepare().
Time complexity assumes a specific input structure. Include setup work such as sorting or building a lookup table when comparing complete solutions.
Step-by-Step Example
The example below makes the behavior visible with small input values that you can change safely.
from graphlib import TopologicalSorter
dependencies = {'test': {'build'}, 'deploy': {'test'}, 'build': set()}
print(list(TopologicalSorter(dependencies).static_order()))How the Example Works
- The imports provide the standard-library tools used in the demonstration: graphlib.
- The variable dependencies holds the result of {'test': {'build'}, 'deploy': {'test'}, 'build': set()}. Later lines use that value or inspect how it changes.
- The print() calls make the return values or state visible so you can compare them with the explanation and check your prediction.
For the chosen input, the expression list(TopologicalSorter(dependencies).static_order()) prints ['build', 'test', 'deploy']. Compare that result with the rule above before you change the argument.
Expected output from the example:
['build', 'test', 'deploy']When You Would Use This
This technique matters when a program handles larger queues, sorted records, or repeated lookups. Choose the data structure based on actual operations and the preconditions the algorithm requires. Verify boundary inputs before optimizing for speed.
Practice: Modify the Code
Add a documentation step that depends on build but not on test.
Verify the changed output against the behavior described above, including the relevant invalid or boundary case.
For complete parameters and version details, consult the official Python documentation.
Common Beginner Mistakes
- Reversing the dependency mapping and scheduling a dependent task before its prerequisite.
- Overlooking this documented constraint: a cycle raises CycleError on prepare() or static_order(); all dependencies must finish before their dependents become ready.
Key Takeaways
- Order tasks with dependency constraints using graphlib.TopologicalSorter.
- TopologicalSorter performs dependency ordering for a directed acyclic graph.
- For concurrent execution, use get_ready(), done(), and is_active() after prepare().