Exploring Alternatives to graphlib.TopologicalSorter.done() for Python's Topological Sort

Data Types and Topological Sorting

• Topological Sorting
  This is a linear ordering of nodes in a Directed Acyclic Graph (DAG) such that for every directed edge from node u to node v, node u comes before node v in the ordering. It ensures tasks are executed in the correct order based on their dependencies.

• Data Types
  Python offers various built-in data types like lists, dictionaries, and sets. When using graphlib.TopologicalSorter, these data types are commonly used to represent the graph structure.

  • Lists
    Can be used to store nodes in a specific order, but not ideal for representing dependencies.
  • Dictionaries
    More suitable for representing a graph, where keys are nodes and values are lists of predecessor nodes.
  • Sets
    Not directly used for graph representation, but can be helpful for checking if a node exists in the graph.

graphlib.TopologicalSorter.done()

This method is part of the graphlib.TopologicalSorter class introduced in Python 3.9. It's used in conjunction with other methods to perform a topological sort on a graph:

1. Creating the TopologicalSorter
   

   • Initialize a TopologicalSorter object, optionally passing a dictionary representing the graph structure (nodes and their predecessors).

   from graphlib import TopologicalSorter
   
   graph = {"A": [], "B": ["A"], "C": ["B", "A"]}  # Example graph
   ts = TopologicalSorter(graph)  # Or ts = TopologicalSorter() for manual adding
   

2. Adding Nodes (Optional)
   

   • If you didn't provide a graph dictionary in the constructor, you can manually add nodes and their predecessors using ts.add(node, *predecessors).

3. ts.is_active()
   

   • Checks if there are still nodes to be processed in the sort.

4. ts.get_ready()
   

   • Returns a tuple containing all the nodes that are currently ready to be processed because all their predecessors have already been processed.

5. ts.done(*nodes)
   

   • This is where done() comes in. It signals that the processing of the nodes passed as a tuple (*nodes) is complete. This allows the TopologicalSorter to update its internal state and identify new nodes that are ready for processing in the next iteration of the loop.

Iterative Topological Sorting

while ts.is_active():
    ready_nodes = ts.get_ready()
    # Process the ready nodes (your custom logic)
    ts.done(*ready_nodes)

• Finally, done() informs the TopologicalSorter that these nodes are finished, allowing it to progress the sorting process.
• You perform your desired processing on these ready nodes.
• In each iteration, get_ready() retrieves nodes without unprocessed predecessors.
• This loop iterates until there are no more active nodes.

Understanding done() with Data Types

• done() updates the internal state of the TopologicalSorter, which likely involves data structures like sets or dictionaries to keep track of processed nodes and their remaining dependencies.
• *nodes in ts.done(*nodes) is typically a tuple containing strings (node names) or other hashable data types that can be used as keys in the internal graph structure.

Key Points

• The specific data types used for nodes depend on your chosen graph representation.
• It interacts with internal data structures of the TopologicalSorter to manage node processing.
• done() is used within an iterative process to complete the topological sorting.

---

from graphlib import TopologicalSorter

# Sample graph represented as a dictionary (nodes as keys, predecessors as values)
graph = {
    "compile": [],
    "test": ["compile"],
    "deploy": ["test", "build"],
    "build": ["compile"],
}

# Create a TopologicalSorter
ts = TopologicalSorter(graph)

# Iterative topological sort
while ts.is_active():
    ready_nodes = ts.get_ready()
    print(f"Ready nodes: {ready_nodes}")  # Display ready nodes with their data type (tuple)

    # Simulate processing (replace with your actual task logic)
    for node in ready_nodes:
        print(f"Processing: {node}")
        # ... perform actions on the node (e.g., compiling, testing, deploying)

    # Mark nodes as done
    ts.done(*ready_nodes)

print("\nTopological order:")
for node in ts.static_order():
    print(node)

1. We define a sample graph using a dictionary. Nodes are strings (suitable for this example), and predecessors are also lists of strings.
2. We create a TopologicalSorter instance, passing the graph dictionary.
3. The loop iterates until there are no more active nodes.
4. In each iteration, get_ready() retrieves a tuple of ready nodes.
5. We print the ready nodes, highlighting their data type (tuple).
6. The # Simulate processing section is a placeholder for your actual task logic specific to each node (e.g., compiling, testing, deploying).
7. After processing, we call ts.done(*ready_nodes) to mark these nodes as finished.
8. Finally, we print the topological order using ts.static_order(), which provides a convenient way to get the sorted nodes after the processing loop is complete.

---

Custom Tracking

• If you have a simple graph and only need basic control over topological sorting, you can track processed nodes manually:
  • Maintain a set to store processed nodes.
  • After processing a node from ts.get_ready(), add it to the processed set.
  • Modify your processing logic to check if a node has already been processed before performing an action.

This approach is less efficient compared to done() for larger graphs and might become error-prone for complex scenarios.

Modify Processing Logic

• This might work for specific algorithms and requires careful design.
• If your processing logic inherently ensures a single pass through each node (e.g., only necessary dependencies are processed before reaching the current node), you might not need done() explicitly.

Custom Topological Sort Implementation

• While potentially more flexible, it would require more coding effort and testing.
• This would involve tracking dependencies and processing nodes in the correct order.
• For more control or using different sorting algorithms, you could implement your own topological sort function.

Third-Party Libraries

• Consider the complexity of your graph and the features you need before switching libraries.
• These libraries might provide different functionalities or require different approaches to signaling completion.
• Libraries like networkx (a comprehensive network analysis library) offer alternative implementations for topological sorting.

• If you have a specific use case that requires more control or different algorithms, explore the alternative options and their trade-offs.
• If you're working with basic graphs in Python 3.9 or above and need efficient topological sorting, done() is the recommended approach within the graphlib module.