[Python] How can I efficiently parallelize and optimize a large-scale graph traversal...

I'm working on Python project that involves processing a very large graph - it has millions of nodes and edges. The goal is to perform a breadth-first search (BFS) or depth-first search (DFS) from a given start node to compute shortest paths or reachability.

Here's the challenge :

• The graph is too large to fit comfortably into memory if stored natively.
• The traversal needs to be fast, ideally leveraging multiple CPU cores.
• I want to avoid race conditions and ensure thread-safe updates to shared data structures.
• The graph data is stored as adjacency lists in files, and I want to process it efficiently without loading the entire graph at once.

Currently, I have a basic BFS implementation using a Python dictionary for adjacency, but it runs slowly and hits memory limits:

from collections import deque

def bfs(graph, start):
visited = set()
queue = deque([start])
while queue:
node = queue.popleft()
if node not in visited:
visited.add(node)
queue.extend(graph.get(node, []))
return visited


Question

1. How can I parallelize BFS/DFS in Python to speed up traversal? Should I use multiprocessing, concurrent.features, or another approach?

I'm open to suggestions involving alternative algorithms (like bidirectional BFS), external databases, or memory-mapped files.

I’ve tried the following:

• 
  A basic BFS using Python’s built-in set, deque, and dict, as shown in the code.
  
  
• 
  Storing the entire adjacency list in memory using a dictionary of lists, but this doesn’t scale well for millions of nodes.
  
  
• 
  I attempted to use the multiprocessing module to run multiple BFS instances in parallel from different starting points, but coordinating shared state and combining results without race conditions became complex.
  
  
• 
  Looked into NetworkX, but found it quite slow and memory-heavy for very large graphs.
  
  
• 
  I also tried reading adjacency data from a file line-by-line and processing it lazily, but traversal logic became messy and error-prone.
  

I expected to be able to:

• 
  Traverse the graph quickly (within a few seconds) even with millions of nodes.
  
  
• 
  Use multicore processing to speed up traversal.
  
  
• 
  Efficiently manage memory without crashing due to RAM limitations.
  
  
• 
  Ideally, stream data or keep only parts of the graph in memory at any given time.
  
  
• 
  I know Python isn’t the fastest language for this, but I’d like to push it as far as reasonably possible before considering rewriting in C++ or Rust.
  

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