Installing Pathway with Python 3.10+

In the cell below, we install Pathway into a Python 3.10+ Linux runtime.

If you are running in Google Colab, please run the colab notebook (Ctrl+F9), disregarding the 'not authored by Google' warning.

The installation and loading time is less than 1 minute.

python

%%capture --no-display
!pip install --prefer-binary pathway

python

import logging

logging.basicConfig(level=logging.CRITICAL)

Real-Time Shortest Paths on Dynamic Networks with Bellman-Ford in Pathway

This article explains step-by-step how the Bellman–Ford algorithm may be implemented in Pathway.

Introduction

The Bellman-Ford algorithm computes the shortest paths from a single source vertex to all the other vertices in a weighted graph. A weighted graph is composed of a set of points, called vertices, which are connected via edges. Each edge is associated to a value, called either weight or distance. For instance, the set of all the cities and the roads which connect them form such a graph. In that example, the Bellman-Ford algorithm would help to find the fastest way, in terms of distance, to go from a given city to another.

This article is also a perfect place to familiarize yourself with several constructs used in Pathway.

Code

First things first - imports 🙂

python

import math

import pathway as pw

# To use advanced features with Pathway Scale, get your free license key from
# https://pathway.com/features and paste it below.
# To use Pathway Community, comment out the line below.
pw.set_license_key("demo-license-key-with-telemetry")

I/O Data

The input is a weighted graph so it is natural to split representation of the data into two parts: Vertices and Edges. Their schemas:

python

class Vertex(pw.Schema):
    is_source: bool


class Edge(pw.Schema):
    u: pw.Pointer[Vertex]
    v: pw.Pointer[Vertex]
    dist: int

These schemas have a natural interpretation. You can think of the Edge schema as of a blueprint of a table that has 3 columns: u, v (foreign keys) and dist. The output schema:

python

class DistFromSource(pw.Schema):
    dist_from_source: int

Note: The schemas inherit from pw.Schema special class.

Note: You might wonder why output schema has only one column dist_from_source. Actually, you can join schemas together to create a new one. And so, the output schema is Vertex + DistFromSource. (Look for that type annotation later in the code.)

The algorithm

The Bellman-Ford algorithm performs some number of relaxations until it reaches a fixed point.

Relaxations

Each node checks if a path via it would make any so-far-optimal path to some other node shorter.

python

def bellman_ford_step(
    vertices_dist: pw.Table[DistFromSource], edges: pw.Table[Edge]
) -> pw.Table[DistFromSource]:
    relaxed_edges = edges + edges.select(
        dist_from_source=vertices_dist.ix(edges.u).dist_from_source + edges.dist
    )
    vertices_dist = vertices_dist.update_rows(
        relaxed_edges.groupby(id=relaxed_edges.v).reduce(
            dist_from_source=pw.reducers.min(relaxed_edges.dist_from_source),
        )
    )

    return vertices_dist

Fixed point

The relaxations are iterated until a fixed point is reached. In this case, reaching a fixed point means that no new (shorter) path was found in the last iteration.

python

def bellman_ford(vertices: pw.Table[Vertex], edges: pw.Table[Edge]):
    vertices_dist: pw.Table[DistFromSource] = vertices.select(
        dist_from_source=pw.if_else(vertices.is_source, 0.0, math.inf)
    )

    fixed_point = pw.iterate(
        lambda iterated, edges: dict(
            iterated=bellman_ford_step(vertices_dist=iterated, edges=edges)
        ),
        # The `pw.iterate_universe` stanza informs iterate that `vertices_dist` grows with each loop iteration. Without it, the system assumes that iterations don't change the set of indices of a table.
        iterated=pw.iterate_universe(vertices_dist),
        edges=edges,
    ).iterated

    return fixed_point.join(vertices, fixed_point.id == vertices.id).select(
        vertices.key, fixed_point.dist_from_source
    )

Tests

Now, let's see the code in action. The following test case runs Bellman-Ford algorithm on a graph depicted below.

python

# a directed graph
vertices = pw.debug.table_from_markdown(
    """
  | key | is_source
1 | 1   | True
2 | 2   | False
3 | 3   | False
4 | 4   | False
5 | 5   | False
6 | 6   | False
7 | 7   | False
"""
).with_id_from(pw.this.key)

edges = pw.debug.table_from_markdown(
    """
    | u  | v | dist
11 | 1  | 2 | 100
12 | 1  | 3 | 200
13 | 1  | 4 | 300
14 | 3  | 5 | 100
15 | 3  | 6 | 500
16 | 5  | 6 | 100
17 | 6  | 3 | -50
"""
).with_columns(
    u=vertices.pointer_from(pw.this.u),
    v=vertices.pointer_from(pw.this.v),
)

Pathway automatically reindexes the tables, so we need a key column of the vertices table and we need ask Pathway to reindex the table using those. In practice, Pathway uses pointers so the keys are automatically converted into pointers.

For the edges, we have to convert the keys into their references in order to be able to use vertices_dist.ix(edges.u) as ix only works with pointers.

python

pw.debug.compute_and_print(bellman_ford(vertices, edges))

That was a simple introduction to writing code and tests in Pathway.

Feel free to take this code and experiment with it 🙂 Do you see any possibility to improve the code? (What happens when there is a negative cycle in the graph?)

Summary

The code above follows a pattern that is quite frequent when working with Pathway:

Define I/O data types
Define transformations on tables
Iterate the transformation until a fixed point is reached
- usually transforms the data by a simple one-liner.
- for example iterate(lambda foo, bar: {foo=fn(foo, bar), bar=bar}, foo=input_table_1, bar=input_table2).foo