Dijkstra’s algorithm - Hadoop Python

This project was developed during my masters at Paris-Dauphine university. You can find here the project report.

Single-source shortest path problem

The purpose of this algorithm is to find shortest path of a source node to all other nodes in a graph. This problem is solved by the Dijkstra’s algorithm.
This project has double purposes. First, to get familiar with Dijkstra’s algorithm, then implement a MapReduce version of it. The algorithm is iterative, so the identified MapReduce job must be iterated several times to find the final solution.
We provided a Python-Hadoop streaming and Spark (Python) implementations of the algorithm.

Optional: perform scalability experiments. A single comparison on a reasonable big graph would be sufficient.

Formating data

This script takes data and convert it to the compatible format of the algorithm. For specific data formats, you can implement your own map job formatter.

cat data/new-data.dat | sort | python/prepare.py 1 >> data/input.dat

Solving a graph using Dijkstra’s algorithm

Python

# Executing python MapReduce job 3 times with the cat command
$ cat data/input.dat | python/mapper.py | sort | python/reducer.py | \
                       python/mapper.py | sort | python/reducer.py | \
                       python/mapper.py | sort | python/reducer.py

Python-Hadoop streaming

# Executing hadoop streaming MapReduce Job
hadoop jar $HADOOP_HOME/share/hadoop/tools/lib/hadoop-streaming-2.8.1.jar \
                    -input data/input.dat -output output \
                    -file python/mapper.py -mapper mapper.py \
                    -file python/reducer.py -reducer reducer.py

Spark

You can copy the source code in this file and paste it in pyspark commandline.
TODO: implement spark submit version.

Scalability experiment

We used facebook data for the Scalability experiment. You can download it here.

Hadoop Streaming Job Chaining

Check this repository to chain Map Reduce streaming jobs and run them multiples time (iterations).