Comp90024 Cluster and Cloud Computing Assignment 1 – Social Media Analytics

Counting Tweets

This program counts the 1. number of tweets made by different author ids, 2. The number of tweets made by different great capital cities, 3. The number of different unique capital cities tweets by author in a JSON file using MPI (Message Passing Interface) to distribute the computation across multiple processes.

Update from Teaching Teams

This work gets 8/10. Based on the feedback from the teaching teams, this project can be improved by letting all the ranks can access the file instead of using rank 0 to send data to other ranks.

Prerequisites

To run this program, you will need:

• Python 3.x
• mpi4py (can be installed using pip)
• Open MPI
• Json
• ijson
• Pandas only for visualize the result
• plotly only for visualize the result

You can install Open MPI on macOS using Homebrew:

brew install openmpi

You can install mpi4py using pip. Open Terminal and run the following command:

pip install mpi4py

Overview

Here is a working pipeline of twitterSearch.py:

Usage

To run the program, first clone the repository or directly download the zip file:

git clone https://github.com/<username>/<repository>.git Replace and with your GitHub username and the name of your repository.

Then, navigate to the repository directory:

cd <repository>

Replace with the name of your repository.

To run the program, use the following command:

mpiexec -n <num_processes> python twitterSearch.py <file_name.json>

Replace <num_processes> with the number of processes you want to use for the computation, and replace <file_name.json> with the json file you want to search.

Here's an example of how to run the program with 2 processes about file <tinyTwitter.json>:

mpiexec -n 2 python twitterSearch.py tinyTwitter.json

If you cannot run the above command you could have tried following:

mpirun -n 2 python twitterSearch.py tinyTwitter.json

Or change python to python3 (Note: We not provide the file of tinyTwitter.json and sal.json due to the reason of copyright)

The program will count the 1. number of tweets made by different author ids, 2. The number of tweets made by different great capital cities, 3. The number of different unique capital cities tweets by author in the <file_name.json> file and present the top 10.

If you could access to a HPC cluster, you could use following command as an example to submit the <twitterSearch.py> to the HPC:

scp twitterSearch.py your_username@hpc_address:/home/username

And also you could upload a slurm script to the cluster, because you might need a slurm script to submit the file to cluster, and you could upload the slurm script use following command as an example:

scp <your_slurm_script.slurm> your_username@hpc_address:/home/username

This task have three slurm script already done for testing, which is locate in file slurm:

• job1.slurm : use configuration of 1 node 1 core to execute the twitterSearch.py
• job2.slurm : use configuration of 1 node 8 core to execute the twitterSearch.py
• job3.slurm : use configuration of 2 node 8 core to execute the twitterSearch.py(4 core per node)

Here is an example to submit the slurm script, assume you are login to a cluster and the slrum script is copy to the cluster

sbatch job1.slurm

This command would submit the <job1.slurm> to cluster, you could replace the filename to your own slurm script, or other slurm script in this file

If you face a problem about using ijson in a hpc cluster, you could upload the file install_ijson.slurm to your task diretory, and run it by

sbatch install_ijson.slrum

Once your job runs successfully, you should have output file in your home directory in your cluster, the following file is the output from the configuration mention above, the example file is placed in example_output_slurm:

• job_1_output.txt : The result of use configuration of 1 node 1 core to execute the twitterSearch.py
• job_2_output.txt : The result of use configuration of 1 node 8 core to execute the twitterSearch.py
• job_3_output.txt : The result of use configuration of 2 node 8 core to execute the twitterSearch.py(4 core per node)

(Note: it might be return a <job_n_error.txt>, if some error is raised)

As last, we prepare a file call example_output_slurm/visualization.ipynb, the data use in <visualization.ipynb> are diretly retrieve from the file job_n_output.txt, you could run from top to down to see how the <twitterSearch.py> performs in <bigTwitter.json>, which is a 18.79GB+ Json files.(Note: you might not access to smallTwitter.json and bigTwitter.json file if you doesn't have access to Spartan)

You also could check the <report.pdf> to help understand this task

Acknowledgments

This program was created as a project for COMP90024 Cluster and Cloud Computing at University of Melbourne. Special thanks to Prof.Richard Sinnott for their guidance and support.