Hashing Performance Benchmark Script

Summary

This Python script is designed to benchmark the performance of various hashing algorithms by measuring the average time it takes to hash a set of random strings of different lengths. It utilizes multiprocessing to perform concurrent hashing for efficient analysis. The script generates visualizations in the form of a bar chart, saves the results as a JSON file, and creates an interactive HTML plot for further analysis.

Key Points

• Hashing Algorithms: The script supports the following hashing algorithms for benchmarking:

  • MD5
  • SHA-1
  • SHA-256
  • SHA-512
  • SHA3-256
  • SHA3-512

• String Lengths: It tests hashing performance across various string lengths, including 10, 25, 50, 75, 100, 150, 200, 500, 1000 and 5000 characters.

• Parallel Processing: The script leverages the multiprocessing module to hash random strings concurrently, making efficient use of available CPU cores.

• Visualization: It generates a bar chart using Matplotlib, illustrating the average hashing time versus string length for each algorithm.

• Results Storage: The results, including the bar chart, are saved in a timestamped folder. The results folder contains:

  • A PDF of the bar chart.
  • A JSON file containing the average hashing times.
  • An interactive HTML plot for further exploration.
  • 

Details

How the Script Works

1. Importing Libraries: The script imports the necessary libraries and modules, including hashlib, random, string, time, multiprocessing, Matplotlib, and mpld3.

2. Constants: It defines various constants, including the hashing algorithms to test, different string lengths to test, the number of random strings to generate, and a custom color palette for plotting.

3. Random String Generation: The script provides a function (generate_random_string) to generate random strings of a specified length, excluding emojis.

4. Hashing Function: Another function (hash_string) is defined to hash a given input string using a specified hashing algorithm.

5. Hashing and Measurement: The hash_and_measure_avg_time function hashes a set of random strings using a chosen algorithm and measures the average time it takes to hash them. It leverages the multiprocessing module for parallel processing.

6. Main Function: The main function orchestrates the entire benchmarking process:

   • Determines the number of available CPU cores.
   • Generates random strings of various lengths.
   • Initializes a dictionary to store the benchmark results.
   • Creates a timestamped folder to store results.
   • Iterates over the specified hashing algorithms, using multiprocessing for efficient hashing.
   • Pauses for 1 second between algorithm changes.
   • Generates a bar chart to visualize the average hashing times.
   • Converts the Matplotlib plot to an interactive HTML plot using mpld3.
   • Saves the results as a PDF, a JSON file, and an HTML file in the results folder.

Running the Script

1. Ensure you have Python installed.

2. Install required libraries using pip: pip install matplotlib mpld3

3. Execute the script: python Hashing_Performance_Benchmark_Script.py

4. The script will generate hash times and visualizations for various hashing algorithms and string lengths, saving results in a timestamped folder for analysis.