This project benchmarks popular Python HTTP libraries to compare their performance across core networking metrics. The goal is to offer insights on how each library performs under real-world request loads.

📦 HTTP Libraries Benchmarked

This benchmark includes five widely-used Python HTTP libraries—requests, urllib3, httpx, aiohttp, and pycurl—chosen for their relevance, diversity, and real-world usage. requests is the most popular and beginner-friendly library, while urllib3 offers low-level control and powers requests internally. httpx and aiohttp provide modern asynchronous support for high-concurrency applications. Finally, pycurl wraps the high-performance C-based libcurl library, often used in system-level or legacy environments. Together, they represent a broad spectrum of HTTP client capabilities in Python, from ease of use to advanced performance tuning.

• aiohttp
• httpx
• requests
• urllib3
• pycurl

📊 Metrics Captured

Each library is evaluated using the following metrics:

• Requests per Second (req/sec): Measures throughput. A higher number indicates the library handles more traffic efficiently.
• Total Time: Measures how long the library takes to complete a full batch of requests.
• Average Connection Time: Indicates the average time spent on establishing connections for each request.

These metrics are chosen to reflect practical usage in applications such as REST APIs, microservices, and integrations.

🎯 Benchmarking Strategy

To ensure fair and unbiased comparisons:

• Each run sends a fixed number of requests (NUM_REQUESTS = 100) to https://postman-echo.com/get.
• The script performs 6 complete benchmark runs, where the first is used as warm-up and excluded from the CSV output.
• Randomized Execution Order: For every run, the order in which HTTP libraries are tested is randomized. This prevents any one library from benefiting from system or network caching effects due to consistent positioning.

📁 Output

Benchmark results are stored in benchmark_results.csv. Each row represents one full benchmarking round and includes:

• Start and end timestamps
• Requests per second, total duration, and average connection time for each library
• Number of requests executed

▶️ How to Run

1. Install requirements:

pip install -r requirements.txt

2. Running the benchmark:

3. Running Analytics:

python benchmark_analytics.py

Results

Environment:

OS: Windows 11 Home

Processor: Intel(R) Core(TM) i5-10400 CPU @ 2.90GHz 2.90 GHz

RAM: 12.0 GB

Location Type: Localhost