Introduction
Performance testing is an essential part of web application development. Apache Benchmark (ab) is a popular tool for load testing APIs and web applications. However, when working with multiple test results in the form of screenshots, analyzing them manually can be cumbersome.
In this article, we will demonstrate how to extract performance data from ab test result screenshots using Python and Tesseract OCR. We will then compare different test runs to identify performance trends and bottlenecks.
Extracting Text from ab Test Screenshots
To automate the extraction of data from Apache Benchmark screenshots, we will use pytesseract, an OCR (Optical Character Recognition) library that allows us to read text from images.
Prerequisites
Before running the script, install the required dependencies:
pip install pytesseract pillow pandasAlso, make sure Tesseract OCR is installed on your system:
- Ubuntu/Debian:
sudo apt update sudo apt install tesseract-ocr - Windows:
Download and install Tesseract from UB Mannheim.
After installation, verify that tesseract is available by running:
tesseract --versionPython Script for Extracting Text
The following Python script extracts text from two ab test screenshots and prints the results:
import pytesseract
from PIL import Image
# Path to the images
image_path_1 = "path/to/first_ab_test_screenshot.png"
image_path_2 = "path/to/second_ab_test_screenshot.png"
# Extract text from images using Tesseract
text_1 = pytesseract.image_to_string(Image.open(image_path_1))
text_2 = pytesseract.image_to_string(Image.open(image_path_2))
# Print extracted text
print("Extracted Text from Test 1:
", text_1)
print("
Extracted Text from Test 2:
", text_2)This script reads the images and extracts all text, including metrics such as requests per second, response times, and failure rates.
Comparing Apache Benchmark Test Results
Once we have extracted the text, we can analyze key performance metrics from multiple test runs.
Example of Performance Comparison
Here’s an example of comparing two test runs:
| Metric | Test 1 | Test 2 | Conclusion |
|---|---|---|---|
| Total Requests | 4021 | 4769 | Test 2 handled more requests |
| Requests per Second | 57.44 | 67.78 | Test 2 is more performant |
| Mean Response Time (ms) | 348.2 | 295.1 | Test 2 has lower response time |
| Max Response Time (ms) | 1480 | 1684 | Test 2 has some slow spikes |
| Transfer Rate (KB/s) | 35.99 | 42.46 | Test 2 has better data transfer |
Key Insights:
✅ Test 2 performed better in terms of handling more requests and achieving a lower average response time.
✅ Transfer rate improved, meaning the system processed data more efficiently.
⚠️ Max response time in Test 2 increased, indicating some requests experienced higher latency.
Next Steps for Optimization
If we observe performance degradation, here are some actions we can take:
- Check backend logs to identify slow database queries or API calls.
- Monitor CPU & Memory Usage during the test to detect potential resource bottlenecks.
- Optimize database queries using indexes and caching.
- Load balance traffic across multiple servers if the system is reaching capacity.
Conclusion
This approach demonstrates how Python, pytesseract, and ab test results can be combined to automate performance analysis. By extracting and comparing key metrics, we can make informed decisions to optimize our web applications.
🚀 Next Steps: Try this approach with your own API performance tests and share your insights with the community!
Further Reading
📢 Do you have experience analyzing ab test results? Share your findings in the comments below!
