Performance Comparison: JavaScript vs Python

This study compares the performance of JavaScript (Node.js v20.17.0) and Python (3.12.6) across four tasks: Fibonacci calculation (recursive), file reading (Shakespeare's works, ~5.4MB), JSON parsing, and matrix multiplication. Each task was executed five times, and the results were recorded for analysis.

Note: This study uses plain JavaScript and Python, without any external libraries (e.g., NumPy for Python or math.js for JavaScript). This ensures a fair comparison of pure language capabilities without leveraging C-based optimizations.

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1. Hypothesis

It is hypothesized that Python is overperformed by JavaScript in various tasks.

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2. Tasks and Benchmarks

Task 1: Fibonacci Calculation (Recursive)

A recursive Fibonacci function was used to calculate the 30th Fibonacci number. Recursive implementations highlight raw CPU-bound performance.

Language	Try 1	Try 2	Try 3	Try 4	Try 5	Average
JavaScript	21.978ms	21.978ms	20.1ms	19.226ms	19.477ms	20.55ms
Python	214.126ms	207.010ms	214.091ms	220.466ms	211.350ms	213.81ms

Observation: JavaScript was approximately 10 times faster than Python in this CPU-bound task.

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Task 2: File Reading

Both languages read Shakespeare's complete works (5.4MB text file). This task evaluates I/O performance.

Language	Try 1	Try 2	Try 3	Try 4	Try 5	Average
JavaScript	9.312ms	9.16ms	9.278ms	9.701ms	8.824ms	9.255ms
Python	15.539ms	19.755ms	21.368ms	13.361ms	15.431ms	17.09ms

Observation: JavaScript was approximately 1.8 times faster than Python in file I/O operations.

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Task 3: JSON Parsing

A large JSON object (~100,000 key-value pairs) was parsed in both languages. This task evaluates string processing and parsing efficiency.

Language	Try 1	Try 2	Try 3	Try 4	Try 5	Average
JavaScript	13.234ms	13.188ms	13.284ms	13.241ms	13.424ms	13.27ms
Python	20.040ms	16.490ms	15.250ms	18.780ms	20.030ms	18.12ms

Observation: JavaScript was approximately 1.4 times faster than Python in JSON parsing.

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Task 4: Matrix Multiplication

Matrix multiplication was performed on two square matrices of size (128 \times 128). This task evaluates raw computational performance for numerical operations.

Language	Try 1	Try 2	Try 3	Try 4	Try 5	Average
JavaScript	29.671ms	32.257ms	28.485ms	29.445ms	29.342ms	29.84ms
Python	338.45ms	341.48ms	337.02ms	339.54ms	352.16ms	341.73ms

Observation: JavaScript was approximately 11.5 times faster than Python in matrix multiplication.

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3. Averages Summary

Task	JavaScript Average	Python Average	Performance Ratio (JS/Python)
Fibonacci Calculation	20.55ms	213.81ms	~10.4x faster
File Reading	9.255ms	17.09ms	~1.8x faster
JSON Parsing	13.27ms	18.12ms	~1.4x faster
Matrix Multiplication	29.84ms	341.73ms	~11.5x faster

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4. Conclusion

1. Fibonacci Calculation:

   • JavaScript is significantly faster than Python in CPU-bound tasks, as evidenced by the recursive Fibonacci calculation.

2. File Reading:

   • JavaScript outperforms Python in file I/O tasks, handling the text file ~1.8 times faster.

3. JSON Parsing:

   • JavaScript maintains its advantage but with a smaller margin. Both implementations performed reasonably well.

4. Matrix Multiplication:

   • JavaScript shows an even greater advantage, performing matrix multiplication ~11.5 times faster than Python.

Overall Findings

JavaScript consistently outperformed Python across all tasks in this study. Python's performance, while reasonable, is limited in raw computational and I/O-heavy tasks when using plain code. For scenarios where performance is critical, JavaScript (Node.js) is the preferred choice.

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5. Environment Details

• JavaScript:

  • Node.js version: v20.17.0
  • Benchmark script: Pure JavaScript.

• Python:

  • Python version: 3.12.6
  • Benchmark script: Pure Python.