Python Profiler
- Python Profiler: A Server Engineer's Guide
This article details the use of Python profilers on our MediaWiki servers. Profiling is crucial for identifying performance bottlenecks in Python-based services, ultimately leading to improved server responsiveness and stability. This guide is aimed at server engineers and developers new to Python profiling techniques within the MediaWiki environment.
Understanding Python Profilers
Python profilers are tools that measure the execution time of different parts of your code. They help pinpoint which functions are consuming the most resources, allowing you to focus your optimization efforts effectively. There are several profiling options available, each with its strengths and weaknesses. We primarily utilize `cProfile` and `line_profiler` on our servers. Understanding the differences is important for choosing the right tool for the job. Debugging is often the first step, but profiling gives quantitative data.
cProfile: Statistical Profiler
`cProfile` is a built-in Python profiler that provides deterministic profiling. It records how often each function is called and how much time is spent in each function. It’s relatively low overhead, making it suitable for production environments (with careful consideration of the overhead – see "Performance Considerations" below).
Usage
To run `cProfile`, you can use the following command:
```bash python -m cProfile -o profile_output.prof your_script.py ```
This command will run `your_script.py` under the profiler and save the results to `profile_output.prof`. You can then analyze the output using the `pstats` module:
```python import pstats p = pstats.Stats('profile_output.prof') p.sort_stats('cumulative').print_stats(20) # Sort by cumulative time and show top 20 lines ```
cProfile Output Analysis
The `pstats` output presents several key columns:
| Column | Description |
|---|---|
| ncalls | Number of times the function was called. |
| tottime | Total time spent *in* the function (excluding calls to sub-functions). |
| percall (tottime) | Average time per call to the function (tottime / ncalls). |
| cumtime | Cumulative time spent in the function *and* all sub-functions it calls. |
| percall (cumtime) | Average cumulative time per call (cumtime / ncalls). |
Focus on the `cumtime` column to identify functions that consume the most time overall. High `tottime` values indicate functions where optimization might yield the greatest benefits. Understanding Code Optimization is key to interpreting the results.
line_profiler: Line-by-Line Profiler
`line_profiler` provides a more granular view of performance by profiling code on a line-by-line basis. This is invaluable for identifying bottlenecks within specific functions. However, it introduces more overhead than `cProfile` and is typically used during development or in controlled testing environments. It requires installation: `pip install line_profiler`.
Usage
1. Decorate the functions you want to profile with `@profile`. Note that `profile` is *not* a built-in decorator – it's provided by `line_profiler`. 2. Run the script with `kernprof -l your_script.py`. This generates a `.lprof` file. 3. Analyze the results with `python -m line_profiler your_script.py.lprof`.
line_profiler Output Analysis
The `line_profiler` output shows the time spent on each line of the decorated function.
| Column | Description |
|---|---|
| Line # | Line number in the function. |
| Hits | Number of times the line was executed. |
| Time | Total time spent on the line (in microseconds). |
| Per Hit | Average time per execution of the line (Time / Hits). |
| % Time | Percentage of the total time spent in the function that was spent on this line. |
Pay attention to lines with high `% Time` values. These represent the primary bottlenecks within the function. Performance Testing should be conducted *after* optimization.
Performance Considerations
Profiling, especially `line_profiler`, introduces overhead. Running profilers in production can negatively impact server performance.
| Profiler | Overhead | Use Case |
|---|---|---|
| cProfile | Low | Production monitoring (with caution), general performance analysis |
| line_profiler | High | Development, targeted performance analysis, controlled testing |
| memory_profiler | Moderate | Identifying memory leaks and high memory usage |
Therefore:
- Use `cProfile` for initial investigations and long-running processes where overhead must be minimized.
- Use `line_profiler` for targeted analysis of specific functions during development or in staging environments.
- Always benchmark your code *before* and *after* applying optimizations to ensure that the changes actually improve performance. See Benchmarking for more details.
- Consider using sampling profilers for extremely low overhead in production, though they provide less precise results.
Integrating with MediaWiki
Profiling Python code within the MediaWiki environment requires understanding the request lifecycle. You can instrument your code within `extensions` or core MediaWiki code by temporarily adding profiling statements. However, avoid modifying core MediaWiki code directly; create extensions instead. Extension Development is the recommended approach. Remember to remove profiling code after analysis to avoid performance degradation. Server Monitoring tools can also aid in identifying slow queries or processes.
Related Pages
- Debugging
- Code Optimization
- Performance Testing
- Benchmarking
- Extension Development
- Server Monitoring
- Database Optimization
- Caching
- Web Server Configuration
- PHP Profiling
- System Resource Monitoring
- Memory Management
- Concurrency Control
- Asynchronous Tasks
- Load Balancing
Intel-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Core i7-6700K/7700 Server | 64 GB DDR4, NVMe SSD 2 x 512 GB | CPU Benchmark: 8046 |
| Core i7-8700 Server | 64 GB DDR4, NVMe SSD 2x1 TB | CPU Benchmark: 13124 |
| Core i9-9900K Server | 128 GB DDR4, NVMe SSD 2 x 1 TB | CPU Benchmark: 49969 |
| Core i9-13900 Server (64GB) | 64 GB RAM, 2x2 TB NVMe SSD | |
| Core i9-13900 Server (128GB) | 128 GB RAM, 2x2 TB NVMe SSD | |
| Core i5-13500 Server (64GB) | 64 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Server (128GB) | 128 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Workstation | 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000 |
AMD-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Ryzen 5 3600 Server | 64 GB RAM, 2x480 GB NVMe | CPU Benchmark: 17849 |
| Ryzen 7 7700 Server | 64 GB DDR5 RAM, 2x1 TB NVMe | CPU Benchmark: 35224 |
| Ryzen 9 5950X Server | 128 GB RAM, 2x4 TB NVMe | CPU Benchmark: 46045 |
| Ryzen 9 7950X Server | 128 GB DDR5 ECC, 2x2 TB NVMe | CPU Benchmark: 63561 |
| EPYC 7502P Server (128GB/1TB) | 128 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/2TB) | 128 GB RAM, 2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/4TB) | 128 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/1TB) | 256 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/4TB) | 256 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 9454P Server | 256 GB RAM, 2x2 TB NVMe |
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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️