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Course 2

Understanding Slowness

Slow Web Server

ab - Apache benchmark tool

ab -n 500 site.example.com

Profiling - Improving the code

Profiling 可透過監控和分析即時資源使用情況，協助軟體工程師設計高效率且有效的應用程式。對 IT 專業人員而言，Profile 的能力是非常寶貴的工具。雖然 Profiling 並非新技術，但類似技術在今日仍然適用，而且 Profiling 可改善反應速度並最佳化資源使用，為軟體開發奠定穩固的基礎

A profiler is a tool that measures the resources that our code is using, giving us a better understanding of what's going on.

  • gprof : For C program
  • cProfile : For Python program
  • pprofile3 + kcachegrind(GUI) : For Python program
  • Flat, Call-graph, and Input-sensitive are integral to debugging
  • timeit (python module) : Measure execution time of small code snippets

Parallelizing operations

Python modules

  • threading
  • asyncio
  • future
Concurrency for I/O-bound tasks

Python has two main approaches to implementing concurrency: threading and asyncio.

  1. Threading is an efficient method for overlapping waiting times. This makes it well-suited for tasks involving many I/O operations, such as file I/O or network operations that spend significant time waiting. There are however some limitations with threading in Python due to the Global Interpreter Lock (GIL), which can limit the utilization of multiple cores.

  2. Alternatively, asyncio is another powerful Python approach for concurrency that uses the event loop to manage task switching. Asyncio provides a higher degree of control, scalability, and power than threading for I/O-bound tasks. Any application that involves reading and writing data can benefit from it, since it speeds up I/O-based programs. Additionally, asyncio operates cooperatively and bypasses GIL limitations, enabling better performance for I/O-bound tasks.

Python supports concurrent execution through both threading and asyncio; however, asyncio is particularly beneficial for I/O-bound tasks, making it significantly faster for applications that read and write a lot of data.

Parallelism for CPU-bound tasks

Parallelism is a powerful technique for programs that heavily rely on the CPU to process large volumes of data constantly. It's especially useful for CPU-bound tasks like calculations, simulations, and data processing.

Instead of interleaving and executing tasks concurrently, parallelism enables multiple tasks to run simultaneously on multiple CPU cores. This is crucial for applications that require significant CPU resources to handle intense computations in real-time.

Multiprocessing libraries in Python facilitate parallel execution by distributing tasks across multiple CPU cores. It ensures performance by giving each process its own Python interpreter and memory space. It allows CPU-bound Python programs  to process data more efficiently by giving each process its own Python interpreter and memory space; this eliminates conflicts and slowdowns caused by sharing resources. Having said that, you should also remember that when running multiple tasks simultaneously, you need to manage resources carefully.

 

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