Hunting Performance in Python Code – Part 2. Measuring Memory Consumption

In this post I will talk about some tools that can help us solve a painful problem in Python, especially when using PyPy: memory consumption.

Why are we concerned with this in the first place? Why don’t we care only about performance? The answer to these questions is rather complex, but I’ll summarize it.

PyPy is an alternative Python interpreter, that features some great advantages over CPython: speed (through it’s Just in Time compiler), compatibility (it is almost a drop in replacement of CPython) and concurrency (using stackless and greenlets).

One downside of PyPy is that in general it uses more memory than CPython, due to it’s JIT  and garbage collector implementation. Nevertheless, in some cases, it is able to use less memory than CPython.

We will see next how you can measure the amount of memory used by your application.

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Hunting Performance in Python Code – Part 1. Environment Setup

This is the first post in the series “Hunting Performance in Python Code”. Through each post I’ll present some of the tools and profilers that exists for Python code and how each of them helps you to better find bottlenecks both in frontend (Python scripts) and/or in the backend (Python interpreter).

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Enabling Profile Guided Optimizations for PyPy

PyPy, compared to CPython relies more on achieving speed-up by “jitting” code as often as possible, rather than rely on its interpreter. However, jitting is not always an option, or at least not entirely. A good improvement for CPython, that we think might benefit PyPy as well, without impacting the JIT performance is Profile Guided Optimization (PGO or profopt).

I thank the PyPy developer community for their patience, kind advice and constant feedback they gave me in #pypy IRC or through email, which helped me to make this possible, especially to Carl Friedrich Bolz-Tereick and Armin Rigo.

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