experiments's Introduction

Introduction

This a learning evolution on how one can speed up basic I/O such as multiple HTTP request connections by using Python.

Let's define our data set first. For this exercise I picked up the Top 1M visited websites. This is a report by Amazon and can be found here.

We basically want to crate a GET HTTP request to every website in the list.

For the sake of this exercise I'm going to get only up to the first 1000 endpoints.

Runs

Let's start by the most simplistic way:

$ python socket_sync.py
10 endpoints took 3.12 seconds
100 endpoints took 41.84 seconds
500 endpoints took 317.75 seconds
1000 endpoints took 642.83 seconds

What about multiprocessing?

# Using 8 processes
$ python socket_multiprocessing.py
10 endpoints took 0.95 seconds
100 endpoints took 8.68 seconds
500 endpoints took 52.82 seconds
1000 endpoints took 86.07 seconds

Now let's try concurrent.futures with a ThreadPoolExecutor.

$ python socket_futures.py
10 endpoints took 0.83 seconds
100 endpoints took 6.06 seconds
500 endpoints took 34.15 seconds
1000 endpoints took 75.72 seconds

Finally, let's use asyncio:

The idea here is that this code is more efficient resource wise because we don't rely on the OS scheduler.

$ python socket_asyncio.py
10 endpoints took 1.46 seconds
100 endpoints took 3.15 seconds
500 endpoints took 16.91 seconds
1000 endpoints took 57.64 seconds

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