- #Difference between parallel and multibrowser testing code#
- #Difference between parallel and multibrowser testing download#
Parallelism and Concurrency in Python: Multithreading Example For convenience, all of these Python scripts can be found in this GitHub repository.
#Difference between parallel and multibrowser testing code#
The good news is that by introducing concurrency or parallelism, we can speed this up dramatically.Īll subsequent code examples will only show import statements that are new and specific to those examples. For 9000 pictures it would take 30 minutes. With an average of 0.2 seconds per picture, 900 images would take approximately 3 minutes.
#Difference between parallel and multibrowser testing download#
19.4 seconds isn’t terribly long, but what if we wanted to download more pictures? Perhaps 900 images, instead of 90. Please do note that these numbers may vary based on the network you are on. On my laptop, this script took 19.4 seconds to download 91 images. Raise Exception("Couldn't find IMGUR_CLIENT_ID environment variable!") Logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') Here is what single.py looks like: import loggingįrom download import setup_download_dir, get_links, download_link Finally, it will fetch a list of images using the get_links function, filter out all GIF and album URLs, and then use download_link to download and save each of those images to the disk. It will invoke the setup_download_dir to create the download destination directory. The module will retrieve the Imgur client ID in the environment variable IMGUR_CLIENT_ID. This will contain the main function of our first, naive version of the Imgur image downloader. Next, we will need to write a module that will use these functions to download the images, one by one. With urlopen(link) as image, download_path.open('wb') as f: Return for item in data if 'type' in item and item in types]ĭownload_path = directory / os.path.basename(link) Req = Request('', headers=headers, method='GET')ĭata = json.loads(resp.read().decode('utf-8')) This is what the script looks like: import jsonįrom urllib.request import urlopen, Request Downloading the image is an even simpler task, as all you have to do is fetch the image by its URL and write it to a file. We can use Python’s standard JSON library to decode it. You can find this client ID from the dashboard of the application that you have registered on Imgur, and the response will be JSON encoded. Imgur’s API requires HTTP requests to bear the Authorization header with the client ID. The third function, setup_download_dir, will be used to create a download destination directory if it doesn’t already exist. We will split these functionalities into three separate functions: This file will contain all the functions necessary to fetch the list of images and download them. Let us start by creating a Python module, named download.py. Getting Started with Python Multithreading With some changes, they should also run with Python 2-urllib is what has changed the most between these two versions of Python. The scripts in these Python multithreading examples have been tested with Python 3.6.4. If you do not have an Imgur account already, please create one first. As a prerequisite, you will have to register an application on Imgur. We will start with a version that downloads images sequentially, or one at a time.
To demonstrate concurrency in Python, we will write a small script to download the top popular images from Imgur. If you haven’t read it yet, I suggest you take a look at Eqbal Quran’s article on concurrency and parallelism in Ruby here on the Toptal Engineering Blog. It must be made clear that one can still write code in Python that runs concurrently or in parallel and make a stark difference in resulting performance, as long as certain things are taken into consideration. Due to this, the Python multithreading module doesn’t quite behave the way you would expect it to if you’re not a Python developer and you are coming from other languages such as C++ or Java. Enjoy!ĭiscussions criticizing Python often talk about how it is difficult to use Python for multithreaded work, pointing fingers at what is known as the global interpreter lock (affectionately referred to as the GIL) that prevents multiple threads of Python code from running simultaneously. Note: By popular request that I demonstrate some alternative techniques-including async/await, only available since the advent of Python 3.5-I've added some updates at the end of the article.
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