Example of Python asyncio module and difference between thread and sync approach
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The repository contains a code that was presented on Kiwi.com Python weekend. It contains a simple code to demonstrate performance differences between asyncio and ThreadPoolExecutor. For the purpose of the testing I created a simple code that scrape bus ticket prices for a date-range from RegioJet website.


The script scrapes prices for 100 days in-front. Memory usage was measured by memory_profiler. For the sake of simplicity the script just loads HTML response from a server and looks for ticket prices. There is no further processing of the scraped data.


For a reference, I executed a single threaded version of the script. The memory usage is pretty stable during the entire execution of the script. The obvious drawback is an execution time. Without any parallelism the script took over 80 seconds.



For a long time, threading was the only way how to speed up an IO heavy application. Threads come up with a drawback of a higher memory usage, but with a great advantage of speed. Exection time is ~17sec. Compared to 29 seconds for synchronous execution, this a huge difference.

ThreadPoolExecutor Example of Python asyncio module and difference between thread and sync approach.


Gevent brings coroutines to older versions of Python. It's a similar concept to asyncio.

Asyncio exection


Since Python 3.5, there is a preferred way how to parallelise IO operations. With coroutines, the script takes the advantage of a parallel execution but requires less memory then multithreading.

Asyncio exection

Try it yourself

Use Python 3.5 version or higher and install dependencies:

bash pip install -r requirements.txt 

Then just execute each script with the memory profiling tool.

bash mprof run student_agency_sync.py

After each run, the memory usage graphs can be generated with a following command:

bash mprof plot