Hyperautomation

How to optimize performance in automations with Python RPA

✅ Robots often run processes 24/7, for months and years. Although Python provides up to 20x faster performance than low-code platforms because it is pure code and easier for the machine to process, applying small improvements can still bring big results over time;

To test the performance of your code in more detail, you can use libraries/profilers to identify bottlenecks:
– Timeit
– line_profiler
– memory_profiler
– cProfile
– Stackfy

✅ Every project has its requirements, but it is worth considering a few items to further optimize your robot in Python:

– Don’t import unnecessary libraries. Only load them when you need them

– Reduce data traffic. Download only what you use

– Evaluate other processes running on the same machine and avoid peak times

– Monitor the performance of your bots to identify degradations

– Don’t do repetitive and unnecessary calculations. Use caching

– Use appropriate algorithms and structures for each case (lists/tuples, sets/dictionaries)

– Use built-in functions and libraries

– Use generators (prefer xrange to range) and keys for sorting

– Free memory (close, exit…) and avoid creating unnecessary objects

– Optimize database queries and processing

– Check how best to access some of the data with APIs

– If possible, use asynchronous communication

– Evaluate whether it makes sense to deploy with container or serverless

– Consider using background and parallel automations when possible

– Evaluate loops and commands within loops, prefer List Comprehensions

– It can be more performant to use IDs and keyboard shortcuts than image recognition and mouse movement

– Use set operations

– Avoid global variables

– Use vector/NumPy operations and optimized libraries like Pandas and Polars

– Use multiple assignments

– Use in when possible

– Optimize string operations (e.g. join instead of + for concatenation) and interning strings

– Also learn about Multiprocessing, Itertools, CPython/Cython, Pypy, Mojo…

– Avoid optimizing performance and hindering robot code reading and maintenance

✅ There are also several tools to help optimize and secure code in Python:
– Deepsource
– Codacy
– SonarQube
– Veracode
– Checkmarx

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