![]() ![]() What about strings, though? How you should store those generally depends on what you intend to do with them. The above tips and tricks should be helpful in dealing with numeric values as well as class objects. The absence of _dict_ removes whole 104 bytes from each instance which can save huge amount of memory when instantiating millions of values. Here we can see how much smaller the Smaller class instance actually is. This tool measures memory usage of specific function on line-by-line basis: It’s clear that there are good reasons to reduce memory usage of our Python applications, before we do that though, we first need to find the bottlenecks or parts of code that are hogging all the memory.įirst tool we will introduce is memory_profiler. Lastly, in some cases performance can be improved by adding more memory (if application performance is memory-bound), but you can’t do that if you don’t have any memory left on the machine. Therefore, optimizing for memory usage might have a nice side effect of speeding-up the application runtime. If data has to be stored on disk rather than in RAM or fast caches, then it will take a while to load and get processed, impacting overall performance. Resources - both CPU and RAM - cost money, why waste memory by running inefficient applications, if there are ways to reduce the memory footprint?Īnother reason is the notion that “data has mass”, if there’s a lot of it, then it will move around slowly. Why Bother, Anyway?īut first, why should you bother saving RAM anyway? Are there really any reason to save memory other than avoiding the aforementioned out-of-memory errors/crashes? In this article we will explore techniques for finding which parts of your Python applications are consuming too much memory, analyze the reasons for it and finally reduce the memory consumption and footprint using simple tricks and memory efficient data structures. There are many reasons to try to limit memory usage, not just avoiding having your application crash because of out-of-memory errors. Rarely is anyone concerned with memory consumption, well, until they run out of RAM. ![]() When it comes to performance optimization, people usually focus only on speed and CPU usage. Photo by The Bored Apeventurer BAYC on Unsplash
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