Note that the stack frame is also responsible for setting the scope for the variables of a method. All objects and instance variables are stored in the heap memory. When a variable is created in Python, it is stored in a private heap which will then allow for allocation and deallocation. Python has three different levels when it comes to its memory structure:. Imagine a desk with 64 books covering its whole surface.
The top of the desk represents one arena that has a fixed size of KiB which is allocated in the heap Note that KiB is different from KB, but you may assume that they are the same for this explanation. An arena represents the largest possible chunk of memory. More specifically, arenas are memory mappings that are used by the Python allocator, pymalloc , which is optimized for small objects less than or equal to bytes. Arenas are responsible for allocating memory, and therefore subsequent structures do not have to do it anymore.
This arena can then be further broken down into 64 pools , which is the next biggest memory structure. Going back to the desk example, the books represent all the pools within one arena. Each pool would typically have a fixed size of 4Kb and can have three possible states:. Note that the size of the pool should correspond to the default memory page size of your operating system.
A pool is then broken down into many blocks , which are the smallest memory structures. Returning to the desk example, the pages within each book represent all the blocks within a pool.
Unlike arenas and pools, the size of a block is not fixed. The size of a block ranges from 8 to bytes and must be a multiple of eight. Each block can only store one Python object of a certain size and have three possible states:. Note that the three different levels of a memory structure arenas, pools, and blocks that we discussed above are specifically for smaller Python objects. Large objects are directed to the standard C allocator within Python, which would be a good read for another day.
Garbage collection is a process carried out by a program to release previously allocated memory for an object that is no longer in use. You can think of garbage allocation as memory recycling or reusing. Back in the day, programmers had to manually allocate and deallocate memory. Forgetting to deallocate memory would lead to a memory leak, leading to a drop in execution performance.
Worse, manual memory allocation and deallocation are even likely to lead to accidental overwriting of memory, which can cause the program to crash altogether. In Python, garbage collection is done automatically and therefore saves you a lot of headaches to manually manage memory allocation and deallocation.
Specifically, Python uses reference counting combined with generational garbage collection to free up unused memory. The reason why reference counting alone does not suffice for Python because it does not effectively clean up dangling cyclical references. A generational garbage collection cycle contains the following steps -. While everyone loves Python, it does not shy away from having memory issues. There are many possible reasons.
According to the Python 3. It is stated in the documentation that "under certain circumstances, the Python memory manager may not trigger appropriate actions, like garbage collection, memory compaction or other preventive measures.
As a result, one may have to explicitly free up memory in Python. One way to do this is to force the Python garbage collector to release unused memory by making use of the gc module. One simply needs to run gc.
This, however, only provides noticeable benefits when manipulating a very large number of objects. Apart from the occasionally erroneous nature of the Python garbage collector, especially when dealing with large datasets, several Python libraries have also been known to cause memory leaks.
Pandas , for example, is one such tool on the radar. Consider taking a look at all the memory-related issues in the pandas official GitHub repository! One obvious reason that may slip past even the keen eyes of code reviewers is that there are lingering large objects within the code which are not released. On the same note, infinitely growing data structures is another cause of concern. For example, a growing dictionary data structure without a fixed size limit.
One way to solve the growing data structure is to convert the dictionary into a list if possible and set a max size for the list. Otherwise, simply set a limit for the dictionary size and clear it whenever the limit is reached.
Now you may be wondering, how do I even detect memory issues in the first place? Additionally, many useful Python modules can help you track and trace memory issues. So what exactly is Application Performance Monitoring and how does it help in tracking down memory issues? Igor Irianto - Nov Jatin Sharma - Nov AmrDarwesh - Oct DEV Community is a community of , amazing developers We're a place where coders share, stay up-to-date and grow their careers.
Create account Log in. Twitter Facebook Github Instagram Twitch. Table of Contents Intro List Comprehension Generator Comprehension Intro Python is ultimate "getting things done" language, where you can soo easily write code and not worry too much about performance and memory. Instead, NumPy arrays store just the numbers themselves. We can see that the memory usage for creating the array was just 8MB, as we expected, plus the memory overhead of importing NumPy:.
And if memory usage is still too high, you can start looking at ways of reducing memory usage even more, like in-memory compression. Your Python batch process is using too much memory, and you have no idea which part of your code is responsible. You need a tool that will tell you exactly where to focus your optimization efforts, a tool designed for data scientists and scientists.
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