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Optimising RAM Usage with Python.

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Titel: Optimising RAM Usage with Python.
Autoren: Arichandrapandian, Thangaselvi
Quelle: Open Source For You; Mar2026, Vol. 14 Issue 5, p82-85, 4p
Schlagwörter: DATA structures, COMPUTER memory management, PROGRAM transformation, DATA management, SOFTWARE development tools
Abstract: The article focuses on optimizing RAM usage in Python programs by employing various measurement tools and memory-efficient coding techniques. It reviews methods to measure memory consumption, including Python’s built-in sys.getsizeof, tracemalloc for detailed allocation tracing, and the third-party memory_profiler for line-by-line memory usage. The article also discusses selecting appropriate data structures—such as arrays, tuples, sparse matrices, and specialized tries like Marisa-Trie and Double-Array Trie—to reduce memory footprint, especially when handling large datasets or text. Additional recommendations include avoiding unnecessary data copies, using generators, leveraging NumPy for numeric data, and applying profiling to identify memory issues, all aimed at improving performance and scalability in Python applications. [Extracted from the article]
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Datenbank: Complementary Index
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Abstract:The article focuses on optimizing RAM usage in Python programs by employing various measurement tools and memory-efficient coding techniques. It reviews methods to measure memory consumption, including Python’s built-in sys.getsizeof, tracemalloc for detailed allocation tracing, and the third-party memory_profiler for line-by-line memory usage. The article also discusses selecting appropriate data structures—such as arrays, tuples, sparse matrices, and specialized tries like Marisa-Trie and Double-Array Trie—to reduce memory footprint, especially when handling large datasets or text. Additional recommendations include avoiding unnecessary data copies, using generators, leveraging NumPy for numeric data, and applying profiling to identify memory issues, all aimed at improving performance and scalability in Python applications. [Extracted from the article]
ISSN:24564885