用Python将大文件加载到内存中
在使用大型文件和数据集时，我遇到困难，通常从1到2 GB甚至更大。我面临的主要挑战是由于可用的RAM耗尽而被杀死的过程。我需要在这些文件上执行各种操作，例如在整个数据集上迭代，将值访问和分配给大变量，并在整个过程中维护对整个文件的读取访问。
我正在寻求有关工具，技术和最佳实践的建议，这些建议可以帮助我有效地管理内存使用情况，同时仍然能够执行这些必要的功能。我想确保我可以在不遇到内存限制的情况下处理整个数据集。
我想指导的一些特定要点是：
有效的迭代：如何在不立即将整个文件加载到内存中的情况下，在大文件或数据集上有效迭代？是否有任何库或方法可以流式传输或部分加载数据？
内存优化技术：是否有任何特定的技术或策略可以在使用大型文件时减少内存消耗？如何优化数据结构和算法以最大程度地减少内存使用情况？
外部内存处理：是否有任何工具或方法可以通过利用外部内存或基于磁盘的存储来促进大型文件？我如何利用这些技术克服RAM限制？
压缩和块：是否可以有效利用文件压缩技术来减少内存足迹？如何将大文件分为较小的，可管理的块以进行处理？
并行处理：是否有任何机会使处理任务可以在多个内核或机器上分配内存负载？我如何利用并行计算的功能来优化内存用法？
我将感谢任何建议，代码示例或推荐的库，可以帮助解决这些与内存有关的挑战。预先感谢您的宝贵见解和专业知识！
I am encountering difficulties while working with large files and datasets, typically ranging from 1 to 2 GB or even larger. The main challenge I face is the process being killed due to running out of available RAM. I need to perform various operations on these files, such as iterating over the entire dataset, accessing and assigning values to large variables, and maintaining read access to the entire file throughout the process.
I am seeking advice on tools, techniques, and best practices that can help me effectively manage memory usage while still being able to perform these necessary functions. I want to ensure that I can process the entire dataset without running into memory limitations.
Some specific points I would like guidance on are:
Efficient Iteration: How can I efficiently iterate over a large file or dataset without loading the entire file into memory at once? Are there any libraries or methods that allow for streaming or partial loading of data?
Memory Optimization Techniques: Are there any specific techniques or strategies that can be employed to reduce memory consumption while working with large files? How can I optimize data structures and algorithms to minimize memory usage?
External Memory Processing: Are there any tools or approaches that facilitate processing large files by utilizing external memory or disk-based storage? How can I leverage these techniques to overcome the RAM limitations?
Compression and Chunking: Can file compression techniques be utilized effectively to reduce the memory footprint? How can I divide the large file into smaller, manageable chunks for processing?
Parallel Processing: Are there any opportunities for parallelizing the processing tasks to distribute the memory load across multiple cores or machines? How can I harness the power of parallel computing to optimize memory usage?
I would appreciate any suggestions, code examples, or recommended libraries that can assist in resolving these memory-related challenges. Thank you in advance for your valuable insights and expertise!
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通常，您可以使用内存 - 莫普尔文件所以不要映射虚拟的一部分存储设备中的内存。这使您能够在不适合RAM的内存映射空间上操作。请注意，这比RAM慢得多（没有免费午餐）。您可以使用numpy用 numpy.memmap 。另外，有 mmap 。为了表现出色，您可以在读取的块上操作一次，从内存映射的部分中写下它们。
Generally, you can use memory-mapped files so not to map a section of a virtual memory in a storage device. This enable you to operate on a use space of memory mapped that would not fit in RAM. Note that this is significantly slower than RAM though (there is no free lunch). You can use Numpy to do that quite transparently with numpy.memmap. Alternatively, there is mmap. For sake of performance, you can operate on chunks on read write them once from the memory-mapped section.