理解buffer pool in computer

内存池是一种内存管理技术,常用于实时操作系统中,通过预先分配固定大小的内存块,提高动态内存分配的效率。缓冲区则是在数据传输过程中临时存储数据的内存区域,常见于硬件实现。而缓冲池是数据库管理系统为加速数据读取,从主内存中预留的一部分空间,用于缓存磁盘上的表格和索引数据。这些技术在提升系统性能和资源利用率上发挥关键作用。
  • Memory pool

    Memory pools (also called fixed-size blocks allocation) is the use of pools for memory management that allows dynamic memory allocation comparable to malloc or C++'s operator new.

    Many real-time operating systems use memory pools.

  • Pool

    A pool is a collection of resources that are kept ready to use, rather than acquired on use and released afterwards.

  • Data buffer

    Data buffer (or just buffer) is a region of a physical memory storage used to temporarily store data while it is being moved from one place to another.

    Buffers can be implemented in a fixed memory location in hardware

  • Buffer pool

    A buffer pool is an area of main memory that has been allocated by the database manager for the purpose of caching table and index data as it is read from disk.

  • References

  1. IBM Db2
File "c:\users\personal computer\.spyder-py3\.zonghetu\天擎逐小时数据筛选暴雨.py", line 92, in batch_read_files chunk_reader = pd.read_csv( File "C:\Users\Personal Computer\.conda\envs\metdig_app\lib\site-packages\pandas\util\_decorators.py", line 211, in wrapper return func(*args, **kwargs) File "C:\Users\Personal Computer\.conda\envs\metdig_app\lib\site-packages\pandas\util\_decorators.py", line 331, in wrapper return func(*args, **kwargs) File "C:\Users\Personal Computer\.conda\envs\metdig_app\lib\site-packages\pandas\io\parsers\readers.py", line 950, in read_csv return _read(filepath_or_buffer, kwds) File "C:\Users\Personal Computer\.conda\envs\metdig_app\lib\site-packages\pandas\io\parsers\readers.py", line 605, in _read parser = TextFileReader(filepath_or_buffer, **kwds) File "C:\Users\Personal Computer\.conda\envs\metdig_app\lib\site-packages\pandas\io\parsers\readers.py", line 1427, in __init__ options = self._get_options_with_defaults(engine) File "C:\Users\Personal Computer\.conda\envs\metdig_app\lib\site-packages\pandas\io\parsers\readers.py", line 1500, in _get_options_with_defaults raise ValueError( ValueError: The 'low_memory' option is not supported with the 'python' engine Traceback (most recent call last): File "c:\users\personal computer\.spyder-py3\.zonghetu\天擎逐小时数据筛选暴雨.py", line 92, in batch_read_files chunk_reader = pd.read_csv( File "C:\Users\Personal Computer\.conda\envs\metdig_app\lib\site-packages\pandas\util\_decorators.py", line 211, in wrapper return func(*args, **kwargs) File "C:\Users\Personal Computer\.conda\envs\metdig_app\lib\site-packages\pandas\util\_decorators.py", line 331, in wrapper return func(*args, **kwargs) File "C:\Users\Personal Computer\.conda\envs\metdig_app\lib\site-packages\pandas\io\parsers\readers.py", line 950, in read_csv return _read(filepath_or_buffer, kwds) File "C:\Users\Personal Computer\.conda\envs\metdig_app\lib\site-packages\pandas\io\parsers\readers.py", line 605, in _read parser = TextFileReader(filepath_or_buffer, **kwds) File "C:\Users\Personal Computer\.conda\envs\metdig_app\lib\site-packages\pandas\io\parsers\readers.py", line 1427, in __init__ options = self._get_options_with_defaults(engine) File "C:\Users\Personal Computer\.conda\envs\metdig_app\lib\site-packages\pandas\io\parsers\readers.py", line 1500, in _get_options_with_defaults raise ValueError( ValueError: The 'low_memory' option is not supported with the 'python' engine An exception has occurred, use %tb to see the full traceback. SystemExit: 1
最新发布
11-08
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