mmap() is typically faster than traditional sequential I/O (e.g., using the read() and write() functions) for the following reasons:
1. Reduces data copying operations
mmap() maps the file directly into the process's address space, allowing the application to read and write directly to this memory without system calls. Unlike traditional sequential I/O, where data is first read into the kernel buffer and then copied to the user space buffer, this 'double copy' operation is avoided with mmap().
2. Leverages the advantages of the virtual memory system
By utilizing the operating system's virtual memory system (VMS), mmap() efficiently manages large memory blocks and leverages the page fault mechanism to load file content on demand. This avoids loading the entire file into memory at once, effectively utilizing system resources and improving access efficiency.
3. Improves cache utilization
Since the memory region mapped by mmap() can be cached by the operating system, multiple accesses to the same file can directly read from the cache without re-reading from disk. This is significantly faster than traditional sequential I/O, where each operation may require disk reads.
4. Supports random access
Although we are comparing with sequential I/O, it's worth noting that mmap() also supports efficient random access. Reading parts of the file does not require starting from the beginning; it can directly access any position. This is very useful for applications that need to access specific parts of large data files.
Example
Suppose we have a large log file that requires frequent read and write operations. Using traditional read() and write() methods, each read/write operation involves data copying between user and kernel space, as well as potential multiple disk I/O operations. With mmap(), the file content can be mapped into the process address space, and subsequent operations are treated as reading/writing ordinary memory, greatly reducing the complexity and time overhead of I/O operations.
Summary In summary, mmap() provides faster data processing capabilities for specific applications by optimizing data copy steps, efficiently utilizing memory and cache, and reducing unnecessary system calls. Of course, its best use cases are typically when files are large and access patterns are complex (e.g., frequent random access or high concurrency).