The `bytesparse` library provides utilities for managing sparse bytes within a virtual memory space. It offers an interface similar to Python's built-in `bytearray`, allowing for non-contiguous data allocation across a potentially infinite addressing space. Data chunks are stored internally using mutable `bytearray` objects. The library is currently at version 1.1.0 and exhibits an active release cadence, with several updates in the past year.
pip install bytesparseVerified import paths — ran on the pinned version, not inferred.
This quickstart demonstrates how to initialize `bytesparse` and `Memory` objects, perform basic read/write operations, and store data sparsely at arbitrary addresses. It shows how `bytesparse` can be initialized from bytes and how `poke` can be used for non-contiguous writes.
Always use the pure Python `bytesparse` if infinite or negative addressing is required. If using `cbytesparse`, be mindful of its address space limitations.
For critical performance paths, benchmark both the pure Python and Cython implementations with your specific workload. Consider alternative specialized memory management solutions if `cbytesparse` does not meet performance targets.
Consult the official documentation for specific method complexities. Be mindful of potential performance implications when performing operations that would be highly optimized on contiguous data structures (e.g., slicing large, sparse regions).
Encode strings to bytes before passing them to `bytesparse` methods (e.g., `my_string.encode('utf-8')`) or use byte literals (e.g., `b'hello'`).To retrieve only the physically stored data blocks, use `bytesparse_obj.to_blocks()`. To get the span of the *allocated* memory, use `bytesparse_obj.span()`. Understand that `len()` represents the virtual length across the entire addressable range, not the compact size.
For better performance, try to coalesce writes into larger contiguous blocks when possible. If highly fragmented writes are unavoidable, periodically analyze the internal block structure (e.g., using `bytesparse_obj.to_blocks()`) to understand the overhead. The library is optimized for sparse data, but extreme fragmentation is a general performance consideration for such structures.