The recently introduced Shift-To-Middle Array data structure has sparked significant discussion among developers, with many pointing out critical flaws despite its promising concept. Designed as an alternative to traditional data structures like std::deque, std::vector, and linked lists, this new implementation aims to optimize insertions and deletions at both ends while maintaining contiguous memory storage.
Infinite Growth Problem Identified
The most significant criticism centers on a fundamental design flaw: the data structure grows indefinitely when used in common queue operations. As one developer pointed out in the discussion, repeatedly pushing to the head and removing from the tail—a standard queue pattern—causes the array to continuously resize even when maintaining a small, constant number of elements.
This implementation grows indefinitely if you repeatedly push to the head and remove from the tail, even if the max number of elements in the array is small.
This behavior stands in stark contrast to ring buffer implementations that handle this common use case efficiently without unnecessary memory allocation or data copying. The growth issue effectively undermines the data structure's viability for one of its primary intended applications: high-performance queues.
Key Features of Shift-To-Middle Array
- Amortized O(1) insertions & deletions at both ends
- Fast random access (O(1))
- Better cache locality than linked lists
- Supports SIMD & parallel optimizations
- Contiguous memory storage (unlike std::deque's fragmented blocks)
Main Criticisms
- Grows indefinitely with queue-like operations (push head, pop tail)
- Issues with non-trivial types (destructors, move constructors)
- Unnecessary copying compared to ring buffer implementations
- Lacks efficient random delete operations
- Missing iterator support for standard algorithm compatibility
Implementation Concerns for Non-Trivial Types
Several developers noted that the current C++ implementation would face problems with non-trivial types. Objects with destructors or move constructors (like std::unique_ptr) would likely cause issues since the implementation appears to operate on raw memory. One commenter suggested adding a static assertion to at least limit usage to trivially copyable types, highlighting the implementation's current limitations.
Comparison to Existing Solutions
The community discussion revealed interesting insights about alternative implementations. Some pointed out that similar approaches already exist, including Apple's CoreFoundation CFArray and Boost's devector. Others questioned whether the new structure offers meaningful advantages over established solutions like VecDeque (a ring buffer implementation).
Several developers shared their own implementations of similar data structures, suggesting the concept itself has merit but requires careful consideration of edge cases and performance characteristics.
Performance Tradeoffs
While the Shift-To-Middle Array promises better cache locality than linked lists and efficient operations at both ends, the discussion highlighted important performance tradeoffs. The structure's approach of shifting elements to the middle during resizing involves copying that a straightforward ring buffer avoids.
Some developers suggested that indexing a ring buffer might be slightly more costly due to the need for a branch or modulus operation to handle wraparound, but others noted that such small differences often disappear due to instruction pipelining. Without comprehensive benchmarks comparing common use cases, it's difficult to determine which approach performs better in practice.
Alternative Approaches
The discussion thread revealed several alternative approaches to solving similar problems. One developer described a double-ended vector that maintains free space at both ends, storing just a pointer and three lengths (total size of 32 bytes compared to Vec's usual 24 bytes). Another mentioned using memory mapping tricks to create ring buffers with contiguous views, though this approach has its own limitations related to page size and system call overhead.
The Shift-To-Middle Array represents an interesting addition to the data structure landscape, but its current implementation falls short for common use cases. As with many specialized data structures, the optimal choice depends heavily on the specific application requirements, access patterns, and performance characteristics. Developers considering this structure should carefully evaluate whether its benefits outweigh the identified limitations, particularly the concerning growth behavior for queue-like operations.
Reference: Shift-To-Middle Array