Rust Region Inference: Locating Data Boundaries

This challenge focuses on developing a system in Rust that can infer the "region" or scope of data based on its access patterns. This is a fundamental concept in memory management and performance optimization, allowing for more efficient resource allocation and analysis. Your task is to build a mechanism that identifies contiguous blocks of memory that are accessed together.

Problem Description

You are tasked with creating a Rust function, infer_regions, that takes a sequence of memory access operations and identifies distinct, contiguous regions of memory that are frequently accessed together. A memory access operation is represented by a tuple (address, operation_type), where address is a usize representing the memory location, and operation_type is an enum OperationType (e.g., Read, Write).

The goal is to group these access operations into "regions." A region is defined by a contiguous range of memory addresses that exhibit a pattern of access. We can infer these regions by looking for clusters of accesses within a certain address span.

Key Requirements:

1. Region Identification: Identify contiguous blocks of memory where operations are concentrated.
2. Region Merging: If two identified regions are sufficiently close and share access patterns, they should be merged into a single, larger region.
3. Region Output: The function should return a Vec of tuples, where each tuple represents a region: (start_address, end_address, count), where count is the total number of access operations within that region. The regions should be sorted by their start_address.

Expected Behavior:

• The function should process a stream of memory accesses.
• It should group accesses that fall within a specified region_size_threshold.
• Consecutive or overlapping identified regions whose total span is within a merge_threshold should be merged.

Edge Cases to Consider:

• Empty input vector of accesses.
• Accesses that fall outside any inferred region.
• Scenarios where region_size_threshold is very small or very large.
• Scenarios where merge_threshold is very small or very large.

Examples

Example 1:

Input: accesses = [(100, OperationType::Read), (101, OperationType::Write), (105, OperationType::Read), (200, OperationType::Write), (202, OperationType::Read), (203, OperationType::Read)], region_size_threshold = 10, merge_threshold = 20
Output: [(100, 105, 3), (200, 203, 3)]
Explanation:
- Accesses at 100, 101, and 105 are within a span of 10 addresses. They form the first region.
- Accesses at 200, 202, and 203 are also within a span of 10 addresses. They form the second region.
- These two regions are separated by more than the merge_threshold, so they are not merged.

Example 2:

Input: accesses = [(100, OperationType::Read), (102, OperationType::Write), (108, OperationType::Read), (115, OperationType::Write), (120, OperationType::Read)], region_size_threshold = 10, merge_threshold = 20
Output: [(100, 120, 5)]
Explanation:
- Initially, accesses at 100, 102, and 108 might form a region.
- Accesses at 115 and 120 might form another.
- However, the span between the end of the first potential region (108) and the start of the second (115) is small (7). When considering the overall span from 100 to 120, it's within the merge_threshold. Therefore, all accesses are consolidated into a single region.

Example 3: (Edge Case: Dispersed Accesses)

Input: accesses = [(10, OperationType::Read), (50, OperationType::Write), (100, OperationType::Read)], region_size_threshold = 5, merge_threshold = 10
Output: [(10, 10, 1), (50, 50, 1), (100, 100, 1)]
Explanation:
- Each access is too far from others to form a region of size 5.
- Even with a merge threshold of 10, no adjacent regions can be formed.
- Each access is treated as its own minimal region.

Constraints

• The number of memory access operations can range from 0 to 1,000,000.
• Memory addresses (usize) will be non-negative and fit within standard usize limits.
• region_size_threshold will be a positive integer between 1 and 1000.
• merge_threshold will be a positive integer between 1 and 2000.
• The solution should aim for a time complexity that can handle the maximum number of operations efficiently, ideally closer to O(N log N) or O(N) if possible, where N is the number of accesses.

Notes

• The OperationType enum should be defined by you, but Read and Write are sufficient for this problem.
• Consider how to group initial potential regions before applying the merge logic. A common approach is to iterate through sorted accesses and form initial clusters.
• Think about the data structures that would be most efficient for storing and manipulating identified regions before and after merging.
• The definition of "contiguous" for region_size_threshold implies that if min_address and max_address are the lowest and highest addresses in a group, then max_address - min_address + 1 <= region_size_threshold. However, for simplicity in this challenge, we can consider max_address - min_address <= region_size_threshold.
• Similarly, for merging, if region A ends at end_A and region B starts at start_B, they can be merged if start_B - end_A <= merge_threshold.