Sovereign DSA Registry (`alnoms.dsa`)

The Sovereign DSA Registry is the "Single Source of Truth" for algorithmic metadata within the Alnoms ecosystem. It acts as a specialized pharmacy, providing the precise "Complexity Profiles" required by the Analyzer and Decision Engine to issue performance verdicts.

🧭 1. Philosophy: The Algorithmic Pharmacy

In Alnoms, data structures are not just code; they are Remedies. Every entry in this registry is mapped to a specific Big-O complexity class, ensuring that when the engine recommends a "Cure," it is backed by mathematical certainty.

Standardized Profiles: Unified metadata for time complexity, access tiers, and implementation paths.
Tier-Awareness: Transparent classification of OSS, PRO, and ENTERPRISE algorithms.
Conceptual Stubs: Educational placeholders for optimization strategies like "Pruning" and "Memoization."

📊 2. Algorithmic Profiles (OSS Tier)

Below are the foundational remedies available in the Open Source tier. These form the core of the Applied Data Intelligence curriculum.

🛠️ Data Structures

Identifier	Complexity	Category	Role
`separate_chaining_hash`	\(O(N/M)\)	structure	Industrial-grade collision-resistant lookup.
`bst_search`	\(O(\log N)\) Avg	structure	Efficient ordered data retrieval.
`graph`	\(O(V + E)\)	structure	Base for network and relationship modeling.
`stack` / `queue`	\(O(1)\)	structure	Fundamental low-latency collections.

⚡ Sorting & Searching

Identifier	Complexity	Category	Role
`merge_sort`	\(O(N \log N)\)	sorting	Stable, predictable high-volume sorting.
`binary_search`	\(O(\log N)\)	searching	Logarithmic lookup in ordered arrays.
`dijkstra`	\(O(E \log V)\)	graph	Shortest path calculation in weighted graphs.

⚙️ 3. Optimization Strategies

Beyond traditional structures, the registry tracks conceptual strategies used to fix high-level performance regressions:

memoization: Used by the Expensive Call Fixer to eliminate redundant \(O(2^N)\) recursive branches.
list_concat: Promotes \(O(N)\) string/list building over \(O(N^2)\) in-place addition.
buffered_io: Prevents IO-bound bottlenecks by batching operations in memory.

🏢 4. Tiered Architecture

The registry scales with your engineering requirements, providing more advanced "Sovereign Remediation" as you move through the tiers:

OSS (Tier 0): Essential DSA for 90% of performance bottlenecks.
PRO (Tier 1/2): High-performance structures like red_black_bst (guaranteed \(O(\log N)\)) and linear_probing_hash.
ENTERPRISE (Tier 3): Specialized industrial algorithms like bellman_ford and shell_sort.

🧪 5. Internal Mechanics: Metadata Retrieval

The MetadataRegistry provides an \(O(1)\) lookup for any algorithm identifier. When the Decision Engine selects a fix, it queries this registry to enrich the final report:

# Internal Lookup Example
metadata = alnoms.dsa.metadata.MetadataRegistry.get_metadata("merge_sort")

# Returns:
# {
#    "complexity": "O(n log n)",
#    "tier": "OSS",
#    "category": "sorting",
#    "module": "dsa.algorithms.sorting.MergeSort"
# }

🧭 6. Design Principles

Industrial Consistency: Ensures that the Analyzer, CLI, and educational modules all use the same Big-O definitions across the entire ecosystem.
Sovereign Implementation: Every module path points to a highly-optimized Python implementation designed to reduce the "Cognitive Tax" on the end-user.
Transparency: Users can verify the mathematical complexity and tier-access of any recommended fix directly from the registry.

👉 Next Step: Return to the Analyzer Architecture to see how this metadata is used to generate governance reports.

Sovereign DSA Registry (alnoms.dsa)