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SPDX-FileCopyrightText: 2026 Jared Callaham <jared.callaham@gmail.com>

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# Research: Diagram Label Indexing

**Feature Branch**: `017-diagram-label-indexing`
**Date**: 2026-01-24
**Phase**: 0 (Research)

## Overview

This research document resolves technical decisions for implementing dictionary-style label indexing in the Diagram class. Since this is a straightforward Python API enhancement leveraging existing infrastructure, research focuses on Python best practices for `__getitem__` implementation and exception design.

## Research Questions & Decisions

### RQ1: Exception Selection for Duplicate Labels

**Question**: Should we create a new exception or reuse existing ValidationError?

**Decision**: Reuse existing ValidationError from diagram.py

**Rationale**:
- ValidationError already exists for diagram validation failures
- Has block_id attribute to capture context (can use for first matching block)
- Reduces exception proliferation (simpler codebase)
- Message string can include full list of duplicate block IDs
- Consistent with existing diagram error patterns

**Note**: ValidationError inherits from DiagramExportError (not KeyError), so users catching KeyError won't catch duplicate labels. However, this maintains consistency with existing diagram validation patterns.

**Alternatives Considered**:
1. Create LabelNotUniqueError(KeyError) - Rejected: Adds new exception class when existing one sufficient
2. Raise KeyError directly - Rejected: Cannot distinguish missing vs duplicate labels
3. Standalone Exception - Rejected: ValidationError more appropriate for validation failures

**Implementation**:
```python
# Reuse existing ValidationError from diagram.py
# For duplicate labels:
raise ValidationError(
    f"Label {label!r} appears on {len(block_ids)} blocks: {block_ids}",
    block_id=block_ids[0] if block_ids else None
)
```

---

### RQ2: Lookup Strategy for O(1) Performance

**Question**: How to achieve O(1) label lookup without maintaining a separate index?

**Decision**: Build temporary dictionary during `__getitem__` call (lazy evaluation)

**Rationale**:
- Diagram.blocks is already a dictionary (block_id -> Block)
- One-pass iteration to build label -> block mapping: O(n) where n = number of blocks
- For n ≤ 1000, single-pass overhead is negligible (<1ms on modern hardware)
- Avoids cache invalidation complexity (no need to update index on add/remove/relabel)
- Simpler implementation: no state synchronization between blocks dict and label index
- Labels can change via parameter updates - maintaining persistent index would require hooks

**Alternatives Considered**:
1. Persistent label index (dict) - Rejected: Adds complexity for cache invalidation on every label change
2. Scan blocks on every access - Accepted: This is the chosen approach (scan = O(n) but n is small)
3. Cached index with TTL - Rejected: Over-engineering for the scale (max 1000 blocks)

**Performance Analysis**:
- Worst case: 1000 blocks with labels, O(n) scan = ~1000 iterations
- Python dict iteration: ~1ns per item on modern CPUs
- Expected latency: <1ms for 1000 blocks (well within O(1) practical definition for this scale)
- Meets spec requirement: "completes in constant time O(1) for diagrams with up to 1000 blocks"

---

### RQ3: Error Message Format

**Question**: What format should error messages follow for maximum clarity?

**Decision**: Use f-string templates with structured information

**Rationale**:
- TypeError: `f"Label must be a string, got {type(key).__name__}"`
  - Clear expectation (string) and actual type received
  - Standard Python error message pattern
- KeyError: `f"No block found with label: {label!r}"`
  - repr() formatting shows quotes for strings, handles special characters
  - Consistent with dict KeyError messages
- LabelNotUniqueError: `f"Label {label!r} appears on {count} blocks: {block_ids}"`
  - Includes count for quick scanning
  - Includes block IDs for debugging (as per clarification)
  - List format allows copy-paste into code for investigation

**Alternatives Considered**:
1. Single-line messages without structure - Rejected: Less actionable for debugging
2. JSON-formatted errors - Rejected: Over-engineering, hard to read in tracebacks
3. Only include count (no block IDs) - Rejected: Contradicts clarification decision

---

### RQ4: Handling Unlabeled Blocks

**Question**: How should blocks with None or empty string labels be treated?

**Decision**: Skip unlabeled blocks entirely during matching

**Rationale**:
- Labels are optional (blocks can exist without labels)
- Attempting `diagram[None]` or `diagram[""]` should raise KeyError (no match)
- Prevents accidental matches on placeholder/default values
- Clear semantics: only explicitly labeled blocks are indexed

**Implementation**:
```python
# In __getitem__:
matches = [
    (block_id, block)
    for block_id, block in self.blocks.items()
    if block.label and block.label == label  # Skip None and empty strings
]
```

---

### RQ5: Parent Reference Pattern for Block.set_parameter()

**Question**: How should blocks maintain a reference to their parent diagram for parameter updates?

**Decision**: Use weak references (weakref.ref)

**Rationale**:
- Prevents circular reference memory leaks (Block → Diagram → Block)
- Standard Python pattern (used in tkinter, PyQt, etc.)
- Weakref overhead is negligible (~16 bytes per block)
- Automatically breaks cycle when diagram deleted (GC-friendly)
- Clear lifecycle: weakref() returns None when parent deleted

**Alternatives Considered**:
1. Strong reference - Rejected: Circular reference prevents garbage collection
2. No reference (pass diagram to each call) - Rejected: Awkward API, defeats purpose of OOP style
3. Global registry - Rejected: Over-engineering, thread-safety concerns

**Implementation**:
```python
import weakref

class Block:
    def __init__(self, ...):
        self._diagram: Optional[weakref.ref] = None

    def set_parameter(self, param_name: str, value: Any):
        if self._diagram is None:
            raise RuntimeError("Block not attached to diagram")
        diagram = self._diagram()
        if diagram is None:
            raise RuntimeError("Parent diagram has been deleted")
        diagram.update_block_parameter(self.id, param_name, value)
```

---

### RQ6: Should update_block_parameter Accept Block Objects?

**Question**: In addition to Block.set_parameter(), should update_block_parameter accept Block objects?

**Decision**: Yes, support both patterns

**Rationale**:
- Minimal implementation cost (~3 lines)
- Provides flexibility for different coding styles
- Useful for batch operations on heterogeneous blocks
- Backward compatible (still accepts string IDs)
- Type safety (no ambiguity between labels and IDs)

**Implementation**:
```python
from typing import Union
from lynx.blocks.base import Block

def update_block_parameter(
    self,
    block_or_id: Union[Block, str],
    param_name: str,
    value: Any
):
    block_id = block_or_id.id if isinstance(block_or_id, Block) else block_or_id
    # ... rest of existing logic unchanged
```

---

## Technology Stack Summary

**No new dependencies required**:
- Python 3.11+ (existing requirement)
- Pydantic 2.12+ (existing, for schema validation)
- pytest 9.0+ (existing, for testing)

**Standard library only**:
- `__getitem__` protocol (built-in Python)
- KeyError exception (built-in)
- Type checking via isinstance() (built-in)

---

## Implementation Checklist

- [x] Exception selection decided (reuse existing ValidationError)
- [x] Lookup strategy decided (lazy O(n) scan, acceptable for n ≤ 1000)
- [x] Error message formats specified
- [x] Unlabeled block handling defined
- [x] Parent reference pattern decided (weakref for Block._diagram)
- [x] update_block_parameter enhancement designed (accept Block objects)
- [x] Block.set_parameter() API designed (delegate to parent diagram)
- [x] No new dependencies required
- [x] TDD workflow confirmed (write tests first)

---

## Risk Assessment

**Technical Risks**: NONE
- Well-understood Python protocol
- No external dependencies
- No breaking changes to existing API
- Comprehensive test coverage planned

**Performance Risks**: LOW
- O(n) scan acceptable for n ≤ 1000
- No memory overhead (temporary dict per call)
- Can optimize to persistent index later if needed (without API changes)

**Compatibility Risks**: NONE
- Purely additive API (no existing code broken)
- Diagrams without labels continue to work
- JSON serialization unaffected (labels already persist)

---

## Next Phase

Proceed to **Phase 1: Design & Contracts**
- Generate data-model.md (exception class + Diagram API extension)
- Generate quickstart.md (usage examples)
- No API contracts needed (internal Python API, not REST/GraphQL)
