Taxonomy Distribution Report

Overview

• Total Concepts: 100
• Number of Taxonomies: 9
• Average Concepts per Taxonomy: 11.1

Distribution Summary

Category	TaxonomyID	Count	Percentage	Status
Spec Model	SPEC	15	15.0%	✅
Flatten Engine	FLAT	14	14.0%	✅
Foundation Concepts	FOUND	12	12.0%	✅
Batch Processing	BATCH	12	12.0%	✅
Format Handlers	FMT	12	12.0%	✅
Build Engine	BUILD	11	11.0%	✅
Schema Handling	SCHEMA	11	11.0%	✅
Type System	TYPES	8	8.0%	✅
Hooks System	HOOKS	5	5.0%	✅

Visual Distribution

Spec Model          ███████  15 ( 15.0%)
Flatten Engine      ███████  14 ( 14.0%)
Foundation Concepts ██████  12 ( 12.0%)
Batch Processing    ██████  12 ( 12.0%)
Format Handlers     ██████  12 ( 12.0%)
Build Engine        █████  11 ( 11.0%)
Schema Handling     █████  11 ( 11.0%)
Type System         ████   8 (  8.0%)
Hooks System        ██   5 (  5.0%)

Balance Analysis

✅ No Over-Represented Categories

All categories are under the 30% threshold. Good balance!

Category Details

Spec Model (SPEC)

Count: 15 concepts (15.0%)

Concepts:

• 1. TreeSpec Dataclass
• 1. MetaConfig
• 1. SettingsConfig
• 1. NodeSpec Dataclass
• 1. ChildSpec Dataclass
• 1. RoutingConfig
• 1. RoutingRule
• 1. PivotEntry
• 1. BatchingConfig
• 1. DedupConfig
• 1. DedupTableConfig
• 1. SourceColumn
• 1. StreamingConfig
• 1. Spec Loader
• 1. Spec Validator

Flatten Engine (FLAT)

Count: 14 concepts (14.0%)

Concepts:

• 1. TreeFlattener Class
• 1. Flatten Method
• 1. Flatten Many Method
• 1. Tree Walk Algorithm
• 1. Field Extraction
• 1. Inline Child Dispatch
• 1. Separate Table Dispatch
• 1. Routed Child Dispatch
• 1. Key Propagation
• 1. Synthetic Key Generation
• 1. Column Naming Convention
• 1. Snake Case Naming
• 1. Prefixed Name Builder
• 1. Null Handling Strategy

Foundation Concepts (FOUND)

Count: 12 concepts (12.0%)

Concepts:

• 1. Hierarchical Data
• 1. Relational Tables
• 1. Tree to Table Decomposition
• 1. Round Trip Fidelity
• 1. Python Dataclasses
• 1. Python Protocols
• 1. Polars DataFrames
• 1. PyArrow Tables
• 1. Foreign Keys
• 1. Primary Keys
• 1. YAML Configuration
• 1. Decorator Pattern

Batch Processing (BATCH)

Count: 12 concepts (12.0%)

Concepts:

• 1. BatchFlattener Class
• 1. BatchBuilder Class
• 1. Multi File Processing
• 1. Source Column Injection
• 1. Source Column Extraction
• 1. Vertical Concat
• 1. Horizontal Concat
• 1. Column Alignment
• 1. Apply Dedup
• 1. Cascade Dedup
• 1. First Wins Strategy
• 1. Partition Key

Format Handlers (FMT)

Count: 12 concepts (12.0%)

Concepts:

• 1. JSON Schema Handler
• 1. Dataclass Field Introspection
• 1. XML Schema Handler
• 1. Xsdata Integration
• 1. XML Streaming Handler
• 1. XML Parser
• 1. XML Serializer
• 1. Protobuf Schema Handler
• 1. Protobuf Parser
• 1. Pydantic Schema Handler
• 1. Avro Schema Handler
• 1. Avro Codegen

Build Engine (BUILD)

Count: 11 concepts (11.0%)

Concepts:

• 1. TreeBuilder Class
• 1. Build Method
• 1. Build Many Method
• 1. FK Pre Indexing
• 1. Inverse Walk Algorithm
• 1. Node Construction
• 1. Handler Based Construct
• 1. Class Registry Lookup
• 1. Child Reassembly
• 1. Inline Child Rebuild
• 1. Separate Table Rebuild

Schema Handling (SCHEMA)

Count: 11 concepts (11.0%)

Concepts:

• 1. SchemaHandler Protocol
• 1. SchemaFieldInfo Protocol
• 1. SchemaHandlerRegistry
• 1. Default Registry
• 1. Handler Registration
• 1. Can Handle Check
• 1. Get Fields Method
• 1. Get Source Name Method
• 1. Get Cardinality Method
• 1. Get Choice Groups
• 1. To FieldSpec Conversion

Type System (TYPES)

Count: 8 concepts (8.0%)

Concepts:

• 1. UniversalType Enum
• 1. Python Type to Universal Map
• 1. Xsdata Type Names Map
• 1. Resolve Universal Type
• 1. Universal to PyArrow Map
• 1. Arrow Schema Builder
• 1. FieldSpec Bridge
• 1. ResolvedField Dataclass

Hooks System (HOOKS)

Count: 5 concepts (5.0%)

Concepts:

• 1. HookRegistry Class
• 1. Condition Hook
• 1. Field Transform Hook
• 1. Pre Flatten Hook
• 1. Post Build Hook

Recommendations

• ✅ Excellent balance: Categories are evenly distributed (spread: 10.0%)
• ✅ MISC category minimal: Good categorization specificity

Educational Use Recommendations

• Use taxonomy categories for color-coding in graph visualizations
• Design curriculum modules based on taxonomy groupings
• Create filtered views for focused learning paths
• Use categories for assessment organization
• Enable navigation by topic area in interactive tools

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Report generated by learning-graph-reports/taxonomy_distribution.py