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Data Transformation Layer Engine - High-performance data transformation engine for hierarchical datasets with advanced caching and validation. Built for developers who need robust data processing with minimal overhead.

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Data Transformation Engine (Data Explorer Component)

npm version npm downloads License: MIT

Overview

The Data Explorer is a high-performance Vue component designed to handle and visualize large hierarchical datasets with an emphasis on categorical and temporal data. It implements an efficient data transformation engine with robust error handling and optimized rendering.

📦 Installation

npm install data-transformation-engine

Requirements

  • Node.js >= 14.0.0
  • npm >= 7.0.0

Data Engine Implementation Guide • Table of Contents

  1. Engine Architecture
  2. Implementation Details
  3. Data Flow Steps
  4. Optimization Techniques
  5. Error Handling
  6. Usage Patterns

Engine Architecture

High-Level Overview

graph TD A[Raw Data Input] --> B[Validation Layer] B --> C[Cache Layer] C --> D[Transform Layer] D --> E[Optimization Layer] E --> F[Output Layer] G[Error Handler] --> B G --> C G --> D G --> E 
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Core Components

  1. Validation Layer

    • Input validation
    • Type checking
    • Structure verification
    • Format validation
  2. Cache Layer

    • Memory cache
    • Cache invalidation
    • Cache hit/miss tracking
  3. Transform Layer

    • Data normalization
    • Structure optimization
    • Data merging
    • Category processing
  4. Optimization Layer

    • Memory optimization
    • Performance tuning
    • Data structure optimization
  5. Output Layer

    • Result formatting
    • Error wrapping
    • Data serialization

Implementation Details

1. Engine Factory

const createDataEngine = () => { // Private cache storage const cache = new Map(); // Private validators const validators = { isValidTerm: (term) => typeof term === 'string' && term.length > 0, isValidStream: (stream) => typeof stream === 'string' && stream.length > 0, isValidDetailed: (detailed) => Array.isArray(detailed) && detailed.length > 0, isValidMonth: (month) => /^\d{4}-\d{2}$/.test(month), isValidValue: (value) => !isNaN(value), isValidStructure: (data) => { return data && Array.isArray(data) && data.every(item => item.term && Array.isArray(item.interval) && Array.isArray(item.info) ); } }; // Private transformers const transformers = { // Merge detailed data mergeDetailed: (existing, incoming) => { const uniqueMap = new Map(); // Process existing data existing.forEach(item => { const key = `${item.month}-${item.branch}`; uniqueMap.set(key, item); }); // Merge incoming data incoming.forEach(item => { const key = `${item.month}-${item.branch}`; if (!uniqueMap.has(key) || new Date(item.month) > new Date(uniqueMap.get(key).month)) { uniqueMap.set(key, item); } }); return Array.from(uniqueMap.values()); }, // Calculate aggregates calculateAggregates: (detailed) => { return detailed.reduce((acc, curr) => { const value = Number(curr.value); return { total: acc.total + value, branches: new Set([...acc.branches, curr.branch]), months: new Set([...acc.months, curr.month]) }; }, { total: 0, branches: new Set(), months: new Set() }); }, // Normalize data structure normalizeData: (data) => { return data.map(item => ({ ...item, value: item.value ? Number(item.value) : 0, month: item.month.trim(), branch: item.branch.trim() })); } }; // Cache management const cacheManager = { get: (key) => cache.get(key), set: (key, value) => cache.set(key, value), has: (key) => cache.has(key), delete: (key) => cache.delete(key), clear: () => cache.clear(), generateKey: (data) => JSON.stringify(data) }; // Error handling const errorHandler = { wrap: (fn, errorMessage) => { try { return fn(); } catch (error) { console.error(`${errorMessage}:`, error); throw error; } }, validate: (data, validator, errorMessage) => { if (!validator(data)) { throw new Error(errorMessage); } } }; // Main processing pipeline const processRawData = (data) => { return errorHandler.wrap(() => { // 1. Input Validation errorHandler.validate( data, validators.isValidStructure, 'Invalid data structure' ); // 2. Cache Check const cacheKey = cacheManager.generateKey(data); if (cacheManager.has(cacheKey)) { return cacheManager.get(cacheKey); } // 3. Data Processing const termMap = new Map(); data.forEach(item => { // Validate term errorHandler.validate( item.term, validators.isValidTerm, `Invalid term: ${item.term}` ); // Initialize or get term data if (!termMap.has(item.term)) { termMap.set(item.term, { id: crypto.randomUUID(), term: item.term, info: new Map() }); } const termData = termMap.get(item.term); // Process info items item.info.forEach(info => { // Validate stream errorHandler.validate( info.stream, validators.isValidStream, `Invalid stream: ${info.stream}` ); // Validate detailed data errorHandler.validate( info.detailed, validators.isValidDetailed, `Invalid detailed data for stream: ${info.stream}` ); // Process existing or new info const existingInfo = termData.info.get(info.stream); if (existingInfo) { // Merge with existing data existingInfo.detailed = transformers.mergeDetailed( existingInfo.detailed, info.detailed ); // Update aggregates const aggregates = transformers.calculateAggregates(existingInfo.detailed); existingInfo.value = aggregates.total; existingInfo.uniqueBranches = aggregates.branches.size; existingInfo.monthRange = { start: Math.min(...aggregates.months), end: Math.max(...aggregates.months) }; } else { // Create new info entry const normalizedDetailed = transformers.normalizeData(info.detailed); const aggregates = transformers.calculateAggregates(normalizedDetailed); termData.info.set(info.stream, { ...info, detailed: normalizedDetailed, uniqueBranches: aggregates.branches.size, monthRange: { start: Math.min(...aggregates.months), end: Math.max(...aggregates.months) } }); } }); }); // 4. Transform to final structure const processed = Array.from(termMap.values()).map(item => ({ ...item, info: Array.from(item.info.values()).map(info => ({ ...info, detailed: info.detailed.sort((a, b) => b.month.localeCompare(a.month)) })) })); // 5. Cache result cacheManager.set(cacheKey, processed); return processed; }, 'Error processing raw data'); }; // Public API return { processRawData, clearCache: cacheManager.clear, validators, transformers }; };

Data Flow Steps

1. Input Validation Layer

// Step 1: Structure Validation errorHandler.validate( data, validators.isValidStructure, 'Invalid data structure' ); // Step 2: Term Validation errorHandler.validate( item.term, validators.isValidTerm, `Invalid term: ${item.term}` ); // Step 3: Stream Validation errorHandler.validate( info.stream, validators.isValidStream, `Invalid stream: ${info.stream}` ); // Step 4: Detailed Data Validation errorHandler.validate( info.detailed, validators.isValidDetailed, `Invalid detailed data for stream: ${info.stream}` );

2. Cache Management Layer

// Step 1: Generate Cache Key const cacheKey = cacheManager.generateKey(data); // Step 2: Check Cache if (cacheManager.has(cacheKey)) { return cacheManager.get(cacheKey); } // Step 3: Process Data const processed = processData(data); // Step 4: Store in Cache cacheManager.set(cacheKey, processed);

3. Data Transformation Layer

// Step 1: Initialize Data Structure const termMap = new Map(); // Step 2: Process Terms termMap.set(item.term, { id: crypto.randomUUID(), term: item.term, info: new Map() }); // Step 3: Process Info Items const existingInfo = termData.info.get(info.stream); if (existingInfo) { // Merge with existing data mergeData(existingInfo, info); } else { // Create new entry createNewEntry(info); } // Step 4: Calculate Aggregates const aggregates = transformers.calculateAggregates(detailed); // Step 5: Normalize and Sort const normalized = transformers.normalizeData(detailed); const sorted = normalized.sort((a, b) => b.month.localeCompare(a.month));

Optimization Techniques

1. Memory Optimization

// Use Map for O(1) lookups const termMap = new Map(); // Use Set for unique values const uniqueBranches = new Set(); // Efficient data merging const mergeDetailed = (existing, incoming) => { const uniqueMap = new Map(); // ... merging logic };

2. Performance Optimization

// Cache expensive operations const cacheKey = JSON.stringify(data); if (cache.has(cacheKey)) return cache.get(cacheKey); // Efficient data structure const termData = { id: crypto.randomUUID(), term: item.term, info: new Map() // O(1) lookup }; // Optimize sorting detailed.sort((a, b) => b.month.localeCompare(a.month));

3. Memory Management

// Clear cache when needed const clearCache = () => cache.clear(); // Remove unused data const cleanup = () => { cache.forEach((value, key) => { if (isStale(value)) cache.delete(key); }); };

Error Handling

1. Validation Errors

const validators = { isValidTerm: (term) => { if (!term || typeof term !== 'string') { throw new Error('Invalid term type'); } if (term.length === 0) { throw new Error('Term cannot be empty'); } return true; } };

2. Processing Errors

const processWithErrorHandling = (data) => { try { return processRawData(data); } catch (error) { console.error('Processing error:', error); return { error: true, message: error.message, data: null }; } };

3. Cache Errors

const safeCacheAccess = (key) => { try { return cache.get(key); } catch (error) { console.error('Cache access error:', error); cache.delete(key); // Clean up potentially corrupted cache return null; } };

Usage Patterns

1. Basic Usage

const engine = createDataEngine(); const processed = engine.processRawData(rawData);

2. With Error Handling

const engine = createDataEngine(); try { const processed = engine.processRawData(rawData); // Use processed data } catch (error) { console.error('Engine error:', error); // Handle error case }

3. With Cache Management

const engine = createDataEngine(); // Process data const result1 = engine.processRawData(data); // Same data will use cache const result2 = engine.processRawData(data); // Clear cache if needed engine.clearCache();

4. Custom Validation

const engine = createDataEngine(); const customValidator = (data) => { return engine.validators.isValidStructure(data) && engine.validators.isValidTerm(data.term); }; if (customValidator(data)) { const processed = engine.processRawData(data); }

Performance Considerations

  1. Memory Usage

    • Use Map and Set for efficient data structures
    • Implement cache size limits
    • Clean up unused data
  2. Processing Speed

    • Optimize loops and iterations
    • Use efficient sorting algorithms
    • Implement lazy loading where possible
  3. Cache Efficiency

    • Implement cache invalidation strategy
    • Monitor cache hit/miss ratio
    • Optimize cache key generation

Best Practices

  1. Data Validation

    • Always validate input data
    • Use type checking
    • Validate data structure
    • Handle edge cases
  2. Error Handling

    • Implement comprehensive error handling
    • Provide meaningful error messages
    • Log errors appropriately
    • Recover gracefully from errors
  3. Code Organization

    • Keep code modular
    • Use consistent naming
    • Document complex logic
    • Follow SOLID principles
  4. Testing

    • Unit test all components
    • Test edge cases
    • Performance testing
    • Memory leak testing

Contributing Guidelines

  1. Follow the established code style
  2. Add tests for new functionality
  3. Update documentation
  4. Consider performance implications
  5. Test edge cases thoroughly

License

MIT License - See LICENSE file for details

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Data Transformation Layer Engine - High-performance data transformation engine for hierarchical datasets with advanced caching and validation. Built for developers who need robust data processing with minimal overhead.

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