🔢 How do you combine or manipulate data in your projects? #3
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Combinare is a TypeScript library focused on combinatorics, permutations, and functional utilities for data manipulation. I’d love to hear from you: What are the most common cases where you need to combine arrays, sets, or objects? Do you often need utilities for permutations, combinations, or functional patterns? Are there features you wish existed in existing libraries (like Lodash, Ramda, etc.) that Combinare could bring in a lightweight way? Feel free to share use cases, feature requests, or even “dream utilities” that would make your daily development easier. 🚀 |
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Combinare Library - Use Cases and Feature SuggestionsCommon Use Cases I EncounterData Analysis & Testing
E-commerce & Product Management
Game Development & Simulations
API & Data Processing
Features Missing from Existing LibrariesPerformance-Focused Combinatorics// Dream utility: Lazy evaluation for large sets
combinare.lazy([1,2,3,4,5]).combinations(3).take(100); // Only compute first 100
combinare.stream(largeDataset).permutations().filter(condition).collect();Constraint-Based Generation// Generate combinations with business rules
combinare.constrained(products)
.combinations(3)
.where(bundle => bundle.totalPrice < 100)
.where(bundle => bundle.categories.size >= 2);Functional Pipeline Integration// Seamless chaining with functional operations
combinare(users)
.cartesianProduct(roles, departments)
.map(([user, role, dept]) => ({ ...user, role, dept }))
.filter(assignment => isValidAssignment(assignment))
.groupBy(assignment => assignment.dept);Type-Safe Operations (TypeScript)// Compile-time safety for combination operations
interface Product { id: string; category: 'A' | 'B' | 'C' }
combinare<Product>(products)
.combinations(2)
.ensure<{ differentCategories: boolean }>(); // Type-guided constraints"Dream Utilities" That Would Be Game-ChangersSmart Deduplication// Intelligent duplicate removal based on custom equality
combinare.dedupe(combinations, (a, b) => deepEqual(a.sort(), b.sort()));Memory-Efficient Streaming// Handle massive datasets without memory issues
combinare.fromStream(dataStream)
.combinations(4)
.toAsyncIterable(); // Process combinations as they're generatedProbability-Weighted Generation// Generate combinations based on likelihood/weights
combinare.weighted(items, weights).combinations(3).sample(1000);Incremental Combination Building// Build combinations step by step with validation at each step
combinare.builder()
.add(item => isValidFirst(item))
.then(item => isValidSecond(item))
.build();Mathematical Operations on Combination Sets// Set operations on combination results
const setA = combinare(dataA).combinations(2);
const setB = combinare(dataB).combinations(2);
combinare.union(setA, setB);
combinare.intersection(setA, setB);
combinare.difference(setA, setB);Integration Wish ListFramework Integrations
Performance Optimizations
Validation & Schema Integration
Export & Interoperability
Specific Pain Points Combinare Could Solve1. Memory LimitationsCurrent libraries often generate all combinations in memory, causing issues with large datasets. Need lazy evaluation and streaming support. 2. Business Logic IntegrationMost libraries are purely mathematical. Need constraint-based generation that integrates with business rules and validation logic. 3. Performance at ScaleWhen dealing with thousands of items, current solutions become unusable. Need parallel processing and incremental generation. 4. Type SafetyJavaScript/TypeScript developers need compile-time guarantees about combination structure and constraints. 5. Real-World WorkflowsCurrent libraries don't integrate well with modern development workflows. Need framework integration, testing utilities, and debugging tools. ConclusionThe key gap I see is that most libraries are either too basic (simple array operations) or too academic (pure math focus). Combinare could bridge this by being developer-friendly while mathematically robust - perfect for real-world applications where you need both power and practicality. The sweet spot would be a library that:
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Combinare Library - Use Cases and Feature Suggestions
Common Use Cases I Encounter
Data Analysis & Testing
E-commerce & Product Management