# Paradigm - Programming Paradigms for Problem Solving
## What It Does
Paradigm helps you choose and apply the right programming approach for your problem. Whether you need functional programming for data pipelines, event-driven architecture for real-time systems, or reactive programming for live dashboards, this tool guides you through each paradigm's strengths and trade-offs.
**Key Value**: Stop defaulting to the paradigm you know best. Paradigm helps you match the right programming style to the problem, leading to cleaner, more maintainable code.
## When to Use This Tool
- **Starting a new project** - Choose the right foundation before writing code
- **Refactoring legacy code** - Understand which paradigm fits your new requirements
- **Performance optimization** - Some paradigms handle concurrency or data flow better
- **Team discussions** - Create shared vocabulary around architectural choices
- **Learning** - Understand when and why to use different approaches
## How It Works
Select a paradigm, describe your problem, and receive implementation guidance:
```
Problem → Select Paradigm → Approach → Benefits → Limitations → Languages
```
### Available Paradigms
| Paradigm | Best For | Core Idea |
|----------|----------|-----------|
| **Functional** | Data transformation, parallel processing | Pure functions, immutability, composition |
| **Object-Oriented** | Domain modeling, encapsulation | Objects with state and behavior |
| **Event-Driven** | Real-time systems, decoupling | React to events rather than poll |
| **Reactive** | Live data streams, UI updates | Observable streams with operators |
| **Imperative** | Scripts, simple automation | Step-by-step instructions |
| **Declarative** | Configuration, queries | Describe what, not how |
| **Concurrent** | Parallel workloads, performance | Multiple execution threads |
| **Logic** | Rule engines, AI reasoning | Facts and inference rules |
## Interactive Examples
### Scenario 1: Functional Programming for Fraud Detection
**Context**: Processing a stream of financial transactions to detect fraud patterns.
**Input**:
```json
{
"paradigmName": "functional",
"problem": "Processing a stream of financial transactions to detect fraud patterns",
"approach": [
"Use pure functions for transaction validation",
"Compose detection rules as higher-order functions",
"Implement immutable state for audit trail",
"Apply map/filter/reduce for pattern analysis"
],
"benefits": [
"Easy to test individual rules",
"Parallelizable processing",
"No hidden side effects"
],
"limitations": [
"Learning curve for imperative developers",
"Memory overhead from immutability"
],
"languages": ["Haskell", "Scala", "JavaScript/TypeScript"]
}
```
**Output**:
```json
{
"paradigmName": "functional",
"status": "success",
"hasApproach": true,
"hasCodeExample": false
}
```
**What This Means**: Functional programming is well-suited for fraud detection. Pure functions make each detection rule independently testable, and immutability creates a reliable audit trail - critical for financial compliance.
---
### Scenario 2: Event-Driven for Collaborative Editing
**Context**: Building a real-time collaborative document editor like Google Docs.
**Input**:
```json
{
"paradigmName": "event_driven",
"problem": "Building a real-time collaborative document editor like Google Docs",
"approach": [
"Emit events for every user action (keystroke, cursor move)",
"Subscribe components to relevant event streams",
"Use event sourcing for document history",
"Implement CRDT for conflict resolution"
],
"benefits": [
"Natural fit for real-time collaboration",
"Decoupled components",
"Easy to add new features by subscribing to events"
],
"limitations": [
"Event ordering complexity",
"Debugging event chains",
"Memory for event history"
],
"languages": ["JavaScript", "Elixir", "Go"]
}
```
**Output**:
```json
{
"paradigmName": "event_driven",
"status": "success",
"hasApproach": true,
"hasCodeExample": false
}
```
**What This Means**: Event-driven architecture naturally maps to real-time collaboration. Every user action becomes an event that propagates to all clients. Event sourcing enables undo/redo and version history for free.
---
### Scenario 3: Reactive Programming for Live Dashboard
**Context**: Dashboard that displays live metrics from 50 different data sources with automatic updates.
**Input**:
```json
{
"paradigmName": "reactive",
"problem": "Dashboard that displays live metrics from 50 different data sources with automatic updates",
"approach": [
"Model each data source as an Observable stream",
"Combine streams using merge/combineLatest operators",
"Apply backpressure for high-frequency updates",
"Use subjects for user interaction events"
],
"benefits": [
"Declarative data flow",
"Built-in handling of async complexity",
"Automatic cleanup of subscriptions"
],
"limitations": [
"Steep learning curve",
"Memory leaks if subscriptions not managed",
"Debugging marble diagrams"
],
"languages": ["RxJS/TypeScript", "RxJava", "ReactiveX"]
}
```
**Output**:
```json
{
"paradigmName": "reactive",
"status": "success",
"hasApproach": true,
"hasCodeExample": false
}
```
**What This Means**: Reactive programming excels at combining multiple async data sources. With 50 data sources, manual callback management would be a nightmare - Observables let you declaratively combine, filter, and transform all those streams.
## User Experience
When you invoke Paradigm, you receive confirmation of your analysis:
| Field | What It Tells You |
|-------|-------------------|
| `paradigmName` | Which paradigm was analyzed |
| `status` | "success" if analysis is complete |
| `hasApproach` | Whether approach steps were provided |
| `hasCodeExample` | Whether code samples were included |
**The insight is in your reasoning** - Paradigm structures your thinking about how to approach the problem, with explicit trade-offs to inform team decisions.
## Integration Tips
- **Compare paradigms** - Run multiple paradigms against the same problem to compare approaches
- **Consider team skills** - The "best" paradigm is useless if your team can't maintain it
- **Hybrid approaches** - Real systems often combine paradigms (e.g., OOP with functional data processing)
- **Chain with Pattern** - After choosing a paradigm, use Pattern for specific design patterns within it
- **Document decisions** - Use output as architectural decision records
## Quick Reference
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `paradigmName` | enum | Yes | functional, object_oriented, event_driven, reactive, imperative, declarative, concurrent, logic, procedural, aspect_oriented |
| `problem` | string | Yes | The problem you're solving |
| `approach` | string[] | No | Step-by-step approach using this paradigm |
| `benefits` | string[] | No | Advantages for this problem |
| `limitations` | string[] | No | Drawbacks and challenges |
| `codeExample` | string | No | Sample code demonstrating the approach |
| `languages` | string[] | No | Languages that support this paradigm well |
