Grokking Simplicity

Functional Thinking

Pure function benefits: Write pure functions whenever possible to make code more testable, reusable and maintainable
Action vs calculation: Distinguish clearly between actions (with side effects) and calculations (pure functions)
Data vs operations: Separate data from operations to increase flexibility and composability
First-class abstractions: Treat functions as first-class values to enable powerful abstractions
Higher-order functions: Use functions that take functions as arguments to reduce code duplication
Function composition: Build complex behavior by composing smaller, focused functions
Immutability advantages: Work with immutable data to avoid unexpected side effects and race conditions
Referential transparency: Aim for referential transparency where a function call can be replaced with its return value
Time management: Handle time explicitly rather than relying on hidden sequences of operations
State handling: Manage state carefully, isolating it to make your system more predictable

Actions and Calculations

Side effect identification: Identify and isolate side effects to make your code easier to reason about
Action reduction: Reduce the number of actions in your code to minimize complexity
Testing approach: Make your code more testable by separating pure calculations from actions
Refactoring strategy: Refactor by extracting calculations from actions to improve modularity
Function categorization: Categorize functions as actions, calculations, or data to clarify their roles
Parameter passing: Pass explicit parameters instead of relying on global state
Return value usage: Use return values rather than modifying state when possible
Defensive copying: Implement defensive copying to prevent unintended data mutations
Idempotent actions: Design actions to be idempotent when possible to improve reliability
Action coordination: Coordinate actions explicitly rather than relying on implicit sequencing

Extracting Calculations from Actions

Code smell awareness: Recognize when code mixes calculations with actions
Extraction technique: Extract pure calculations from actions by identifying inputs and outputs
Function signature design: Design function signatures to clearly indicate actions vs calculations
Refactoring steps: Follow systematic steps to separate calculations from actions
Testing benefit: Leverage easier testing of pure calculations after extraction
Naming conventions: Use naming conventions that distinguish between actions and calculations
Composition opportunities: Look for opportunities to compose pure functions after extraction
Code organization: Organize code with calculations separate from actions
Reuse improvement: Improve code reuse by making calculations independent of actions
Reasoning simplification: Simplify reasoning about code by isolating complex logic in calculations

Stratified Design

Layer organization: Organize code in layers with higher levels calling lower levels, not vice versa
Abstraction levels: Create appropriate levels of abstraction with clear responsibilities
Interface design: Design interfaces that hide implementation details effectively
Layer dependencies: Ensure dependencies only go downward in your abstraction layers
Composability planning: Design for composability by creating functions that work well together
Layer isolation: Isolate layers to contain changes and minimize their impact
Interface stability: Keep higher-level interfaces stable while allowing lower-level implementations to evolve
Appropriate abstractions: Create abstractions at the right level for your domain
Domain modeling: Model your domain accurately in your abstraction layers
Refactoring guidance: Use stratified design principles to guide refactoring decisions

First-Class Functions

Function as values: Treat functions as values that can be passed, returned, and stored
Callback implementation: Implement callbacks to make your code more flexible and extensible
Higher-order function creation: Create higher-order functions to abstract common patterns
Function returning benefits: Return functions from functions to create specialized behavior
Function storage: Store functions in data structures when configuration or rules need to be dynamic
Functional patterns: Apply functional patterns like map, filter, and reduce to process collections
Function composition: Compose functions to build complex behavior from simple pieces
Closure usage: Use closures to create functions with built-in context
Partial application: Apply partial application to create reusable specialized functions
Function factories: Implement function factories to generate related functions

Immutable Data

Defensive copying: Create defensive copies when receiving or returning mutable data
Persistent data structures: Use persistent data structures for efficient immutable operations
Immutable update patterns: Learn patterns for updating immutable data efficiently
Copy-on-write implementation: Implement copy-on-write to maintain immutability
Performance considerations: Consider performance implications of immutability and optimize when needed
Language support: Leverage language features or libraries that support immutable data
Collection handling: Handle collections in an immutable way using map, filter, and reduce
Nested data structures: Update nested immutable data structures correctly
Equality comparison: Compare immutable data structures correctly for equality
Value semantics: Embrace value semantics over reference semantics

Dealing with Time

Timeline visualization: Visualize your program as a timeline of actions to reason about ordering
Explicit sequencing: Make action sequences explicit rather than relying on implicit ordering
Concurrency management: Manage concurrency carefully when actions need to happen in parallel
Timing dependency reduction: Reduce dependencies on precise timing to make systems more robust
Event sourcing consideration: Consider event sourcing for systems where time and history are important
Idempotent design: Design idempotent operations that can be safely retried
Timeline decoupling: Decouple timelines when actions don’t need to be sequenced together
Task coordination: Coordinate tasks explicitly when they must run in a specific order
Race condition prevention: Prevent race conditions by using appropriate concurrency primitives
Time modeling: Model time explicitly in your domain when it’s an important concept

Reactive Systems

Event handling: Handle events in a functional way using callbacks or streams
Stream processing: Process streams of events with functional operations
Reactive architecture: Design reactive architectures that respond to events rather than polling
Subscription management: Manage subscriptions carefully to avoid memory leaks
Backpressure handling: Handle backpressure when consumers can’t keep up with event producers
Error propagation: Propagate errors appropriately in asynchronous and reactive systems
Event transformation: Transform events using pure functions to maintain functional principles
State management: Manage state carefully in reactive systems using functional approaches
Testing reactive code: Test reactive code by controlling event sequences
Composing streams: Compose event streams to create complex reactive behavior

Data Modeling

Data structure choice: Choose appropriate data structures for your problem domain
Immutable-by-default approach: Make data immutable by default to avoid accidental mutations
Nested structure handling: Handle nested data structures with appropriate accessor patterns
Collection transformation: Transform collections using functional operations rather than loops
Record design: Design records (or objects) to represent domain entities
Value object usage: Use value objects for concepts with value semantics
Entity identification: Identify entities by identity rather than state
Schema evolution: Plan for schema evolution in long-lived systems
Domain modeling accuracy: Model your domain accurately in your data structures
Data validation: Validate data at boundaries to ensure system integrity

Functional Patterns

Common pattern recognition: Recognize common functional patterns like map, filter, and reduce
Pattern implementation: Implement these patterns from scratch to understand them deeply
Collection processing: Process collections functionally rather than imperatively
Error handling patterns: Use functional error handling patterns like Option/Maybe or Either/Result
Pattern composition: Compose patterns to solve complex problems
Custom pattern creation: Create custom patterns for your specific domain needs
Recursion schemes: Learn recursion schemes for processing recursive data structures
Monadic operations: Understand monadic operations for sequencing computations
Functors and applicatives: Use functors and applicatives for applying functions to wrapped values
Pattern matching: Implement pattern matching for expressive data handling

Key Takeaways

Pure function benefits: Write pure functions to improve testability, reusability, and maintainability
Action isolation: Isolate actions (functions with side effects) to make your code more predictable
Stratified design: Organize code in layers with clear responsibilities and downward dependencies
First-class functions: Treat functions as values to create powerful abstractions
Immutable data advantages: Use immutable data to prevent unexpected side effects and race conditions
Explicit time handling: Handle time and sequencing explicitly rather than implicitly
Higher-order function power: Leverage higher-order functions to reduce duplication and increase flexibility
Functional data processing: Process data using functional operations like map, filter, and reduce
Reactive programming techniques: Build reactive systems using functional programming principles
Domain modeling with immutability: Model your domain using immutable data structures for reliability