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Distributed across servers, difficult to test, and resistant to modification—modern software is complex. Grokking Simplicity is a friendly, practical guide that will change the way you approach software design and development. It introduces a unique approach to functional programming that explains why certain features of software are prone to complexity, and teaches you the functional techniques you can use to simplify these systems so that they’re easier to test and debug.A very accessible introduction to functional programming for developers who don't already know anything about it.
Available in PDF (ePub, kindle, and liveBook formats coming soon).
About the Technology
Even experienced developers struggle with software systems that sprawl across distributed servers and APIs, are filled with redundant code, and are difficult to reliably test and modify. Adopting ways of thinking derived from functional programming can help you design and refactor your codebase in ways that reduce complexity, rather than encouraging it. Grokking Simplicity lays out how to use functional programming in a professional environment to write a codebase that’s easier to test and reuse, has fewer bugs, and is better at handling the asynchronous nature of distributed systems.About the book
In Grokking Simplicity, you’ll learn techniques and, more importantly, a mindset that will help you tackle common problems that arise when software gets complex. Veteran functional programmer Eric Normand guides you to a crystal-clear understanding of why certain features of modern software are so prone to complexity and introduces you to the functional techniques you can use to simplify these systems so that they’re easier to read, test, and debug. Through hands-on examples, exercises, and numerous self-assessments, you’ll learn to organize your code for maximum reusability and internalize methods to keep unwanted complexity out of your codebase. Regardless of the language you’re using, the ways of thinking in this book will help recognize problematic code and tame even the most complex software.
Table of Contents takes you straight to the bookdetailed table of contents
Part 1: Actions, Calculations, and Data
1 Welcome to Grokking Simplicity
What is functional programming?
The problems with the definition for practical use
Problem 1
Problem 2
Problem 3
The definition of functional programming is confusing to managers
We treat functional programming as a set of skills and concepts
Distinguishing actions, calculations, and data
Functional programmers distinguish code that matters when you call it
Functional programmers distinguish inert data from code that does work
Functional programmers see actions, calculations, and data
The three categories of code in functional programming
How does distinguishing actions, calculations, and data help us?
FP works great for distributed systems, and most software written today is distributed
Why is this book different from other functional programming books?
This book uses real-world scenarios
This book focuses on software design
This book conveys the richness of functional programming
This book is language agnostic
What is functional thinking?
Part 1: Distinguishing actions, calculations, and data
Part 2: Using first-class abstractions
Part 3: Building composable models
Ground rules for ideas and skills in this book
1. The skills can’t be based on language features
2. The skills must have immediate practical benefit
3. The skills must apply regardless of your current code situation
Brain break
Conclusion
Summary
2 Functional thinking in action
Welcome to Toni’s Pizza
Part 1: Distinguishing actions, calculations, and data
Part 2: Using first-class abstractions
Part 3: Building composable models
Part 1: Distinguishing actions, calculations, and data
Actions
Calculations
Data
Organizing code by “rate of change”
A first glimpse of stratified design
Part 2: First-class abstractions
As applied to a robotic kitchen
Timelines help you understand distributed systems
Multiple timelines can execute in different orderings
Hard-won lessons about distributed systems
Toni does a post-mortem
1. Timelines are uncoordinated by default
2. You cannot rely on the duration of actions
3. Bad timing, however rare, can happen in production
4. The timeline diagram reveals problems in the system
Cutting the timeline: Making the robots wait for each other
Positive lessons learned about timelines
Retrospective on coordinating robots
1. Cutting a timeline makes it easy to reason about thepieces in isolation
2. Working with timeline diagrams helps me understand howthe system works through time
3. Timeline diagrams are flexible
Part 3: Building composable models
A first glimpse at modeling a domain with data
Comparing functional programming to an object-oriented approach
A typical object-oriented approach
A typical functional approach
We use data all the time outside of the computer
Representing a recipe as data
Data can be interpreted in many ways
The ingredients list operations evolved into a powerful API
Conclusion
Summary
3 Distinguishing actions, calculations, and data
Actions, calculations, and data
1. Thinking about a problem
2. Coding up a solution
3. Reading code
Actions, calculations, and data apply to any situation (page 1 of 3)
Actions, calculations, and data apply to any situation (page 2 of 3)
check fridge
drive to store
buy what you need
drive home
Actions, calculations, and data apply to any situation (page 3 of 3)
Lessons from our shopping process
1. We can apply the actions, calculations, and data perspective to any situation
2. Actions can hide actions, calculations, and data
3. Calculations can be composed of smaller calculations and data
4. Data can only be composed of more data
5. Calculations often happen “in our heads”
Deep dive: Data
What is data?
How do we implement data?
How does data encode meaning?
What are the advantages of data?
Applying functional thinking to new code
A new marketing tactic at CouponDog
Brainstorm!
Don’t worry about getting the wrong answer. It’s just about exploring the ideas.
Drawing the coupon email process (page 1 of 5)
1. Let’s start with fetching subscribers from the database
2. Fetching the coupons from the database
Drawing the coupon email process (page 2 of 5)
3. Generating the emails to send
4. Sending the emails
Drawing the coupon email process (page 3 of 5)
Zooming into generating emails
Drawing the coupon email process (page 4 of 5)
Zooming into generating emails
Drawing the coupon email process (page 5 of 5)
Zooming into generating emails
Implementing the coupon email process (page 1 of 5)
The subscriber is data from the database
The coupon rank is a string
Deciding a coupon rank is a function
Implementing the coupon email process (page 2 of 5)
The coupon is data from the database
The calculation to select coupons by rank is a function
Implementing the coupon email process (page 3 of 5)
An email is just data
Implementing the coupon email process (page 4 of 5)
The calculation to plan one email for a subscriber
Implementing the coupon email process (page 5 of 5)
Planning all emails
Sending the emails is an action
Brain break
Deep dive: Calculations
What are calculations?
How do we implement calculations?
How do calculations encode meaning?
Why prefer calculations to actions?
What worries do calculations avoid?
Applying functional thinking to existing code
Actions spread through code
Actions can take many forms
Deep dive: Actions
Conclusion
Summary
4 Extracting calculations from actions
Welcome to MegaMart.com!
Where your shopping cart is always full
MegaMart reveals its secret code to you
No NDA required
Calculating free shipping
Your new assignment
The imperative way to do it
Sometimes the imperative way is the most straightforward
Calculating tax
Your next assignment
We need to make it more testable
The code contains business rules that are not easy to test
George from testing’s code notes
George from testing’s suggestions
We need to make it more reusable
The accounting and shipping departments want to use our code
Jenna on dev team’s code notes
Jenna on dev team’s suggestions
Distinguishing actions, calculations, and data
Functions have inputs and outputs
Inputs and outputs can be implicit or explicit
Implicit inputs and outputs make a function an action
How testing and reuse are related to inputs and outputs
Suggestions from George and Jenna
George 1: Separate business rules from DOM updates
George 2: Get rid of global variables
Jenna 1: Don’t depend on global variables
Jenna 2: Don’t assume the answer goes in the DOM
Jenna 3: Return the answer from the function
Extracting a calculation from an action (page 1 of 3)
Extracting a calculation from an action (page 2 of 3)
Extracting a calculation from an action (page 3 of 3)
Extracting out another calculation from an action (page 1 of 3)
Extracting out another calculation from an action (page 2 of 3)
Extracting out another calculation from an action (page 3 of 3)
Brain break
There’s more to go, but let’s take a break for questions
Step-by-step: Extracting a calculation
1. Select and extract the calculation code
2. Identify the implicit inputs and outputs of the function
3. Convert inputs to arguments and outputs to return values
Let’s see all of our code in one place
Conclusion
Summary
5 Improving the design of actions
Improving the design by aligning it with business requirements
Choosing a better level of abstraction that matches its usage
Aligning the function with business requirements (page 1 of 2)
This isn’t refactoring, really, since we’re changing the behavior.
Brain break
There’s more to go, but let’s take a break for questions
Principle: When it comes to implicit inputs and outputs, the fewer, the better
Reducing the implicit inputs and outputs update_shipping_icons()
When it comes to implicit inputs and outputs, the fewer, the better
Giving the code a once-over (page 1 of 2)
Giving the code a once-over (page 2 of 2)
Categorizing our calculations
By grouping our calculations, we can learn something about layers of meaning
Principle: Design is about pulling things apart
Improving the design by pulling add_item() apart
This function might look simple, but we can pull something out
Extracting out a copy-on-write pattern
Using add_item()
Categorizing our calculations
Let’s take another look at what they’re about
Brain break
There’s more to go, but let’s take a break for questions
Smaller functions and more calculations
Conclusion
Summary
6 Staying immutable in a mutable language
Can immutability be applied everywhere?
Categorizing operations into reads, writes, or both
We can categorize an operation as either a read or a write
The three steps of the copy-on-write discipline
Converting a write to a read with copy-on-write
Converting a write to a read with copy-on-write
Converting a write to a read with copy-on-write
Converting a write to a read with copy-on-write
Converting a write to a read with copy-on-write
Complete diff from mutating to copy-on-write
These copy-on-write operations are generalizable
JavaScript Arrays at a glance
What to do if an operation is a read and a write
Preferred approach for copy-on-write for a read+write
1. Splitting the read and write into two operations
Splitting the operation into read and write
Convert the write into a copy-on-write
Also good approach for copy-on-write for a read+write
2. Returning two values
Wrap up the operation
Convert the read+write to a read
Another option
Brain break
Reads to immutable data structures are calculations
Applications have state that changes over time
Immutable data structures are fast enough
We can always optimize later
Garbage collectors are really fast
Garbage collectors are really fast
We’re not copying as much as you might think at first
Functional programming languages have fast implementations
Copy-on-write operations on Objects
JavaScript Objects at a glance
Converting nested writes to reads
What gets copied?
Arrays: 1 (shopping cart) Objects: 3 (t-shirt, shoes, and socks in cart)
Arrays: 1 (shopping cart) Objects: 1 (t-shirt)
Visualizing shallow copies and structural sharing
Conclusion
Summary
Up next …
7 Staying immutable with untrusted code
Immutability with legacy code
Our code code has to interact with untrusted code
Defensive copying defends the immutable original
Implementing defensive copies
The rules of defensive copying
Rule 1: Copy as data leaves your code
Rule 2: Copy as data enters your code
Wrapping untrusted code
Defensive copying you may be familiar with
Defensive copying in web APIS
Defensive copying in Erlang and Elixir
Brain break
Copy-on-write and defensive copying compared
Deep copies are much more expensive than shallow copies
Implementing deep copy in JavaScript is difficult
A dialog between copy-on-write and defensive copying
Conclusion
Summary
8 Stratified design, part 1
What is software design?
What is stratified design?
Developing our design sense
Patterns of stratified design
Pattern 1: Straightforward implementations
Desired shopping cart operations
Checking if an item is in the cart can help us
Visualizing our function calls with a call graph
Call functions from a similar layer of abstraction
Brain break
Adding another function to the graph
Brain break
All functions in a layer should serve the same pupose
Three different zoom levels
Arrow length at the layer zoom level
Arrow length at the function zoom level
Extracting out the for loop
Brain break
Pattern 1 Review: Straightforward implementation
Conclusion
Summary
9 Stratified design, part 2
Patterns of stratified design
Pattern 2: Abstraction barrier
Abstraction barriers hide implementations
Ignoring details is symmetrical
Swapping the shopping cart’s data structure
Reimplementing the shopping cart as an object
The abstraction barrier lets us ignore details
When to use (and when not to use!) abstraction barriers
Pattern 2 Review: Abstraction barrier
Our code is more straightforward
Pattern 3: Minimal interface
Two choices for coding the marketing campaign
Implementing the campaign above the barrier is better
Marketing wants to log items added to the cart
The design consequences of code location
A better place to log adds to cart
Pattern 3 Review: Minimal interface
Pattern 4: Comfortable layers
Patterns of stratified design
What does the graph show us about our code?
Code at the top of the graph is easier to change
Code at the top of the graph is easier to change
Testing code at the bottom is more important
Testing code at the bottom is more important
Testing code at the bottom is more important
Code at the bottom is more reusable
Summarizing what the graph shows us about NFRs
Conclusion
Summary
Part 2: First-class abstractions
10 First-class functions, part 1
One code smell and two refactorings
Marketing still needs to coordinate with dev
Code smell: Implicit argument in function name
Refactoring: express implicit argument
Recognize what is and what isn’t first class
Will field names as strings lead to more bugs?
Will first-class fields make the API hard to change?
We will use a lot of Objects and Arrays
First-class functions can replace any syntax
For loop example: eating and cleaning up
Refactoring: replace body with callback
Duplication in the logging system
Deduplicating the logging system
What is this syntax?
Why are we wrapping the code in a function?
Brain break
Conclusion
Summary
11 First-class functions, part 2
One code smell and two refactorings
Refactoring copy-on-write
Refactoring copy-on-write for arrays
Returning functions from functions
Review of the automated logging system
Deduplicating with common code in a function
Returning a function from a function
Brain break
Conclusion
Summary
12 Functional iteration
13 Chaining functional tools
14 Functional tools for nested data
15 Isolating timelines
16 Sharing resources between timelines
17 Cutting timelines
18 Onion architecture
Part 3: Building composable models
19 Modeling facts with data
20 Modeling change over with data
21 Modeling state with data
22 Modeling operations first
23 Reactive architecture
24 Conclusion
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