language: en kae3g ← back to index
(unlisted) — This essay is not shown on the main index but remains accessible via direct link

kae3g 9504: What Is Clojure? A Practical Lisp for the JVM

Phase 1: Foundations & Philosophy | Week 1 | Reading Time: 16 minutes

What You'll Learn

Prerequisites

Optional:

The Pragmatic Lisp

Nock (Essay 9503) showed us radical minimalism: 12 rules, eternal spec, auditable simplicity.

Clojure shows us pragmatic minimalism: Simple philosophy + practical platform + real-world adoption.

Clojure (2007) by Rich Hickey: A Lisp for the JVM that embraces hosted platforms, immutability, and simplicity.

The balance:

Lisp: Code as Data (Homoiconicity)

The central insight of Lisp (1958, John McCarthy):

Code is data. Data is code. They're the same thing.

In Most Languages

Code:

def add(x, y):
    return x + y

Data:

[1, 2, 3]

Code and data are different. You can't easily manipulate code as data.

In Lisp (and Clojure)

Code:

(defn add [x y]
  (+ x y))

Data:

[1 2 3]

Both are S-expressions (symbolic expressions)—lists of atoms and nested lists.

The code IS a list: (defn add [x y] (+ x y))

You can:

This is homoiconicity: The language's syntax is its own data structure.

Clojure in 5 Minutes

1. Everything Is an Expression

;; Numbers
42                    ; => 42

;; Strings
"hello valley"        ; => "hello valley"

;; Keywords (like symbols/atoms in other languages)
:name                 ; => :name

;; Lists (code!)
(+ 1 2)              ; => 3

;; Vectors (ordered collections)
[1 2 3]              ; => [1 2 3]

;; Maps (key-value pairs)
{:name "Clojure"
 :year 2007
 :paradigm :functional}  ; => {:name "Clojure", :year 2007, :paradigm :functional}

;; Sets
#{1 2 3}             ; => #{1 3 2} (unordered)

Everything evaluates to something. No statements—only expressions.

2. Functions

;; Define a function
(defn greet [name]
  (str "Hello, " name "!"))

;; Call it
(greet "Valley Builder")  ; => "Hello, Valley Builder!"

;; Functions are first-class (pass them around)
(map greet ["Alice" "Bob" "Carol"])
; => ("Hello, Alice!" "Hello, Bob!" "Hello, Carol!")

Prefix notation (the Lisp way):

Why? Consistency. Everything is (operator operands...). No special cases.

3. Immutability

;; Create a vector
(def v [1 2 3])

;; "Add" an element (creates NEW vector, old unchanged)
(conj v 4)           ; => [1 2 3 4]

;; Original is unchanged!
v                    ; => [1 2 3]

;; To "update", rebind the name
(def v (conj v 4))
v                    ; => [1 2 3 4]

Values never change. You create new values instead.

Why this matters:

4. The REPL (Read-Eval-Print Loop)

;; In a Clojure REPL:

user=> (+ 1 2)
3

user=> (defn factorial [n]
         (if (<= n 1)
           1
           (* n (factorial (dec n)))))
#'user/factorial

user=> (factorial 5)
120

user=> (map factorial [1 2 3 4 5])
(1 2 6 24 120)

The REPL is interactive programming:

  1. Write an expression
  2. Evaluate it immediately
  3. See the result
  4. Refine and iterate

No compile-wait-run cycle. Instant feedback. This changes how you think.

5. Host Interop (JVM)

;; Call Java from Clojure
(import java.time.LocalDateTime)

(def now (LocalDateTime/now))
; => #object[java.time.LocalDateTime "2025-10-10T23:47:13.123"]

(.toString now)
; => "2025-10-10T23:47:13.123"

;; All of Java's libraries available!

Clojure runs on the JVM, giving you:

ClojureScript is the same language compiled to JavaScript (runs in browsers, Node.js).

Immutability: The Superpower

Most languages: Variables vary.

let x = 10;
x = x + 1;  // x is now 11 (mutated)

Clojure: Values are immutable.

(def x 10)
(def x (+ x 1))  ; Creates NEW binding, doesn't mutate

Wait, how do you build programs without mutation?

Example: Updating a Map

Mutable (JavaScript):

let user = {name: "Alice", age: 30};
user.age = 31;  // Mutated in place

Immutable (Clojure):

(def user {:name "Alice" :age 30})
(def updated-user (assoc user :age 31))

user          ; => {:name "Alice", :age 30}  (unchanged!)
updated-user  ; => {:name "Alice", :age 31}  (new value)

"But isn't that inefficient? Copying the whole map?"

No! Clojure uses persistent data structures (structural sharing):

Original:  {:name "Alice" :age 30}
                    ↓ (shares structure)
Updated:   {:name "Alice" :age 31}

Only the CHANGED part is new. The rest is shared.
Performance: O(log n) updates, not O(n) copying!

This is engineering brilliance: Immutability with performance.

The REPL-Driven Development

Traditional workflow:

1. Write code
2. Compile
3. Run
4. See output
5. Edit code
6. Recompile (wait...)
7. Run again
8. Repeat

Clojure workflow:

1. Start REPL
2. Write function
3. Eval it (instant!)
4. Test it (in same REPL)
5. Refine it (eval again)
6. Build entire program incrementally
7. REPL session IS your development

No compile-wait cycle. Changes are instant.

Example Session

;; Start with a sketch
user=> (defn greet [name]
         (str "Hi " name))

user=> (greet "Alice")
"Hi Alice"

;; Refine (add punctuation)
user=> (defn greet [name]
         (str "Hi, " name "!"))

user=> (greet "Alice")
"Hi, Alice!"

;; Extend (handle nil)
user=> (defn greet [name]
         (if name
           (str "Hi, " name "!")
           "Hi, stranger!"))

user=> (greet nil)
"Hi, stranger!"

;; Perfect! Now save to file.

You built the function interactively, testing each iteration immediately.

This is how Lisp programmers have worked since 1958. Clojure brings it to modern platforms.

Rich Hickey's Philosophy: "Simple Made Easy"

Rich Hickey (Clojure's creator) gave a seminal talk: "Simple Made Easy" (2011).

Core distinction:

Simple (from simplex - "one fold/braid"):

Easy (from adjacent - "near at hand"):

Example:

ToolSimple?Easy?
makeNo (complects building + dependency tracking + shell scripting)Yes (familiar to Unix users)
NixYes (pure functions, no hidden state)No (unfamiliar paradigm)
MavenNo (XML config + build lifecycle + plugins all tangled)Yes (lots of tutorials)

Hickey's argument: We confuse "easy" (familiar) with "simple" (not intertwined).

Clojure prioritizes SIMPLE:

Clojure's Core Principles

1. Immutability

Already covered, but worth repeating: Values don't change.

Benefits:

2. First-Class Functions

Functions are values (can be passed, returned, stored):

;; Function as argument
(defn apply-twice [f x]
  (f (f x)))

(apply-twice inc 5)  ; => 7  (inc twice: 5 → 6 → 7)

;; Function as return value
(defn make-adder [n]
  (fn [x] (+ x n)))

(def add10 (make-adder 10))
(add10 5)  ; => 15

Higher-order functions enable powerful abstractions (map, filter, reduce).

3. Code as Data (Homoiconicity)

Clojure code is Clojure data:

;; This is code:
(+ 1 2)

;; This is also data (a list):
'(+ 1 2)  ; Quote prevents evaluation

;; Manipulate code as data:
(first '(+ 1 2))   ; => +
(rest '(+ 1 2))    ; => (1 2)

;; Build code at runtime:
(def code-to-run '(+ 1 2))
(eval code-to-run)  ; => 3

Macros exploit this:

;; Macro: transform code before evaluation
(defmacro when-positive [n & body]
  `(if (pos? ~n)
     (do ~@body)))

;; Expands to:
(when-positive 5
  (println "positive!")
  (println "doing work"))

; Becomes:
(if (pos? 5)
  (do
    (println "positive!")
    (println "doing work")))

Macros are code that writes code. This is metaprogramming at the language level.

4. Hosted on the JVM (and JavaScript)

Clojure doesn't fight its platform—it embraces it:

;; Use Java libraries seamlessly
(import java.util.Date)
(def now (Date.))

;; Use Clojure idioms on Java objects
(.getTime now)  ; => 1728604033123

;; Mix freely
(defn days-until [target-date]
  (let [now (Date.)
        diff (- (.getTime target-date) (.getTime now))]
    (/ diff (* 1000 60 60 24))))

ClojureScript does the same for JavaScript:

;; ClojureScript in browser
(ns my-app.core
  (:require [reagent.core :as r]))

(defn hello []
  [:div "Hello from ClojureScript!"])

(r/render [hello] (.getElementById js/document "app"))

Benefits of hosting:

Trade-off: You're dependent on the host (JVM startup time, JavaScript quirks). But Clojure's philosophy: embrace the platform, don't fight it.

5. Interactive Development (The REPL)

The REPL is central to Clojure workflow:

;; Connected to running application
user=> (def users (atom []))

user=> (swap! users conj {:name "Alice"})
[{:name "Alice"}]

;; Add validation
user=> (defn add-user [db user]
         (if (:name user)
           (conj db user)
           db))

user=> (swap! users add-user {:name "Bob"})
[{:name "Alice"} {:name "Bob"}]

;; Test edge cases
user=> (swap! users add-user {})  ; No name
[{:name "Alice"} {:name "Bob"}]  ; Unchanged (validation worked)

You're testing against LIVE data in your running app. Instant feedback. This changes everything.

Clojure vs Other Lisps

Common Lisp (1984)

Pros: Mature, powerful, standardized
Cons: Complex (CLOS object system, 1,000-page spec), isolated ecosystem

Clojure's improvement: Simpler (no CLOS), hosted (JVM ecosystem)

Scheme (1975)

Pros: Minimal, elegant, educational
Cons: Too minimal for production, fragmented implementations

Clojure's improvement: More practical (richer core library), unified (one canonical implementation)

Racket (1995, Scheme descendant)

Pros: Excellent for education, rich IDE support
Cons: Smaller ecosystem, less production use

Clojure's improvement: Production-focused, huge ecosystem (via JVM)

Emacs Lisp

Pros: Scriptable editor, huge community
Cons: Single-threaded, old design, Emacs-specific

Clojure's improvement: Modern (concurrency-first), general-purpose, multiplatform

Summary: Clojure is Lisp for 2007+—learning from decades of Lisp experience, optimized for real-world use.

Practical Examples

Example 1: Transforming Data

Problem: Given a list of users, extract names.

JavaScript:

const users = [{name: "Alice", age: 30}, {name: "Bob", age: 25}];
const names = users.map(u => u.name);
// => ["Alice", "Bob"]

Clojure:

(def users [{:name "Alice" :age 30} {:name "Bob" :age 25}])
(map :name users)
; => ("Alice" "Bob")

Notice: :name is a function that gets the :name key from a map. Keywords are functions!

Example 2: Filtering

Problem: Find users over 25.

Clojure:

(filter #(> (:age %) 25) users)
; => ({:name "Alice", :age 30})

;; Or more readable:
(filter (fn [user] (> (:age user) 25)) users)

#(...) is shorthand for anonymous functions: #(> (:age %) 25) = (fn [x] (> (:age x) 25))

Example 3: Threading (Composition)

Problem: Transform data through multiple steps.

Nested (hard to read):

(reduce + (map :age (filter #(> (:age %) 25) users)))
; Read inside-out: filter, then map, then reduce

Threaded (clear pipeline):

(->> users
     (filter #(> (:age %) 25))
     (map :age)
     (reduce +))
; Read top-to-bottom: filter, then map, then reduce

The ->> macro threads each result as last argument to next function.

Result: Same computation, but reads like a pipeline (water flowing through stages).

Example 4: Building a Web Server

Clojure + Ring (web library):

(ns my-server.core
  (:require [ring.adapter.jetty :as jetty]))

(defn handler [request]
  {:status 200
   :headers {"Content-Type" "text/plain"}
   :body "Hello from Clojure!"})

(defn -main []
  (jetty/run-jetty handler {:port 3000}))

;; Run: (main)
;; Visit: http://localhost:3000

That's it. A function that takes a request (map) and returns a response (map).

No framework ceremony. Just data in, data out.

Simple Made Easy: The Design Philosophy

Rich Hickey's core thesis:

"We should aim for simplicity, because simplicity is a prerequisite for reliability." - Dijkstra

How Clojure embodies simplicity:

1. Separate Identity and State

Complex: Object-oriented (identity and state bundled together)

// Java: object has mutable state
User user = new User("Alice");
user.setAge(31);  // Mutated—identity same, state changed

Simple: Clojure separates them

;; Values (state) are immutable
(def alice-v1 {:name "Alice" :age 30})
(def alice-v2 (assoc alice-v1 :age 31))

;; Identity (if needed) managed explicitly
(def alice (atom {:name "Alice" :age 30}))
(swap! alice assoc :age 31)

Benefit: You choose when to conflate identity with state. Not forced.

2. No Hidden Coupling

Complex: Global state, singletons, implicit dependencies

Simple: Explicit arguments, pure functions

;; Bad (implicit dependency on global)
(def users (atom []))

(defn add-user [name]
  (swap! users conj {:name name}))  ; Mutates global—spooky!

;; Good (explicit dependency)
(defn add-user [db name]
  (conj db {:name name}))  ; Pure function—returns new db

;; Call site makes dependency visible
(swap! users add-user "Alice")

3. Data Orientation

Complex: Objects with methods, inheritance hierarchies

Simple: Plain data with functions

;; No classes, just data
(def user {:name "Alice" :age 30 :role :admin})

;; Functions on data
(defn admin? [user]
  (= (:role user) :admin))

(admin? user)  ; => true

Benefits:

Concurrency: The Atom Model

Clojure's approach to mutable state (when you actually need it):

Atoms (Synchronous, Independent)

;; Create mutable reference
(def counter (atom 0))

;; Read it
@counter  ; => 0

;; Update it (atomically)
(swap! counter inc)  ; => 1
(swap! counter inc)  ; => 2

;; Even with 10 threads calling inc simultaneously,
;; you get correct count (no race conditions!)

How? swap! uses compare-and-swap (CAS) at hardware level. Retries if another thread changed the value. Lock-free concurrency.

Refs (Coordinated Transactions)

;; Transfer money between accounts (must be atomic)
(def alice-account (ref 100))
(def bob-account (ref 50))

(defn transfer [from to amount]
  (dosync
    (alter from - amount)
    (alter to + amount)))

(transfer alice-account bob-account 20)

@alice-account  ; => 80
@bob-account    ; => 70

dosync creates a transaction. Either both refs change, or neither does. STM (Software Transactional Memory).

No locks, no deadlocks, no race conditions. The runtime handles it.

Clojure in Production

Who uses Clojure?

Why they chose Clojure:

It's not just a hobby language. It's production-proven at scale.

Hands-On: Your First Clojure

Exercise 1: Install Clojure

macOS/Linux:

# Install via brew (macOS)
brew install clojure/tools/clojure

# Or via install script (Linux)
curl -L -O https://github.com/clojure/brew-install/releases/latest/download/linux-install.sh
chmod +x linux-install.sh
sudo ./linux-install.sh

Verify:

clj
# Should start a REPL!

Exercise 2: REPL Exploration

In the REPL, try:

;; Basic math
(+ 1 2 3 4 5)

;; Define a function
(defn square [x] (* x x))
(square 7)

;; Higher-order function
(map square [1 2 3 4 5])

;; Build a data structure
(def user {:name "You" :learning :clojure})

;; Transform it
(assoc user :excited? true)

;; Quit
(System/exit 0)

Notice: Instant feedback. No compile step. This is the Clojure way.

Exercise 3: Compare to a Language You Know

If you know JavaScript:

// JS
const arr = [1, 2, 3];
const doubled = arr.map(x => x * 2);


;; Clojure
(def arr [1 2 3])
(def doubled (map #(* % 2) arr))

Similar! But Clojure:

If you know Python:

# Python
users = [{"name": "Alice"}, {"name": "Bob"}]
names = [u["name"] for u in users]


;; Clojure
(def users [{:name "Alice"} {:name "Bob"}])
(def names (map :name users))

Clojure is more concise (keywords are functions!) and immutable (Python lists are mutable).

Try This

Exercise 1: Solve FizzBuzz

Classic interview question: Print 1-100, but:

Try in Clojure:

(defn fizzbuzz [n]
  (cond
    (zero? (mod n 15)) "FizzBuzz"
    (zero? (mod n 3))  "Fizz"
    (zero? (mod n 5))  "Buzz"
    :else              n))

(map fizzbuzz (range 1 101))

Appreciate: How declarative this is (say what, not how).

Exercise 2: Immutable Update

Task: Update a nested map.

(def system {:services {:web {:port 8080 :running true}
                        :db  {:port 5432 :running true}}})

;; Stop the web service (deep update)
(assoc-in system [:services :web :running] false)

;; Original unchanged!
system  ; => {:services {:web {:port 8080 :running true} ...}}

assoc-in updates nested structures immutably. Try this in Python (you'll appreciate Clojure's elegance).

Exercise 3: Build a Simple CLI

;; save as hello.clj
(ns hello.core)

(defn -main [& args]
  (println "Hello," (or (first args) "Valley Builder") "!"))

;; Run:
;; clj -M -m hello.core Alice
;; => "Hello, Alice !"

That's a complete CLI program. No boilerplate. Just functions and data.

Going Deeper

Related Essays

External Resources

For the Narrative-Inclined

Reflection Questions

  1. Why does immutability prevent bugs? (If values can't change, whole classes of errors disappear)
  2. Is prefix notation really better? (Consistent vs special cases—what's the trade-off?)
  3. Could you REPL-drive your current work? (What would change if you had instant feedback?)
  4. What does "simple" mean to you? (Not easy, not familiar—but not intertwined)
  5. Is Clojure "too weird" or "refreshingly different"? (Depends on your openness to new paradigms)

Summary

Clojure is:

Key Insights:

Rich Hickey's Gift:

In the Valley:

Next: We'll explore the Unix Philosophy—principles that shaped computing for 50 years, and still guide us today. Clojure embodies many of these principles ("do one thing well" = simple functions!).

Navigation:
← Previous: 9503 (what is nock) | Phase 1 Index | Next: 9505 (house of wisdom knowledge gardens)

Bridge to Narrative: For the story of Clojure, see 9949 (The Wise Elders)!

Metadata:

Copyright © 2025 kae3g | Dual-licensed under Apache-2.0 / MIT
Competitive technology in service of clarity and beauty

← back to index