Crystal is not “Ruby but fast.” That framing undersells it and misleads newcomers. Crystal is a statically typed, compiled language that borrows Ruby’s syntax and idioms to the extent that large chunks of Crystal code are valid Ruby and vice versa — but underneath it produces native binaries via LLVM, performs type inference across your entire program at compile time, and offers a macro system that operates on the AST rather than through string substitution.
For a Ruby developer, Crystal feels like home. For a systems programmer, Crystal offers a comfortable middle ground between Go’s simplicity and Rust’s safety guarantees. For a performance engineer, Crystal routinely runs 30–100x faster than Ruby 3.x on CPU-bound workloads.
This post is a thorough technical reference. We will cover the type system, macros, fibers and channels, C bindings, the standard library, tooling, and the ecosystem — and we will be honest about where Crystal is not yet the right choice.
What Crystal Is
Crystal was created by Ary Borenszweig and Juan Wajnerman at Manas Tech in Argentina. The first public commit appeared in 2012; version 1.0.0 shipped in March 2021. As of Crystal 1.15 (early 2026), the language is stable with a well-defined roadmap and a small but committed core team.
The compilation pipeline:
Crystal source → Crystal compiler (itself written in Crystal) → LLVM IR → native binary
Crystal compiles to a single statically linked executable by default. No runtime virtual machine, no garbage collector pause surprises at the scale of scripting languages — Crystal uses a precise, tri-color mark-and-sweep GC (the Boehm-Demers-Weiser GC, with the Crystal team moving toward a custom GC). Memory management is automatic but the binary has no interpreter overhead.
Hello, Crystal
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# hello.cr
puts "Hello, Crystal!"
# String interpolation — identical to Ruby
name = "world"
puts "Hello, #{name}!"
# Types are inferred — no annotation needed here
x = 42
y = 3.14
puts typeof(x) # => Int32
puts typeof(y) # => Float64
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crystal run hello.cr
# or compile to a binary:
crystal build hello.cr -o hello
./hello
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How Crystal Positions Against Peers
| Language |
Typing |
Speed |
Ruby familiarity |
Ecosystem |
Concurrency |
| Ruby 3.x |
Dynamic |
Slow |
Baseline |
Huge |
GIL / Ractors |
| Crystal |
Static (inferred) |
Near-C |
Very high |
Small |
Fibers / M:N |
| Go |
Static (explicit) |
Fast |
Low |
Large |
Goroutines |
| Rust |
Static (explicit) |
Fastest |
Low |
Medium |
async/await, threads |
| Nim |
Static (inferred) |
Near-C |
Medium |
Small |
async/threads |
Crystal’s primary value proposition: you write code that reads like Ruby, you ship code that runs like C, and the compiler catches type errors before production does.
Production Users
Crystal in production includes:
- Manas Tech — internal tooling and client projects (the language’s birthplace)
- 84codes — CloudAMQP, CloudMQ, and LavinMQ (a high-throughput AMQP broker written in Crystal handling millions of messages/sec)
- Nikola Motor Company — embedded tooling
- Monterail — web API services
- Various companies using Kemal or Lucky for internal APIs
LavinMQ is the most cited production story: a message broker that outperforms RabbitMQ on throughput while using a fraction of the memory, written in Crystal.
Core Language and Syntax
Ruby Familiarity
If you know Ruby, Crystal’s syntax is immediately readable. Blocks, iterators, do...end, string interpolation, symbols, named arguments, method chaining — all present and accounted for.
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# Blocks and iterators
[1, 2, 3, 4, 5].each do |n|
puts n * 2
end
# Block with curly braces (single expression)
[1, 2, 3].map { |n| n ** 2 } # => [1, 4, 9]
# select / reject / reduce
evens = (1..10).select { |n| n.even? }
total = evens.reduce(0) { |sum, n| sum + n }
puts total # => 30
# Named arguments
def greet(name : String, greeting : String = "Hello")
"#{greeting}, #{name}!"
end
puts greet(name: "Alice") # => Hello, Alice!
puts greet(name: "Bob", greeting: "Hi") # => Hi, Bob!
# Symbols
status = :ok
puts status # => ok
# String interpolation with expressions
n = 10
puts "#{n} squared is #{n ** 2}"
# Multi-line strings
message = <<-HEREDOC
Line one
Line two
HEREDOC
puts message
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What Is Different from Ruby
Crystal is not a Ruby superset. Several Ruby patterns do not exist or are replaced.
No runtime type changes. In Ruby, you can assign any object to any variable. In Crystal, a variable’s type is determined at compile time and cannot change to an incompatible type:
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# This is fine — Crystal infers x as Int32
x = 42
x = 100 # still Int32
# This is a compile error — x cannot become a String
# x = "hello" # Error: type must be Int32, not String
# If you need a variable that holds Int32 or String, declare the union explicitly:
x : Int32 | String = 42
x = "hello" # now this is fine
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No method_missing for arbitrary dispatch. Crystal has method_missing but it is a macro, not a runtime hook. You can use it to generate methods at compile time, but you cannot intercept calls to undefined methods at runtime — because at runtime, the method either exists in the binary or it does not.
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class Proxy
macro method_missing(call)
puts "Called: #{{{call.name}}}"
end
end
p = Proxy.new
p.anything # prints: Called: anything
p.whatever # prints: Called: whatever
# These are generated at compile time based on usage
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Union types instead of duck typing. Ruby relies on duck typing — if an object responds to a message, it works. Crystal replaces this with union types and responds_to? checked at compile time:
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# Duck typing in Ruby: any object with a .name method works
# In Crystal: express this as a union or use responds_to?
def print_name(obj : String | Symbol)
puts obj.to_s
end
# Or use responds_to? (checked at compile time):
def process(obj)
if obj.responds_to?(:upcase)
puts obj.upcase
else
puts obj.to_s
end
end
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Nil Safety
This is one of Crystal’s most practically valuable features. In Ruby, nil can appear anywhere at runtime. In Crystal, nil is a distinct type (Nil), and a variable that might be nil has type Type | Nil, also written as Type?.
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# Non-nilable String — cannot be nil
name : String = "Alice"
# Nilable String — might be nil
maybe_name : String? = nil
maybe_name = "Bob"
# The compiler forces you to handle nil before calling String methods
def greet(name : String?)
# This would be a compile error:
# puts name.upcase # Error: undefined method 'upcase' for Nil
# Correct: guard with if
if name
puts name.upcase # safe — inside the if, name is String
else
puts "No name given"
end
end
greet("alice") # ALICE
greet(nil) # No name given
# Compact: nil-check with &. (safe navigation operator, like Ruby 2.3+)
puts maybe_name&.upcase # prints "BOB" or nothing if nil
# Or use not_nil! to assert non-nil (raises at runtime if nil)
puts maybe_name.not_nil!.upcase
# Try: returns nil instead of raising on failure
def find_user(id : Int32) : String?
id == 1 ? "Alice" : nil
end
user = find_user(2)
puts user.try(&.upcase) || "not found" # not found
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Classes, Structs, and Modules
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# Classes are reference types (heap allocated)
class Animal
getter name : String
getter sound : String
def initialize(@name : String, @sound : String)
end
def speak
"#{@name} says #{@sound}"
end
end
# Structs are value types (stack allocated, copied on assignment)
struct Point
getter x : Float64
getter y : Float64
def initialize(@x : Float64, @y : Float64)
end
def distance_to(other : Point) : Float64
Math.sqrt((x - other.x) ** 2 + (y - other.y) ** 2)
end
end
# Modules as mixins
module Serializable
def to_json_string : String
# simplified example
"{\"class\": \"#{self.class.name}\"}"
end
end
class Dog < Animal
include Serializable
def initialize(name : String)
super(name, "woof")
end
end
dog = Dog.new("Rex")
puts dog.speak # Rex says woof
puts dog.to_json_string # {"class": "Dog"}
p1 = Point.new(0.0, 0.0)
p2 = Point.new(3.0, 4.0)
puts p1.distance_to(p2) # 5.0
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The Type System
Crystal’s type system is one of its most interesting design choices. You write code that looks dynamically typed, but the compiler infers types across your entire program using a global type inference algorithm. Only at boundaries where the compiler cannot infer the type — or where you want to enforce a contract — do you add annotations.
Type Inference
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# The compiler infers all of these:
x = 42 # Int32
y = 42_i64 # Int64
z = 3.14 # Float64
s = "hello" # String
b = true # Bool
arr = [1, 2, 3] # Array(Int32)
h = {"a" => 1} # Hash(String, Int32)
# Method return types are also inferred:
def add(a, b)
a + b
end
result = add(1, 2) # Int32
result2 = add(1.0, 2.0) # Float64
# But you can (and often should at public API boundaries) be explicit:
def add_ints(a : Int32, b : Int32) : Int32
a + b
end
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Union Types
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# A variable that holds Int32 or String
value : Int32 | String = 42
value = "hello" # valid
# Methods handle unions with type narrowing:
def describe(v : Int32 | String | Bool)
case v
when Int32
puts "Integer: #{v}"
when String
puts "String of length #{v.size}"
when Bool
puts "Boolean: #{v}"
end
end
describe(42) # Integer: 42
describe("hello") # String of length 5
describe(true) # Boolean: true
# Crystal infers union return types automatically:
def parse_input(s : String)
if s == "true" || s == "false"
s == "true" # Bool
elsif s.to_i?
s.to_i # Int32
else
s # String
end
end
# Return type is inferred as Bool | Int32 | String
result = parse_input("42")
puts typeof(result) # Bool | Int32 | String
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Generics
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# Generic class
class Stack(T)
def initialize
@data = [] of T
end
def push(item : T) : Nil
@data.push(item)
end
def pop : T
@data.pop
end
def peek : T
@data.last
end
def empty? : Bool
@data.empty?
end
def size : Int32
@data.size
end
end
int_stack = Stack(Int32).new
int_stack.push(1)
int_stack.push(2)
int_stack.push(3)
puts int_stack.pop # 3
puts int_stack.peek # 2
str_stack = Stack(String).new
str_stack.push("a")
str_stack.push("b")
puts str_stack.pop # b
# Generic methods
def swap(a : T, b : T) : {T, T} forall T
{b, a}
end
x, y = swap(1, 2)
puts "#{x}, #{y}" # 2, 1
a, b = swap("hello", "world")
puts "#{a}, #{b}" # world, hello
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Abstract Classes and Modules
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# Abstract class — cannot be instantiated directly
abstract class Shape
abstract def area : Float64
abstract def perimeter : Float64
def describe
"#{self.class.name}: area=#{area.round(2)}, perimeter=#{perimeter.round(2)}"
end
end
class Circle < Shape
def initialize(@radius : Float64)
end
def area : Float64
Math::PI * @radius ** 2
end
def perimeter : Float64
2 * Math::PI * @radius
end
end
class Rectangle < Shape
def initialize(@width : Float64, @height : Float64)
end
def area : Float64
@width * @height
end
def perimeter : Float64
2 * (@width + @height)
end
end
shapes = [Circle.new(5.0), Rectangle.new(4.0, 6.0)] of Shape
shapes.each { |s| puts s.describe }
# Circle: area=78.54, perimeter=31.42
# Rectangle: area=24.0, perimeter=20.0
# Abstract module (interface-like)
module Drawable
abstract def draw : String
end
module Resizable
abstract def resize(factor : Float64) : self
end
class Canvas
include Drawable
include Resizable
def initialize(@width : Float64, @height : Float64)
end
def draw : String
"Canvas #{@width}x#{@height}"
end
def resize(factor : Float64) : self
Canvas.new(@width * factor, @height * factor)
end
end
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responds_to?, typeof, Casting
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# responds_to? — compile-time check, enables duck typing
def process(obj)
{% if obj.class.has_method?(:upcase) %}
# This branch only exists if the type has upcase at compile time
{% end %}
# Runtime-style usage (Crystal evaluates at compile time per type):
if obj.responds_to?(:upcase)
obj.upcase
else
obj.to_s
end
end
# typeof — returns the type at compile time
x = 42
puts typeof(x) # Int32
puts typeof(x + 1.0) # Float64
arr = [1, "two", 3.0]
puts typeof(arr) # Array(Float64 | Int32 | String)
# as — cast (raises InvalidCastException if wrong)
value : Int32 | String = 42
num = value.as(Int32)
puts num + 1 # 43
# as? — safe cast (returns nil if wrong type)
value2 : Int32 | String = "hello"
maybe_num = value2.as?(Int32)
puts maybe_num.nil? # true
maybe_str = value2.as?(String)
puts maybe_str # hello
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Macros
Crystal’s macro system is one of its most distinctive features. Macros run at compile time, operate on the AST, and generate Crystal code. Unlike C macros (text substitution), Crystal macros understand the structure of your program.
Basic Macro Syntax
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# A simple macro — expands at compile time
macro say_hello(name)
puts "Hello, #{{{name}}}!"
end
say_hello("Crystal") # expands to: puts "Hello, Crystal!"
# {% %} for control flow within macros
# {{ }} for AST interpolation (inserting values)
# {% %} runs at compile time but produces no output
# {{ }} produces output inserted into the generated code
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Conditional Compilation
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macro debug_log(message)
{% if flag?(:debug) %}
puts "[DEBUG] #{{{message}}}"
{% end %}
end
debug_log("This only appears with -Ddebug flag")
# Check the type at compile time
macro print_type_info(var)
{% puts "Variable #{var} has type #{var.class_name}" %}
end
x = 42
print_type_info(x) # prints during compilation: Variable x has type Int32
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Iterating at Compile Time
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# Generate methods for multiple types
macro define_converters(*types)
{% for type in types %}
def to_{{type.id.downcase}}
{{type}}.new(self)
end
{% end %}
end
class MyNumber
def initialize(@value : Int32)
end
define_converters Int64, Float64, Float32
end
n = MyNumber.new(42)
# Generated methods: to_int64, to_float64, to_float32
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The record Macro
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# record is a built-in macro that creates a simple immutable struct
record Point, x : Float64, y : Float64
p = Point.new(x: 1.0, y: 2.0)
puts p.x # 1.0
puts p.y # 2.0
# record generates: struct, initialize, getters, ==, to_s, clone, copy_with
p2 = p.copy_with(x: 5.0)
puts p2 # Point(@x=5.0, @y=2.0)
puts p == p2 # false
# record with methods:
record Color, r : UInt8, g : UInt8, b : UInt8 do
def to_hex : String
"#%02X%02X%02X" % [r, g, b]
end
def luminance : Float64
0.299 * r + 0.587 * g + 0.114 * b
end
end
red = Color.new(255, 0, 0)
puts red.to_hex # #FF0000
puts red.luminance # 76.245
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getter, setter, property Macros
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class Config
# getter — generates a reader method
getter host : String
# setter — generates a writer method
setter port : Int32
# property — generates both reader and writer
property debug : Bool
# getter? — generates a boolean reader with ? suffix
getter? ssl_enabled : Bool
def initialize
@host = "localhost"
@port = 8080
@debug = false
@ssl_enabled = false
end
end
config = Config.new
puts config.host # localhost
config.port = 9090
puts config.debug? # false (would be debug without ?)
config.debug = true
puts config.ssl_enabled? # false
# Nilable with getter:
class Server
getter? running : Bool = false
property name : String?
def start
@running = true
end
end
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Serialization Macro Pattern
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# A simplified version of what JSON::Serializable does internally
macro json_fields(*fields)
def to_json_object : Hash(String, JSON::Any::Type)
result = {} of String => JSON::Any::Type
{% for field in fields %}
result[{{field.stringify}}] = @{{field}}.as(JSON::Any::Type)
{% end %}
result
end
def self.from_hash(h : Hash(String, JSON::Any::Type))
obj = allocate
{% for field in fields %}
obj.@{{field}} = h[{{field.stringify}}]
{% end %}
obj
end
end
# @type macro variable — refers to the current type being defined
macro inspect_type
puts "Current type: #{@type}"
puts "Instance vars: #{@type.instance_vars.map(&.name).join(", ")}"
end
class Person
property name : String = ""
property age : Int32 = 0
inspect_type
end
# Prints during compilation:
# Current type: Person
# Instance vars: name, age
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DSL Creation with Macros
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# Build a simple DSL for defining routes
class Router
ROUTES = {} of String => Proc(String)
macro get(path, &block)
ROUTES[{{path}}] = ->{ {{block.body}} }
end
def handle(path : String) : String
if handler = ROUTES[path]?
handler.call
else
"404 Not Found"
end
end
end
class MyApp < Router
get "/hello" do
"Hello, World!"
end
get "/status" do
"OK"
end
end
app = MyApp.new
puts app.handle("/hello") # Hello, World!
puts app.handle("/missing") # 404 Not Found
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Concurrency with Fibers
Crystal’s concurrency model is based on fibers — lightweight, cooperatively scheduled coroutines — multiplexed onto OS threads using an M:N threading model. This is conceptually similar to Go’s goroutines.
Fibers and spawn
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# spawn creates a new fiber
spawn do
puts "Fiber 1: started"
Fiber.yield # yield to another fiber
puts "Fiber 1: resumed"
end
spawn do
puts "Fiber 2: started"
Fiber.yield
puts "Fiber 2: resumed"
end
# The event loop runs fibers
Fiber.yield # give fibers a chance to run
sleep 0.1
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Channels (CSP Model)
Channels are the primary communication mechanism between fibers. Crystal’s channels are type-safe and follow the Communicating Sequential Processes (CSP) model, similar to Go.
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# Buffered channel
ch = Channel(Int32).new(10)
# Producer fiber
spawn do
(1..5).each do |i|
ch.send(i)
puts "Sent: #{i}"
end
ch.close
end
# Consumer fiber
spawn do
while value = ch.receive?
puts "Received: #{value}"
sleep 0.01
end
puts "Channel closed"
end
sleep 1 # wait for fibers to complete
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# Unbuffered (synchronous) channel — sender blocks until receiver is ready
ch = Channel(String).new
spawn do
ch.send("ping")
response = ch.receive
puts "Got response: #{response}"
end
message = ch.receive
puts "Got message: #{message}"
ch.send("pong")
sleep 0.1
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Multiple Channels with select
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require "random"
ch1 = Channel(String).new
ch2 = Channel(String).new
done = Channel(Nil).new
spawn do
sleep Random.rand(0.1..0.3)
ch1.send("from ch1")
end
spawn do
sleep Random.rand(0.1..0.3)
ch2.send("from ch2")
end
spawn do
2.times do
select
when msg = ch1.receive
puts "ch1: #{msg}"
when msg = ch2.receive
puts "ch2: #{msg}"
end
end
done.send(nil)
end
done.receive
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Real-World Pattern: Worker Pool
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require "wait_group"
def run_worker_pool(jobs : Array(Int32), num_workers : Int32)
job_ch = Channel(Int32).new(jobs.size)
result_ch = Channel(String).new(jobs.size)
wg = WaitGroup.new(num_workers)
# Enqueue jobs
jobs.each { |job| job_ch.send(job) }
job_ch.close
# Spawn workers
num_workers.times do |worker_id|
spawn do
while job = job_ch.receive?
# Simulate work
sleep 0.01
result_ch.send("Worker #{worker_id} processed job #{job}")
end
wg.done
end
end
# Collect results in a separate fiber
spawn do
wg.wait
result_ch.close
end
# Drain results
while result = result_ch.receive?
puts result
end
end
run_worker_pool((1..20).to_a, 4)
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Mutex and WaitGroup
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require "mutex"
require "wait_group"
# Mutex for shared state
counter = 0
mutex = Mutex.new
wg = WaitGroup.new(100)
100.times do |i|
spawn do
mutex.synchronize do
counter += 1
end
wg.done
end
end
wg.wait
puts "Counter: #{counter}" # 100
# Mutex with reentrant option
class SafeCache(K, V)
def initialize
@data = {} of K => V
@mutex = Mutex.new
end
def set(key : K, value : V) : Nil
@mutex.synchronize { @data[key] = value }
end
def get(key : K) : V?
@mutex.synchronize { @data[key]? }
end
def delete(key : K) : Nil
@mutex.synchronize { @data.delete(key) }
end
end
cache = SafeCache(String, Int32).new
cache.set("hits", 0)
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Real Parallelism with Multi-Threading
By default, Crystal runs all fibers on a single OS thread. To enable actual parallel execution across multiple CPU cores:
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# Compile with multi_thread support
crystal build --release -Dpreview_mt app.cr
# Or set at runtime via environment variable
CRYSTAL_WORKERS=4 ./app
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# With preview_mt, fibers can run on multiple OS threads in parallel
# The channel API remains the same — Crystal handles the synchronization
require "wait_group"
# CPU-bound parallel work
results = Array(Int64).new(8, 0_i64)
mutex = Mutex.new
wg = WaitGroup.new(8)
8.times do |i|
spawn do
# Heavy computation
sum = (1..1_000_000).reduce(0_i64) { |acc, n| acc + n }
mutex.synchronize { results[i] = sum }
wg.done
end
end
wg.wait
puts results.sum # sum of all partial sums
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Comparison to Go Goroutines and Ruby Fibers
vs. Go goroutines:
- Crystal fibers and Go goroutines are conceptually nearly identical
- Go’s scheduler is more mature and better tested at scale
- Crystal channels have the same CSP semantics as Go
- Crystal’s multi-threading (preview_mt) requires an explicit compile flag; Go’s parallelism is on by default
- Go has
select with a default branch for non-blocking; Crystal has the same
vs. Ruby fibers:
- Ruby fibers (pre-3.x) are cooperative and manually managed — call
fiber.resume explicitly
- Ruby 3.x Fibers with
Fiber::Scheduler can be made non-blocking, but the scheduler must be provided by a gem
- Crystal’s event loop is built-in and automatic — I/O operations yield automatically
- Crystal’s fibers can span OS threads (with multi-threading); Ruby’s cannot (GIL)
The LLVM Advantage
Crystal uses LLVM as its backend, which means it benefits from decades of compiler optimization work: inlining, dead code elimination, loop unrolling, vectorization (SIMD), link-time optimization, and target-specific instruction selection.
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# Standard release build
crystal build --release app.cr
# With link-time optimization (slower compile, faster binary)
crystal build --release --lto=thin app.cr
# Target-specific optimization (for the current CPU)
crystal build --release --mcpu native app.cr
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Benchmark: Fibonacci
Here’s a comparison to illustrate the performance gap with Ruby:
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# fib.cr
def fib(n : Int64) : Int64
return n if n <= 1
fib(n - 1) + fib(n - 2)
end
start = Time.monotonic
result = fib(45)
elapsed = Time.monotonic - start
puts "fib(45) = #{result}"
puts "Time: #{elapsed.total_milliseconds.round(1)}ms"
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# fib.rb
def fib(n)
return n if n <= 1
fib(n - 1) + fib(n - 2)
end
start = Process.clock_gettime(Process::CLOCK_MONOTONIC)
result = fib(45)
elapsed = Process.clock_gettime(Process::CLOCK_MONOTONIC) - start
puts "fib(45) = #{result}"
puts "Time: #{(elapsed * 1000).round(1)}ms"
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Typical results on a modern x86_64 system (release build):
| Language |
fib(45) time |
| Crystal (–release) |
~2.5s |
| Go |
~3.0s |
| Rust (release) |
~2.0s |
| Ruby 3.3 |
~70s |
| Python 3.12 |
~120s |
Crystal is competitive with Go on recursive benchmarks. Rust is slightly faster due to better stack handling. Both are 25–50x faster than Ruby on CPU-bound recursive work.
HTTP Benchmark Context
For network services, I/O dominates and the gap narrows — but Crystal’s lower per-request overhead still matters at scale:
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# Minimal HTTP server for benchmarking
require "http/server"
server = HTTP::Server.new do |context|
context.response.content_type = "text/plain"
context.response.print "Hello, World!"
end
server.bind_tcp("0.0.0.0", 8080)
puts "Listening on http://0.0.0.0:8080"
server.listen
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At 50k req/s with wrk, Crystal’s HTTP::Server uses roughly 30–40MB of memory. A comparable Rails app at the same request rate requires 10–20 worker processes, each using 200–400MB. The practical win is not raw speed but resource efficiency — fewer servers, lower cloud bill.
Zero-Cost Abstractions
Crystal’s abstractions — generics, blocks, iterators — compile to code as efficient as hand-written loops. The Crystal compiler monomorphizes generics (creates specialized versions per type), inlines blocks aggressively, and the LLVM backend can vectorize loops over typed arrays.
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# This iterator chain:
result = (1..1_000_000)
.select { |n| n % 2 == 0 }
.map { |n| n * n }
.first(10)
# Compiles to roughly equivalent code as this manual loop:
result2 = [] of Int32
count = 0
(1..1_000_000).each do |n|
break if count == 10
if n % 2 == 0
result2 << n * n
count += 1
end
end
# Both produce the same binary output with --release
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Standard Library Highlights
HTTP Server and Client
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require "http/server"
require "http/client"
require "json"
# HTTP Server with routing
server = HTTP::Server.new do |ctx|
req = ctx.request
res = ctx.response
case {req.method, req.path}
when {"GET", "/"}
res.content_type = "application/json"
res.print({status: "ok", message: "Crystal API"}.to_json)
when {"GET", "/users"}
users = [{id: 1, name: "Alice"}, {id: 2, name: "Bob"}]
res.content_type = "application/json"
res.print(users.to_json)
when {"POST", "/echo"}
body = req.body.try(&.gets_to_end) || ""
res.content_type = "text/plain"
res.print("Echo: #{body}")
else
res.status = HTTP::Status::NOT_FOUND
res.print("Not found")
end
end
address = server.bind_tcp(8080)
puts "Listening on #{address}"
server.listen
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require "http/client"
require "json"
# HTTP Client
response = HTTP::Client.get("https://httpbin.org/json")
puts response.status_code # 200
puts response.body[0..100]
# With headers and timeout
HTTP::Client.new("api.example.com", tls: true) do |client|
client.connect_timeout = 5.seconds
client.read_timeout = 30.seconds
response = client.get("/v1/resource",
headers: HTTP::Headers{"Authorization" => "Bearer token123"})
puts response.status
puts response.body
end
# POST with JSON body
payload = {name: "Alice", age: 30}.to_json
response = HTTP::Client.post(
"https://httpbin.org/post",
headers: HTTP::Headers{"Content-Type" => "application/json"},
body: payload
)
puts response.status_code
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JSON::Serializable
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require "json"
# Annotation-based JSON serialization (zero runtime reflection)
class User
include JSON::Serializable
property id : Int32
property name : String
property email : String
@[JSON::Field(key: "created_at")]
property created_at : Time
@[JSON::Field(ignore: true)]
property password_hash : String = ""
def initialize(@id, @name, @email, @created_at = Time.utc)
end
end
# Serialize to JSON
user = User.new(1, "Alice", "alice@example.com")
json_str = user.to_json
puts json_str
# {"id":1,"name":"Alice","email":"alice@example.com","created_at":"..."}
# Deserialize from JSON
json = %({ "id": 2, "name": "Bob", "email": "bob@example.com", "created_at": "2025-01-01T00:00:00Z" })
user2 = User.from_json(json)
puts user2.name # Bob
# Array of users
json_array = "[#{json_str}, #{json_str}]"
users = Array(User).from_json(json_array)
puts users.size # 2
# JSON::Any for dynamic JSON
raw = JSON.parse(%({ "x": 1, "y": [2, 3] }))
puts raw["x"].as_i # 1
puts raw["y"][0].as_i # 2
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Database Access with DB
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require "db"
require "pg" # or sqlite3, mysql2
# Generic DB interface — works with any driver
DB.open("postgres://user:pass@localhost:5432/mydb") do |db|
# Create table
db.exec "CREATE TABLE IF NOT EXISTS users (
id SERIAL PRIMARY KEY,
name VARCHAR(100),
email VARCHAR(200)
)"
# Insert
db.exec "INSERT INTO users (name, email) VALUES ($1, $2)",
"Alice", "alice@example.com"
# Query single row
user = db.query_one "SELECT id, name, email FROM users WHERE name = $1",
"Alice",
as: {Int32, String, String}
id, name, email = user
puts "Found: #{name} (#{email})"
# Query multiple rows
db.query "SELECT id, name FROM users" do |rs|
rs.each do
id = rs.read(Int32)
name = rs.read(String)
puts " #{id}: #{name}"
end
end
# With transaction
db.transaction do |tx|
conn = tx.connection
conn.exec "INSERT INTO users (name, email) VALUES ($1, $2)", "Bob", "bob@example.com"
conn.exec "INSERT INTO users (name, email) VALUES ($1, $2)", "Carol", "carol@example.com"
# Automatically committed; rolls back on exception
end
end
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File, Dir, Path
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require "file"
# Reading and writing files
File.write("/tmp/hello.txt", "Hello, Crystal!\n")
contents = File.read("/tmp/hello.txt")
puts contents.chomp # Hello, Crystal!
# Line by line
File.each_line("/tmp/hello.txt") do |line|
puts line.upcase
end
# File info
info = File.info("/tmp/hello.txt")
puts "Size: #{info.size} bytes"
puts "Modified: #{info.modification_time}"
# Path manipulation
path = Path.new("/home/user/projects/app/src/main.cr")
puts path.basename # main.cr
puts path.stem # main
puts path.extension # .cr
puts path.dirname # /home/user/projects/app/src
puts path.parent # /home/user/projects/app/src
# Directory operations
Dir.mkdir_p("/tmp/crystal_test/nested")
Dir.each_child("/tmp") do |entry|
puts entry
end
# Glob
Dir.glob("/tmp/**/*.cr").each do |file|
puts file
end
# Temp files
File.tempfile("crystal", ".tmp") do |f|
f.print("temporary data")
puts f.path
end # automatically deleted
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Logging
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require "log"
# Configure backend
Log.setup do |config|
backend = Log::IOBackend.new(STDOUT)
config.bind("*", :debug, backend)
end
# Module-scoped logger
Log.for("myapp.http").info { "Server started on port 8080" }
Log.for("myapp.db").debug { "Query executed in 2ms" }
# Structured logging
Log.for("myapp").error(exception: RuntimeError.new("oops")) do |entry|
entry.emit("Database error", query: "SELECT * FROM users", duration_ms: 500)
end
# In a class
class MyService
Log = ::Log.for(self)
def process(item : String)
Log.debug { "Processing: #{item}" }
# do work
Log.info { "Processed: #{item}" }
rescue ex
Log.error(exception: ex) { "Failed to process: #{item}" }
end
end
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Testing with spec
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# spec/user_spec.cr
require "spec"
class Calculator
def add(a : Int32, b : Int32) : Int32
a + b
end
def divide(a : Float64, b : Float64) : Float64
raise ArgumentError.new("Division by zero") if b == 0.0
a / b
end
end
describe Calculator do
subject { Calculator.new }
describe "#add" do
it "adds two positive numbers" do
subject.add(2, 3).should eq(5)
end
it "handles negative numbers" do
subject.add(-1, 1).should eq(0)
end
end
describe "#divide" do
it "divides correctly" do
result = subject.divide(10.0, 4.0)
result.should be_close(2.5, 0.001)
end
it "raises on division by zero" do
expect_raises(ArgumentError, "Division by zero") do
subject.divide(5.0, 0.0)
end
end
end
end
# Run with: crystal spec
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OptionParser
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require "option_parser"
# Build a CLI tool with argument parsing
options = {
host: "localhost",
port: 8080,
verbose: false,
output: nil.as(String?),
}
parser = OptionParser.new do |p|
p.banner = "Usage: myapp [options]"
p.on("-h HOST", "--host=HOST", "Hostname to connect to") do |h|
options = options.merge(host: h)
end
p.on("-p PORT", "--port=PORT", "Port number") do |port|
options = options.merge(port: port.to_i)
end
p.on("-v", "--verbose", "Enable verbose output") do
options = options.merge(verbose: true)
end
p.on("-o FILE", "--output=FILE", "Output file") do |f|
options = options.merge(output: f)
end
p.on("--help", "Show this help") do
puts p
exit
end
p.invalid_option do |flag|
STDERR.puts "ERROR: #{flag} is not a valid option."
STDERR.puts p
exit(1)
end
end
parser.parse
puts "Connecting to #{options[:host]}:#{options[:port]}"
puts "Verbose: #{options[:verbose]}"
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C Bindings
Crystal’s Foreign Function Interface (FFI) lets you call C libraries directly. This is how Crystal’s standard library wraps OpenSSL, libc, zlib, and others.
Basic lib Block
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# Bind to C's standard library functions
@[Link("c")]
lib LibC
# Function binding
fun puts(s : UInt8*) : Int32
fun strlen(s : UInt8*) : SizeT
fun malloc(size : SizeT) : Void*
fun free(ptr : Void*) : Void
# Type aliases
alias SizeT = UInt64 # on 64-bit systems
# C struct
struct Timespec
tv_sec : Int64
tv_nsec : Int64
end
fun clock_gettime(clk_id : Int32, tp : Timespec*) : Int32
CLOCK_REALTIME = 0
CLOCK_MONOTONIC = 1
end
# Use the binding
ts = LibC::Timespec.new
LibC.clock_gettime(LibC::CLOCK_MONOTONIC, pointerof(ts))
puts "Seconds: #{ts.tv_sec}, Nanoseconds: #{ts.tv_nsec}"
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Binding a Real C Library — libsqlite3
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# sqlite3_binding.cr
@[Link("sqlite3")]
lib LibSQLite3
alias Database = Void*
alias Statement = Void*
alias Callback = (Void*, Int32, UInt8**, UInt8**) -> Int32
SQLITE_OK = 0
SQLITE_ROW = 100
SQLITE_DONE = 101
fun open(filename : UInt8*, db : Database*) : Int32
fun close(db : Database) : Int32
fun prepare_v2(
db : Database,
sql : UInt8*,
nbyte : Int32,
stmt : Statement*,
tail : UInt8**
) : Int32
fun step(stmt : Statement) : Int32
fun column_count(stmt : Statement) : Int32
fun column_text(stmt : Statement, col : Int32) : UInt8*
fun column_int(stmt : Statement, col : Int32) : Int32
fun column_double(stmt : Statement, col : Int32) : Float64
fun column_type(stmt : Statement, col : Int32) : Int32
fun finalize(stmt : Statement) : Int32
fun errmsg(db : Database) : UInt8*
fun exec(
db : Database,
sql : UInt8*,
callback : Callback,
arg : Void*,
errmsg : UInt8**
) : Int32
SQLITE_INTEGER = 1
SQLITE_FLOAT = 2
SQLITE_TEXT = 3
SQLITE_BLOB = 4
SQLITE_NULL = 5
end
# Wrapper class
class SQLiteDB
def initialize(path : String)
@db = uninitialized LibSQLite3::Database
rc = LibSQLite3.open(path, pointerof(@db))
raise "Cannot open database: #{error_msg}" if rc != LibSQLite3::SQLITE_OK
end
def exec(sql : String) : Nil
errmsg = Pointer(UInt8).null
rc = LibSQLite3.exec(@db, sql, nil, nil, pointerof(errmsg))
raise "SQL error: #{String.new(errmsg)}" if rc != LibSQLite3::SQLITE_OK
end
def query(sql : String) : Array(Array(String))
stmt = uninitialized LibSQLite3::Statement
rc = LibSQLite3.prepare_v2(@db, sql, -1, pointerof(stmt), nil)
raise "Prepare failed: #{error_msg}" if rc != LibSQLite3::SQLITE_OK
rows = [] of Array(String)
while LibSQLite3.step(stmt) == LibSQLite3::SQLITE_ROW
cols = LibSQLite3.column_count(stmt)
row = (0...cols).map do |i|
text = LibSQLite3.column_text(stmt, i)
text.null? ? "NULL" : String.new(text)
end
rows << row
end
LibSQLite3.finalize(stmt)
rows
end
def close
LibSQLite3.close(@db)
end
private def error_msg : String
String.new(LibSQLite3.errmsg(@db))
end
end
# Usage
db = SQLiteDB.new(":memory:")
db.exec("CREATE TABLE users (id INTEGER PRIMARY KEY, name TEXT)")
db.exec("INSERT INTO users VALUES (1, 'Alice')")
db.exec("INSERT INTO users VALUES (2, 'Bob')")
rows = db.query("SELECT * FROM users")
rows.each { |row| puts row.join(", ") }
# 1, Alice
# 2, Bob
db.close
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Union and Pointer Types
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# C unions in Crystal
lib LibExample
union Number
i : Int32
f : Float32
bytes : UInt8[4]
end
struct ComplexStruct
flag : Int32
data : Number
next : ComplexStruct* # pointer to self
end
end
# Pointer arithmetic (use carefully)
ptr = Pointer(Int32).malloc(4)
4.times { |i| ptr[i] = i * 10 }
4.times { |i| puts ptr[i] } # 0, 10, 20, 30
ptr.free
# Convert between Crystal String and C char*
def c_string_demo
crystal_str = "Hello from Crystal"
# Crystal String to C char*
crystal_str.to_unsafe # returns UInt8*
# C char* to Crystal String
c_ptr = crystal_str.to_unsafe
back_to_crystal = String.new(c_ptr)
puts back_to_crystal
end
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Shards — The Package Manager
Shards is Crystal’s package manager. It reads a shard.yml file for dependencies and resolves them from Git repositories.
shard.yml
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name: my_api
version: 0.1.0
description: A Crystal API service
authors:
- Your Name <you@example.com>
crystal: ">= 1.10.0"
license: MIT
dependencies:
kemal:
github: kemalcr/kemal
version: "~> 1.3"
jennifer:
github: imdrasil/jennifer.cr
version: "~> 0.12"
pg:
github: will/crystal-pg
version: "~> 0.28"
redis:
github: stefanwille/crystal-redis
version: "~> 2.9"
development_dependencies:
ameba:
github: crystal-ameba/ameba
version: "~> 1.5"
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# Install dependencies
shards install
# Update dependencies
shards update
# Check for outdated shards
shards check
# Build the project
shards build
# Build a specific target
shards build my_api --release
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Kemal — Sinatra-Style Web Framework
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# shard.yml dependency: kemal
require "kemal"
# Middleware
before_all do |env|
env.response.headers["X-Powered-By"] = "Crystal/Kemal"
end
# Routes
get "/" do
"Hello, Kemal!"
end
get "/users/:id" do |env|
id = env.params.url["id"].to_i
# fetch from DB in real app
{id: id, name: "User #{id}"}.to_json
end
post "/users" do |env|
body = env.request.body.try(&.gets_to_end) || ""
data = JSON.parse(body)
# create user in real app
env.response.status_code = 201
{id: 999, name: data["name"]}.to_json
end
# Static files
serve_static({"gzip" => true, "dir_listing" => false})
# Error handling
error 404 do
"Route not found"
end
error 500 do |env, err|
env.response.content_type = "application/json"
{error: err.message}.to_json
end
Kemal.run(port: 3000)
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Lucky — Full-Stack Web Framework
Lucky is the most full-featured Crystal web framework, inspired by Phoenix and Rails but Crystal-native:
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# Lucky uses a strongly-typed routing system
# In src/actions/users/show.cr
class Users::Show < BrowserAction
get "/users/:user_id" do
user = UserQuery.new.id(user_id).first
html ShowPage, user: user
end
end
# Type-safe query builder (no string SQL)
class UserQuery < User::BaseQuery
def admins
role("admin")
end
def recently_active
created_at.gt(1.week.ago)
end
end
# Usage
users = UserQuery.new.admins.recently_active.select
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Jennifer ORM
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require "jennifer"
require "jennifer/adapter/postgres"
Jennifer::Config.configure do |conf|
conf.host = "localhost"
conf.db = "myapp_development"
conf.user = "postgres"
conf.password = "secret"
conf.adapter = "postgres"
end
class User < Jennifer::Model::Base
with_timestamps
mapping(
id: Primary32,
name: String,
email: String,
role: {type: String, default: "user"},
created_at: Time?,
updated_at: Time?,
)
has_many :posts, Post
validates_presence :name, :email
validates_uniqueness :email
validates_format :email, /\A[\w+\-.]+@[a-z\d\-.]+\.[a-z]+\z/i
end
class Post < Jennifer::Model::Base
with_timestamps
mapping(
id: Primary32,
title: String,
body: String,
user_id: Int32,
created_at: Time?,
updated_at: Time?,
)
belongs_to :user, User
end
# Query interface
admin_users = User.where { _role == "admin" }.order(name: :asc).to_a
puts admin_users.map(&.name)
user = User.find!(1)
user.posts.where { _created_at > 1.week.ago }.each do |post|
puts post.title
end
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Athena Framework
Athena is an annotation-driven framework for building structured APIs:
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require "athena"
@[ARTA::Prefix("api/v1")]
class UserController < ATH::Controller
@[ARTA::Get("/users")]
def list_users : Array(User)
UserRepository.all
end
@[ARTA::Get("/users/:id")]
def show_user(id : Int32) : User
UserRepository.find(id) || raise ATH::Exceptions::NotFound.new("User #{id} not found")
end
@[ARTA::Post("/users")]
@[ATHA::View(status: HTTP::Status::CREATED)]
def create_user(request : ATH::Request) : User
user_data = ATH::Serializer.deserialize(UserCreateDTO, request.body.not_nil!, :json)
UserRepository.create(user_data)
end
end
ATH.run
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crystal build vs crystal run
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# Run directly (compiles in temp dir, slower startup, no optimization)
crystal run app.cr
# Compile to binary (debug mode, fast compile)
crystal build app.cr -o app
# Compile with optimizations (slower compile, faster binary)
crystal build --release app.cr -o app
# Show compiler progress
crystal build --progress app.cr
# Show generated LLVM IR (useful for understanding optimization)
crystal build --emit llvm-ir app.cr
cat app.ll | head -50
# Show generated assembly
crystal build --emit asm app.cr
# Link-time optimization (best for final production builds)
crystal build --release --lto=thin app.cr
# Cross-compile (generate LLVM IR for another target, link on target)
crystal build --cross-compile --target x86_64-linux-musl app.cr
# Then on the target: cc app.o -o app $(crystal env CRYSTAL_PATH)/src/ext/libcrystal.a -lpcre2-8 -lgc -lpthread -lm -levent -lrt -ldl
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Running Tests
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# spec/calculator_spec.cr
require "spec"
require "../src/calculator"
describe Calculator do
it "adds numbers" do
Calculator.add(2, 3).should eq(5)
end
it "handles floats" do
Calculator.add(1.5, 2.5).should be_close(4.0, 0.001)
end
pending "handles complex numbers" do
# Not yet implemented
end
end
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# Run all specs
crystal spec
# Run a specific file
crystal spec spec/calculator_spec.cr
# Run a specific line
crystal spec spec/calculator_spec.cr:10
# Verbose output
crystal spec --verbose
# With release mode (rare, for performance testing)
crystal spec --release
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# Format all Crystal files in place
crystal tool format
# Check formatting without modifying (for CI)
crystal tool format --check
# Generate documentation
crystal docs
# Opens browser at docs/index.html
# Check for unreachable code and other hints
crystal tool unreachable src/app.cr
# Context tool: show type of expression at a location
crystal tool context src/app.cr:42:10
# Hierarchy tool: show class hierarchy
crystal tool hierarchy src/app.cr
# Implementations: find method implementations
crystal tool implementations src/app.cr:15:5
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Ameba — Crystal’s Linter
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# Install via shard (development dependency)
# Then run:
./bin/ameba
# With specific rules
./bin/ameba --only Layout/TrailingWhitespace,Naming/MethodNames src/
# Ignore a rule for a specific line:
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x = 1 # ameba:disable Lint/UselessAssign
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Docker-Based Builds
For production deployments, multi-stage Docker builds keep images small:
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# Dockerfile
FROM crystallang/crystal:1.15-alpine AS builder
WORKDIR /app
# Install shards
COPY shard.yml shard.lock ./
RUN shards install --production
# Copy source and build
COPY src/ ./src/
RUN crystal build --release --static src/main.cr -o app
# Final image — scratch or distroless
FROM alpine:3.19
RUN apk add --no-cache libgcc
WORKDIR /app
COPY --from=builder /app/app .
EXPOSE 8080
CMD ["./app"]
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# Build and run
docker build -t my-crystal-app .
docker run -p 8080:8080 my-crystal-app
# Multi-platform build (Crystal cross-compilation)
docker buildx build --platform linux/amd64,linux/arm64 -t my-app .
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Cross-Compilation
Crystal’s cross-compilation story requires compiling the LLVM IR on the build machine and linking on the target:
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# Step 1: On the build machine, generate .o file targeting musl for static binary
crystal build --cross-compile --target x86_64-linux-musl app.cr
# Produces: app.o and a link command
# Step 2: On the target (or in a musl container), link:
cc app.o -o app -rdynamic -static \
$(pkg-config --libs crystal) \
-lpcre2-8 -lgc -lpthread -lm -levent
# Easier: use Docker with cross-compilation container
docker run --rm -v $(pwd):/workspace crystallang/crystal:1.15-alpine \
crystal build --release --static /workspace/src/app.cr -o /workspace/app
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Honest Assessment
Crystal is a genuinely good language. It is also a small language with real limitations. Here is an honest accounting.
Where Crystal Wins
Ruby teams that need speed. If your team writes Ruby and you need a service that handles 10x more load on the same hardware, Crystal is the most natural migration path. The syntax is familiar, the idioms are the same, and the productivity hit during migration is smaller than with Go or Rust.
CLI tools. Crystal produces single-file static binaries (with --static on Linux). No runtime to install, fast startup, small binary. This is where Crystal’s compilation model shines — you ship an executable, nothing else.
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# A fast, self-contained CLI tool
require "option_parser"
require "colorize"
version = "1.0.0"
input_file = ""
verbose = false
OptionParser.parse do |p|
p.banner = "Usage: mytool [options] <file>"
p.on("-v", "--verbose", "Verbose output") { verbose = true }
p.on("--version", "Show version") { puts version; exit }
p.on("-h", "--help", "Show help") { puts p; exit }
end
input_file = ARGV.first? || begin
STDERR.puts "Error: no input file".colorize(:red)
exit 1
end
puts "Processing #{input_file}".colorize(:green) if verbose
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Network services with high connection counts. Crystal’s fiber model handles thousands of concurrent connections efficiently. A Crystal HTTP service with 10,000 concurrent connections uses less memory than an equivalent Ruby/Python service with a fraction of the connections.
Performance-sensitive internal services. Data processing pipelines, log parsers, protocol servers, background job processors — these are Crystal’s wheelhouse.
Where Crystal Loses
Compilation speed. Crystal’s compiler is slower than Go’s — noticeably so on large projects. A Go codebase of 100k lines compiles in 2–3 seconds. A Crystal codebase of comparable size may take 30–60 seconds in development mode. The global type inference Crystal performs is expensive. Crystal 1.x has improved this significantly compared to 0.x, but it remains a pain point.
Project size Crystal (debug) Go Rust (debug)
10k lines ~3s ~0.5s ~8s
50k lines ~15s ~2s ~40s
100k lines ~40s ~4s ~90s
Windows support. Crystal’s Windows support is available but not first-class. Many shards do not test on Windows. The standard library has some gaps. If your team develops on Windows, Crystal adds friction. The Crystal 1.x series has made substantial progress, but Linux and macOS remain the primary development targets.
Ecosystem size. Crystal’s ecosystem is a fraction of Ruby’s, Go’s, or Python’s. For most infrastructure tasks, you will find a shard. For specialized domains — machine learning, scientific computing, financial modeling — you will either write C bindings or abandon Crystal for a language with better libraries. There is no Crystal equivalent of NumPy, scikit-learn, PyTorch, or Pandas.
Community and hiring. Crystal’s community is small. Finding engineers who know Crystal is difficult. If your team turns over, onboarding new engineers to Crystal requires more time than onboarding to Ruby, Go, or Python.
Parallelism maturity. The preview_mt multi-threading flag has been “preview” for years. It works, but it is not the default because the standard library has not been fully audited for thread safety. Writing correctly parallel Crystal code requires understanding which operations are fiber-safe but not thread-safe. Go’s goroutines run on multiple OS threads by default and the runtime is more battle-hardened.
Garbage collector pauses. Crystal uses the Boehm GC by default, which can introduce pauses that are unpredictable under heavy allocation. For soft real-time work, this matters. Rust’s ownership model eliminates GC entirely; Go’s GC is highly tuned; Crystal’s is functional but less optimized than Go’s.
Compilation Speed Workaround
For development iteration speed, Crystal’s crystal run and incremental compilation via --incremental (experimental) help. The community also uses cruse and similar tools to hot-reload during development.
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# Use crystal run for fast iteration (skips final optimization)
crystal run src/app.cr
# Check types without generating code (even faster feedback)
crystal tool hierarchy src/app.cr 2>&1 | head -20
# Parallel compilation (experimental, Crystal 1.13+)
crystal build --jobs=4 src/app.cr
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A Practical Decision Framework
Use Crystal if:
- Your team knows Ruby and needs 10–100x better throughput
- You are building CLI tools that need fast startup and no runtime dependency
- You are building network services where memory efficiency matters
- You are comfortable with a smaller ecosystem and community
Use Go instead if:
- You need first-class Windows support
- Compilation speed is critical for CI/CD
- You need the largest ecosystem for cloud-native work
- Parallelism is core to your problem and you want a battle-hardened runtime
Use Rust instead if:
- You need maximum performance or zero GC pauses
- Memory safety guarantees are a hard requirement
- You are doing embedded or systems work that cannot tolerate a GC
Stick with Ruby if:
- Your bottleneck is not CPU or memory
- Your team’s productivity in Ruby outweighs the performance delta
- You need Rails, Sidekiq, or other Ruby ecosystem staples
Putting It Together: A Complete Example
Here is a small but complete Crystal service that demonstrates the language features in combination: an HTTP API with JSON serialization, a simple in-memory datastore, concurrent request handling, and graceful shutdown.
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# src/api_server.cr
require "http/server"
require "json"
require "log"
require "mutex"
require "option_parser"
require "signal"
# Configuration
record Config,
host : String = "0.0.0.0",
port : Int32 = 8080,
workers : Int32 = 4
# Domain model
struct Item
include JSON::Serializable
property id : Int32
property name : String
property value : Float64
property created_at : Time
def initialize(@id, @name, @value)
@created_at = Time.utc
end
end
# In-memory store with thread-safe access
class ItemStore
Log = ::Log.for(self)
def initialize
@items = {} of Int32 => Item
@mutex = Mutex.new
@next_id = 0
end
def create(name : String, value : Float64) : Item
@mutex.synchronize do
@next_id += 1
item = Item.new(@next_id, name, value)
@items[@next_id] = item
Log.debug { "Created item ##{item.id}: #{item.name}" }
item
end
end
def find(id : Int32) : Item?
@mutex.synchronize { @items[id]? }
end
def all : Array(Item)
@mutex.synchronize { @items.values }
end
def delete(id : Int32) : Bool
@mutex.synchronize do
if @items.delete(id)
Log.debug { "Deleted item ##{id}" }
true
else
false
end
end
end
def count : Int32
@mutex.synchronize { @items.size }
end
end
# Request handler
class RequestHandler
Log = ::Log.for(self)
def initialize(@store : ItemStore)
end
def call(ctx : HTTP::Server::Context) : Nil
req = ctx.request
res = ctx.response
res.headers["Content-Type"] = "application/json"
start = Time.monotonic
begin
route(req, res)
rescue ex : JSON::ParseException
res.status = HTTP::Status::BAD_REQUEST
res.print({error: "Invalid JSON: #{ex.message}"}.to_json)
rescue ex
Log.error(exception: ex) { "Unhandled error in #{req.method} #{req.path}" }
res.status = HTTP::Status::INTERNAL_SERVER_ERROR
res.print({error: "Internal server error"}.to_json)
end
elapsed_ms = (Time.monotonic - start).total_milliseconds
Log.info { "#{req.method} #{req.path} → #{res.status.code} (#{elapsed_ms.round(1)}ms)" }
end
private def route(req : HTTP::Request, res : HTTP::Server::Response) : Nil
case {req.method, req.path}
when {"GET", "/health"}
res.print({status: "ok", items: @store.count}.to_json)
when {"GET", "/items"}
res.print(@store.all.to_json)
when {"POST", "/items"}
body = req.body.try(&.gets_to_end) || raise "Empty body"
data = JSON.parse(body)
name = data["name"].as_s
value = data["value"].as_f
item = @store.create(name, value)
res.status = HTTP::Status::CREATED
res.print(item.to_json)
else
# Check for /items/:id patterns
if req.path =~ %r{^/items/(\d+)$}
id = $1.to_i
item = @store.find(id)
case req.method
when "GET"
if item
res.print(item.to_json)
else
res.status = HTTP::Status::NOT_FOUND
res.print({error: "Item #{id} not found"}.to_json)
end
when "DELETE"
if @store.delete(id)
res.status = HTTP::Status::NO_CONTENT
else
res.status = HTTP::Status::NOT_FOUND
res.print({error: "Item #{id} not found"}.to_json)
end
else
res.status = HTTP::Status::METHOD_NOT_ALLOWED
res.print({error: "Method not allowed"}.to_json)
end
else
res.status = HTTP::Status::NOT_FOUND
res.print({error: "Route not found"}.to_json)
end
end
end
end
# Main
Log.setup do |c|
backend = Log::IOBackend.new(STDOUT, formatter: Log::ShortFormat)
c.bind("*", :info, backend)
end
config = Config.new
OptionParser.parse do |p|
p.banner = "Usage: api_server [options]"
p.on("-H HOST", "--host=HOST", "Listen host (default: 0.0.0.0)") { |h| config = Config.new(h, config.port, config.workers) }
p.on("-p PORT", "--port=PORT", "Listen port (default: 8080)") { |p| config = Config.new(config.host, p.to_i, config.workers) }
p.on("-h", "--help", "Show help") { puts p; exit }
end
store = ItemStore.new
handler = RequestHandler.new(store)
server = HTTP::Server.new { |ctx| handler.call(ctx) }
Signal::INT.trap { server.close }
Signal::TERM.trap { server.close }
address = server.bind_tcp(config.host, config.port)
Log.for("main").info { "Server started at http://#{address}" }
server.listen
Log.for("main").info { "Server stopped" }
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# Build and run
crystal build --release src/api_server.cr -o api_server
./api_server --port 8080
# Test it
curl -s http://localhost:8080/health | jq .
# {"status":"ok","items":0}
curl -s -X POST http://localhost:8080/items \
-H "Content-Type: application/json" \
-d '{"name": "widget", "value": 9.99}' | jq .
curl -s http://localhost:8080/items | jq .
curl -s http://localhost:8080/items/1 | jq .
curl -s -X DELETE http://localhost:8080/items/1
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Getting Started
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# Install Crystal (Linux — via snap)
sudo snap install crystal --classic
# Install Crystal (macOS — via Homebrew)
brew install crystal
# Install Crystal (Debian/Ubuntu — via apt)
curl -fsSL https://crystal-lang.org/install.sh | sudo bash
# Verify installation
crystal --version
shards --version
# Create a new project
mkdir my_project && cd my_project
crystal init app my_project
# Project structure created:
# my_project/
# src/
# my_project.cr
# spec/
# my_project_spec.cr
# spec_helper.cr
# shard.yml
# .gitignore
# README.md
# Run the project
crystal run src/my_project.cr
# Run specs
crystal spec
# Format code
crystal tool format
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Summary
Crystal occupies a specific and defensible niche. It is not trying to replace Ruby — it is trying to answer the question “what would Ruby look like if we compiled it and enforced types?” The answer turns out to be quite usable: a language that experienced Ruby developers can be productive in within days, that produces binaries competitive with Go and near-competitive with Rust, and that has a surprisingly capable standard library for web services, CLI tools, and systems programming.
The honest trade-offs:
- Compilation speed is worse than Go, though much better than Rust
- The ecosystem is small — expect to write more from scratch or bind C libraries
- Windows support is functional but not first-class
- Parallelism is available but requires intentional use of
preview_mt
The genuine wins:
- Ruby syntax fluency transfers almost completely
- Nil safety eliminates an entire class of production bugs
- The macro system is powerful without being opaque
- LLVM-backed performance with zero-cost abstractions
- Single static binary deployment with no runtime dependency
If you write Ruby today and have a service that needs to handle 50x more throughput, Crystal is likely the path of least resistance. If you are evaluating compiled languages from scratch, Crystal is worth a serious look — particularly if you value expressiveness and are willing to accept a smaller ecosystem in exchange.
The language has reached 1.x stability. Production users exist. The core team continues active development. Crystal is not a hobby project — it is a production-ready language for teams who know what they are getting into.
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