Optimization Strategies
Comprehensive guide to optimizing Azu applications for maximum performance and efficiency.
Overview
Azu provides multiple layers of optimization opportunities, from compile-time optimizations to runtime performance tuning. This guide covers strategies for achieving optimal performance in production environments.
Compile-Time Optimizations
Type Safety Benefits
# Compile-time optimizations through type safety
struct OptimizedEndpoint
include Endpoint(OptimizedRequest, OptimizedResponse)
get "/optimized/:id"
def call : OptimizedResponse
# Crystal's type system eliminates runtime checks
id = @request.params["id"].to_i32
# Compile-time method resolution
user = User.find(id)
# Zero-cost abstractions
OptimizedResponse.new(user)
end
end
Benefits:
Method dispatch: Resolved at compile time
Memory layout: Optimized for type safety
Null checks: Eliminated where possible
Macro Optimizations
# Macro-based optimizations
macro optimized_routes
{% for method, path in ROUTES %}
{{method.id}} "{{path}}"
{% end %}
end
# Compile-time route generation
struct OptimizedRouter
include Router
optimized_routes
end
Memory Management
Object Pooling
# Object pooling for high-frequency operations
class ConnectionPool
@pool = [] of Database::Connection
@mutex = Mutex.new
def get_connection : Database::Connection
@mutex.synchronize do
if @pool.empty?
create_new_connection
else
@pool.pop
end
end
end
def return_connection(connection)
@mutex.synchronize do
@pool << connection
end
end
end
Memory-Efficient Data Structures
# Use appropriate data structures
struct MemoryOptimizedEndpoint
include Endpoint(MemoryRequest, MemoryResponse)
def call : MemoryResponse
# Use Array instead of Set for small collections
small_collection = Array(String).new(10)
# Use String.build for string concatenation
result = String.build do |str|
str << "prefix"
str << @request.data
str << "suffix"
end
MemoryResponse.new(result)
end
end
Database Optimization
Query Optimization
# Optimized database queries
struct DatabaseOptimizedEndpoint
include Endpoint(DbRequest, DbResponse)
def call : DbResponse
# Use eager loading to avoid N+1 queries
users = User.includes(:posts, :comments)
.where(active: true)
.limit(100)
# Use select to fetch only needed columns
user_data = users.select(:id, :name, :email)
DbResponse.new(user_data)
end
end
Connection Pooling
# Database connection pooling
CONFIG.database = {
pool_size: 20,
pool_timeout: 5.seconds,
checkout_timeout: 5.seconds,
max_retries: 3
}
# Connection pool configuration
class DatabasePool
@pool = DB::Pool.new(
DB::Connection.new(CONFIG.database_url),
initial_pool_size: CONFIG.database.pool_size,
max_pool_size: CONFIG.database.pool_size * 2
)
end
Caching Strategies
Response Caching
# Response caching with TTL
struct CachedEndpoint
include Endpoint(CacheRequest, CacheResponse)
def call : CacheResponse
cache_key = generate_cache_key(@request)
if cached_response = cache.get(cache_key)
return CacheResponse.from_cache(cached_response)
end
# Generate fresh response
response = generate_response(@request)
# Cache for 5 minutes
cache.set(cache_key, response, ttl: 5.minutes)
CacheResponse.new(response)
end
end
Fragment Caching
# Template fragment caching
class FragmentCache
@cache = {} of String => String
def cached_fragment(key, ttl = 1.hour)
if cached = @cache[key]?
return cached
end
# Generate fragment
fragment = yield
# Cache fragment
@cache[key] = fragment
fragment
end
end
Async Processing
Background Jobs
# Background job processing
struct AsyncEndpoint
include Endpoint(AsyncRequest, AsyncResponse)
def call : AsyncResponse
# Spawn background job
spawn do
process_heavy_task(@request.data)
end
# Return immediately
AsyncResponse.new(job_id: generate_job_id)
end
end
WebSocket Optimization
# Optimized WebSocket handling
class OptimizedChannel < Azu::Channel
ws "/optimized"
def on_message(message)
# Use spawn for non-blocking operations
spawn do
result = process_message_async(message)
broadcast(result)
end
end
def on_connect
# Minimal connection setup
subscribe_to_channel("optimized")
end
end
Template Optimization
Compile-Time Template Compilation
# Pre-compiled templates
class OptimizedTemplate
include Templates::Renderable
# Templates compiled at build time
COMPILED_TEMPLATES = {
"user_list" => compile_template("user_list.html"),
"user_detail" => compile_template("user_detail.html")
}
def render(template_name, data)
COMPILED_TEMPLATES[template_name].render(data)
end
end
Template Caching
# Template caching strategy
class TemplateCache
@cache = {} of String => CompiledTemplate
def get_or_compile(template_path)
@cache[template_path] ||= compile_template(template_path)
end
private def compile_template(path)
# Compile template to optimized bytecode
TemplateCompiler.compile(File.read(path))
end
end
Network Optimization
HTTP/2 Optimization
# HTTP/2 server push
struct Http2OptimizedEndpoint
include Endpoint(Http2Request, Http2Response)
def call : Http2Response
response = Http2Response.new(@request.data)
# Server push for critical resources
response.push("/css/critical.css")
response.push("/js/app.js")
response
end
end
Compression
# Response compression
struct CompressedEndpoint
include Endpoint(CompressRequest, CompressResponse)
def call : CompressResponse
response = CompressResponse.new(@request.data)
# Enable compression for large responses
if response.size > 1024
response.compress = true
end
response
end
end
Monitoring and Profiling
Performance Monitoring
# Performance monitoring middleware
struct PerformanceMonitor
include Handler
def call(request, response)
start_time = Time.monotonic
start_memory = GC.stats.total_allocated
result = @next.call(request, response)
duration = Time.monotonic - start_time
memory_used = GC.stats.total_allocated - start_memory
record_metrics(request.path, duration, memory_used)
result
end
end
Profiling Tools
# Crystal profiling integration
require "profile"
# Profile specific endpoints
Profile.start
# ... run your endpoint
Profile.stop
# Analyze results
Profile.print
Configuration Optimization
Production Configuration
# Optimized production configuration
CONFIG.production = {
# Disable development features
debug: false,
hot_reload: false,
# Optimize for performance
workers: System.cpu_count,
backlog: 1024,
# Memory optimization
gc_interval: 100,
gc_threshold: 1000
}
Environment-Specific Tuning
# Environment-specific optimizations
case CONFIG.environment
when "development"
CONFIG.optimization = {
debug_symbols: true,
inline_threshold: 0
}
when "production"
CONFIG.optimization = {
debug_symbols: false,
inline_threshold: 100,
dead_code_elimination: true
}
end
Best Practices
1. Measure First
# Always measure before optimizing
struct MeasuredEndpoint
include Endpoint(MeasureRequest, MeasureResponse)
def call : MeasureResponse
# Measure current performance
baseline = measure_performance do
original_implementation
end
# Apply optimization
optimized = measure_performance do
optimized_implementation
end
# Compare results
improvement = (baseline - optimized) / baseline * 100
log_optimization(improvement)
MeasureResponse.new(optimized_result)
end
end
2. Profile Regularly
# Regular profiling schedule
SCHEDULER.every(1.hour) do
Profile.start
# Run representative workload
Profile.stop
# Save profile data
save_profile_data
end
3. Monitor Production
# Production monitoring
struct ProductionMonitor
include Handler
def call(request, response)
# Track key metrics
track_request_count(request.path)
track_response_time(request.path, measure_time)
track_error_rate(request.path, response.status)
@next.call(request, response)
end
end
Next Steps
Benchmarks - Compare performance metrics
Scaling Patterns - Scale your optimized applications
Performance Tuning - Advanced optimization techniques
Remember: Premature optimization is the root of all evil. Always measure first, then optimize based on real bottlenecks.
Last updated
Was this helpful?