🦀 🦀 TuskLang Rust Performance Optimization
🦀 TuskLang Rust Performance Optimization
"We don't bow to any king" - Rust Edition
Master performance optimization for TuskLang Rust applications. From zero-copy parsing to intelligent caching, from parallel processing to memory optimization - learn how to build applications that scale with maximum efficiency and minimal resource usage.
⚡ Zero-Copy Parsing Optimization
Basic Zero-Copy Implementation
use tusklang_rust::{parse, Parser};
use std::borrow::Cow;#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let mut parser = Parser::new();
// Enable zero-copy mode
parser.set_zero_copy(true);
let tsk_content = r#"
[application]
name: "HighPerformanceApp"
version: "1.0.0"
debug: false
[database]
host: "localhost"
port: 5432
name: "perfdb"
user: "postgres"
password: "secret"
[server]
host: "0.0.0.0"
port: 8080
workers: 8
threads_per_worker: 4
"#;
// Zero-copy parsing - no memory allocation for strings
let data = parser.parse(tsk_content)?;
// Access data without cloning
let app_name = data["application"]["name"].as_str().unwrap();
let db_host = data["database"]["host"].as_str().unwrap();
let server_port = data["server"]["port"].as_u64().unwrap();
println!("App: {} on {}:{}", app_name, db_host, server_port);
Ok(())
}
Advanced Zero-Copy with String Interning
use tusklang_rust::{parse, Parser};
use std::collections::HashMap;
use std::sync::Arc;
use parking_lot::RwLock;struct StringInterner {
strings: Arc<RwLock<HashMap<String, u32>>>,
reverse: Arc<RwLock<HashMap<u32, String>>>,
next_id: Arc<RwLock<u32>>,
}
impl StringInterner {
fn new() -> Self {
Self {
strings: Arc::new(RwLock::new(HashMap::new())),
reverse: Arc::new(RwLock::new(HashMap::new())),
next_id: Arc::new(RwLock::new(0)),
}
}
fn intern(&self, s: &str) -> u32 {
let mut strings = self.strings.write();
if let Some(&id) = strings.get(s) {
return id;
}
let id = {
let mut next_id = self.next_id.write();
let id = *next_id;
*next_id += 1;
id
};
strings.insert(s.to_string(), id);
self.reverse.write().insert(id, s.to_string());
id
}
fn get(&self, id: u32) -> Option<String> {
self.reverse.read().get(&id).cloned()
}
}
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let mut parser = Parser::new();
let interner = StringInterner::new();
// Enable string interning
parser.set_string_interner(Arc::new(interner));
let tsk_content = r#"
[performance]
cache_enabled: true
cache_size: 10000
cache_ttl: "5m"
parallel_processing: true
worker_threads: 8
memory_limit: "2GB"
"#;
let data = parser.parse(tsk_content)?;
// Strings are now interned - minimal memory usage
let cache_enabled = data["performance"]["cache_enabled"].as_bool().unwrap();
let cache_size = data["performance"]["cache_size"].as_u64().unwrap();
println!("Cache enabled: {}, Size: {}", cache_enabled, cache_size);
Ok(())
}
🧠 Memory Optimization
Memory Pool Implementation
use tusklang_rust::{parse, Parser};
use std::alloc::{alloc, dealloc, Layout};
use std::sync::Arc;
use parking_lot::Mutex;struct MemoryPool {
pools: Arc<Mutex<HashMap<usize, Vec<*mut u8>>>>,
}
impl MemoryPool {
fn new() -> Self {
Self {
pools: Arc::new(Mutex::new(HashMap::new())),
}
}
fn allocate(&self, size: usize) -> *mut u8 {
let mut pools = self.pools.lock();
// Try to reuse existing allocation
if let Some(pool) = pools.get_mut(&size) {
if let Some(ptr) = pool.pop() {
return ptr;
}
}
// Allocate new memory
let layout = Layout::from_size_align(size, 8).unwrap();
unsafe { alloc(layout) }
}
fn deallocate(&self, ptr: *mut u8, size: usize) {
let mut pools = self.pools.lock();
// Return to pool instead of deallocating
pools.entry(size).or_insert_with(Vec::new).push(ptr);
}
}
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let mut parser = Parser::new();
let memory_pool = MemoryPool::new();
// Enable memory pooling
parser.set_memory_pool(Arc::new(memory_pool));
let tsk_content = r#"
[memory_optimization]
use_pool: true
pool_size: 1000
max_allocation_size: 1048576
garbage_collection_interval: "30s"
"#;
let data = parser.parse(tsk_content)?;
let use_pool = data["memory_optimization"]["use_pool"].as_bool().unwrap();
let pool_size = data["memory_optimization"]["pool_size"].as_u64().unwrap();
println!("Memory pool enabled: {}, Size: {}", use_pool, pool_size);
Ok(())
}
Garbage Collection Optimization
use tusklang_rust::{parse, Parser};
use std::sync::Arc;
use std::time::{Duration, Instant};
use parking_lot::RwLock;struct GarbageCollector {
last_gc: Arc<RwLock<Instant>>,
gc_interval: Duration,
allocations: Arc<RwLock<Vec<*mut u8>>>,
}
impl GarbageCollector {
fn new(gc_interval: Duration) -> Self {
Self {
last_gc: Arc::new(RwLock::new(Instant::now())),
gc_interval,
allocations: Arc::new(RwLock::new(Vec::new())),
}
}
fn should_gc(&self) -> bool {
let last_gc = *self.last_gc.read();
Instant::now().duration_since(last_gc) >= self.gc_interval
}
fn collect(&self) {
let mut last_gc = self.last_gc.write();
*last_gc = Instant::now();
let mut allocations = self.allocations.write();
allocations.clear();
}
fn track_allocation(&self, ptr: *mut u8) {
self.allocations.write().push(ptr);
}
}
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let mut parser = Parser::new();
let gc = GarbageCollector::new(Duration::from_secs(30));
// Enable garbage collection
parser.set_garbage_collector(Arc::new(gc));
let tsk_content = r#"
[gc_settings]
enabled: true
interval: "30s"
max_memory_usage: "1GB"
aggressive_mode: false
"#;
let data = parser.parse(tsk_content)?;
let gc_enabled = data["gc_settings"]["enabled"].as_bool().unwrap();
let gc_interval = data["gc_settings"]["interval"].as_str().unwrap();
println!("GC enabled: {}, Interval: {}", gc_enabled, gc_interval);
Ok(())
}
🔄 Parallel Processing
Parallel Parser Implementation
use tusklang_rust::{parse, Parser};
use rayon::prelude::*;
use std::sync::Arc;#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let mut parser = Parser::new();
// Enable parallel processing
parser.set_parallel_processing(true);
parser.set_worker_threads(8);
let configs = vec![
r#"
[app1]
name: "App1"
port: 8081
"#,
r#"
[app2]
name: "App2"
port: 8082
"#,
r#"
[app3]
name: "App3"
port: 8083
"#,
];
// Parse multiple configs in parallel
let results: Vec<_> = configs.par_iter()
.map(|config| parser.parse(config))
.collect();
for (i, result) in results.into_iter().enumerate() {
let data = result?;
println!("App{}: {} on port {}",
i + 1,
data[&format!("app{}", i + 1)]["name"],
data[&format!("app{}", i + 1)]["port"]
);
}
Ok(())
}
Parallel FUJSEN Execution
use tusklang_rust::{parse, Parser};
use rayon::prelude::*;
use std::sync::Arc;#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let mut parser = Parser::new();
let tsk_content = r#"
[parallel_math]
add_fujsen = '''
fn add(a: i32, b: i32) -> i32 {
a + b
}
'''
[parallel_processing]
process_data_fujsen = '''
fn process_data(data: Vec<i32>) -> i32 {
data.iter().sum()
}
'''
"#;
parser.parse(tsk_content)?;
// Prepare data for parallel processing
let data_sets: Vec<Vec<i32>> = (0..1000)
.map(|i| (i..i+100).collect())
.collect();
// Execute FUJSEN functions in parallel
let results: Vec<_> = data_sets.par_iter()
.map(|data| {
tokio::runtime::Handle::current().block_on(async {
parser.execute_fujsen("parallel_processing", "process_data", &[data]).await
})
})
.collect();
let total_sum: i32 = results.into_iter()
.filter_map(|r| r.ok())
.sum();
println!("Total sum from parallel processing: {}", total_sum);
Ok(())
}
💾 Intelligent Caching
Multi-Level Cache Implementation
use tusklang_rust::{parse, Parser, cache::{MemoryCache, RedisCache, TieredCache}};
use std::time::Duration;#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let mut parser = Parser::new();
// Setup multi-level caching
let memory_cache = MemoryCache::new();
let redis_cache = RedisCache::new(RedisConfig {
host: "localhost".to_string(),
port: 6379,
db: 0,
}).await?;
let tiered_cache = TieredCache::new()
.add_tier(memory_cache, "5s") // L1: Memory cache, 5 seconds
.add_tier(redis_cache, "1h"); // L2: Redis cache, 1 hour
parser.set_cache(tiered_cache);
let tsk_content = r#"
[cached_operations]
user_profile: @cache("1h", "user_profile", @request.user_id)
expensive_calculation: @cache("5m", "expensive_operation")
api_response: @cache("30s", "api_call", @request.endpoint)
database_query: @cache("10m", "db_query", @request.query_hash)
"#;
let data = parser.parse(tsk_content).await?;
println!("Cached operations configured:");
println!(" User profile: {}", data["cached_operations"]["user_profile"]);
println!(" Expensive calculation: {}", data["cached_operations"]["expensive_calculation"]);
println!(" API response: {}", data["cached_operations"]["api_response"]);
println!(" Database query: {}", data["cached_operations"]["database_query"]);
Ok(())
}
Adaptive Cache Implementation
use tusklang_rust::{parse, Parser, cache::{AdaptiveCache, CacheStrategy}};
use std::collections::HashMap;#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let mut parser = Parser::new();
let adaptive_cache = AdaptiveCache::new()
.with_strategy(CacheStrategy::LRU)
.with_max_size(1000)
.with_ttl_adaptation(true)
.with_access_tracking(true);
parser.set_cache(adaptive_cache);
let tsk_content = r#"
[adaptive_caching]
frequently_accessed: @cache.adaptive("frequent_data", @request.key)
rarely_accessed: @cache.adaptive("rare_data", @request.key)
hot_data: @cache.adaptive("hot_data", @request.key, "1h")
cold_data: @cache.adaptive("cold_data", @request.key, "24h")
"#;
let data = parser.parse(tsk_content).await?;
println!("Adaptive cache configured:");
println!(" Frequently accessed: {}", data["adaptive_caching"]["frequently_accessed"]);
println!(" Rarely accessed: {}", data["adaptive_caching"]["rarely_accessed"]);
println!(" Hot data: {}", data["adaptive_caching"]["hot_data"]);
println!(" Cold data: {}", data["adaptive_caching"]["cold_data"]);
Ok(())
}
🔧 Database Optimization
Connection Pooling
use tusklang_rust::{parse, Parser, adapters::postgresql::{PostgreSQLAdapter, PostgreSQLConfig, PoolConfig}};
use std::time::Duration;#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let mut parser = Parser::new();
// Setup optimized database connection pool
let postgres = PostgreSQLAdapter::with_pool(PostgreSQLConfig {
host: "localhost".to_string(),
port: 5432,
database: "perfdb".to_string(),
user: "postgres".to_string(),
password: "secret".to_string(),
ssl_mode: "disable".to_string(),
}, PoolConfig {
max_open_conns: 50,
max_idle_conns: 20,
conn_max_lifetime: Duration::from_secs(300),
conn_max_idle_time: Duration::from_secs(60),
}).await?;
parser.set_database_adapter(postgres);
let tsk_content = r#"
[database_optimization]
connection_pool_size: 50
max_idle_connections: 20
connection_lifetime: "5m"
idle_timeout: "1m"
query_timeout: "30s"
statement_cache_size: 1000
"#;
let data = parser.parse(tsk_content).await?;
println!("Database optimization configured:");
println!(" Pool size: {}", data["database_optimization"]["connection_pool_size"]);
println!(" Max idle: {}", data["database_optimization"]["max_idle_connections"]);
println!(" Lifetime: {}", data["database_optimization"]["connection_lifetime"]);
println!(" Idle timeout: {}", data["database_optimization"]["idle_timeout"]);
Ok(())
}
Query Optimization
use tusklang_rust::{parse, Parser, adapters::sqlite::SQLiteAdapter};#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let mut parser = Parser::new();
let db = SQLiteAdapter::new(":memory:").await?;
// Setup optimized database
db.execute(r#"
PRAGMA journal_mode=WAL;
PRAGMA synchronous=NORMAL;
PRAGMA cache_size=10000;
PRAGMA temp_store=MEMORY;
PRAGMA mmap_size=268435456;
CREATE TABLE users (
id INTEGER PRIMARY KEY,
name TEXT NOT NULL,
email TEXT UNIQUE,
active BOOLEAN DEFAULT 1,
created_at DATETIME DEFAULT CURRENT_TIMESTAMP
);
CREATE INDEX idx_users_active ON users(active);
CREATE INDEX idx_users_email ON users(email);
CREATE INDEX idx_users_created_at ON users(created_at);
INSERT INTO users (name, email) VALUES
('Alice', 'alice@example.com'),
('Bob', 'bob@example.com'),
('Charlie', 'charlie@example.com');
"#).await?;
parser.set_database_adapter(db);
let tsk_content = r#"
[query_optimization]
optimized_queries: true
use_indexes: true
prepared_statements: true
query_cache_size: 1000
slow_query_threshold: "100ms"
"#;
let data = parser.parse(tsk_content).await?;
// Test optimized queries
let users = parser.query("SELECT * FROM users WHERE active = 1").await?;
println!("Active users: {}", users.len());
let user_by_email = parser.query("SELECT * FROM users WHERE email = ?", &["alice@example.com"]).await?;
println!("User by email: {:?}", user_by_email);
Ok(())
}
📊 Performance Monitoring
Real-Time Metrics
use tusklang_rust::{parse, Parser, metrics::{MetricsCollector, PrometheusExporter}};
use std::sync::Arc;
use std::time::Instant;#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let mut parser = Parser::new();
let metrics_collector = MetricsCollector::new();
let prometheus_exporter = PrometheusExporter::new("0.0.0.0:9090").await?;
parser.set_metrics_collector(metrics_collector);
parser.set_metrics_exporter(prometheus_exporter);
let tsk_content = r#"
[performance_monitoring]
enabled: true
metrics_port: 9090
collection_interval: "5s"
memory_tracking: true
cpu_tracking: true
query_tracking: true
cache_tracking: true
"#;
let data = parser.parse(tsk_content).await?;
// Start performance monitoring
let start = Instant::now();
// Simulate some operations
for i in 0..1000 {
let _result = parser.parse(&format!(r#"
[test]
value: {}
string: "test_string_{}"
"#, i, i)).await?;
}
let duration = start.elapsed();
println!("Processed 1000 configs in {:?}", duration);
println!("Average time per config: {:?}", duration / 1000);
Ok(())
}
Performance Benchmarking
use tusklang_rust::{parse, Parser};
use std::time::{Duration, Instant};
use std::collections::HashMap;struct PerformanceBenchmark {
results: HashMap<String, Vec<Duration>>,
}
impl PerformanceBenchmark {
fn new() -> Self {
Self {
results: HashMap::new(),
}
}
fn benchmark<F>(&mut self, name: &str, iterations: usize, mut test_fn: F)
where
F: FnMut() -> Result<(), Box<dyn std::error::Error>>,
{
let mut times = Vec::new();
for _ in 0..iterations {
let start = Instant::now();
test_fn().expect("Benchmark test failed");
times.push(start.elapsed());
}
self.results.insert(name.to_string(), times);
}
fn print_results(&self) {
println!("Performance Benchmark Results:");
println!("{:<20} {:<10} {:<15} {:<15} {:<15} {:<15}",
"Test", "Iterations", "Total Time", "Avg Time", "Min Time", "Max Time");
println!("{}", "-".repeat(90));
for (name, times) in &self.results {
let total_time: Duration = times.iter().sum();
let avg_time = total_time / times.len() as u32;
let min_time = times.iter().min().unwrap().clone();
let max_time = times.iter().max().unwrap().clone();
println!("{:<20} {:<10} {:<15?} {:<15?} {:<15?} {:<15?}",
name,
times.len(),
total_time,
avg_time,
min_time,
max_time
);
}
}
}
#[tokio::main]
async fn test_performance_benchmarks() -> Result<(), Box<dyn std::error::Error>> {
let mut benchmark = PerformanceBenchmark::new();
let mut parser = Parser::new();
let tsk_content = r#"
[test]
value: 42
string: "hello"
boolean: true
array: [1, 2, 3, 4, 5]
object: {
key: "value"
nested: {
deep: "data"
}
}
"#;
// Benchmark parsing
benchmark.benchmark("TSK Parsing", 1000, || {
parser.parse(tsk_content)?;
Ok(())
});
// Benchmark FUJSEN execution
let fujsen_tsk = r#"
[math]
add_fujsen = '''
fn add(a: i32, b: i32) -> i32 {
a + b
}
'''
"#;
parser.parse(fujsen_tsk).expect("Failed to parse FUJSEN");
benchmark.benchmark("FUJSEN Execution", 1000, || {
parser.execute_fujsen("math", "add", &[&5, &3]).await?;
Ok(())
});
// Benchmark database queries
let db = SQLiteAdapter::new(":memory:").await?;
db.execute("CREATE TABLE test (id INTEGER, value TEXT)").await?;
parser.set_database_adapter(db);
benchmark.benchmark("Database Query", 100, || {
parser.query("SELECT * FROM test").await?;
Ok(())
});
// Print results
benchmark.print_results();
Ok(())
}
🚀 Optimization Strategies
Compile-Time Optimization
use tusklang_rust::{parse, Parser};// Compile-time configuration for maximum performance
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let mut parser = Parser::new();
// Enable all optimizations
parser.set_optimizations(ParserOptimizations {
zero_copy: true,
string_interning: true,
memory_pooling: true,
parallel_processing: true,
aggressive_caching: true,
compile_time_validation: true,
});
let tsk_content = r#"
[optimization_settings]
zero_copy_enabled: true
string_interning_enabled: true
memory_pooling_enabled: true
parallel_processing_enabled: true
aggressive_caching_enabled: true
compile_time_validation_enabled: true
"#;
let data = parser.parse(tsk_content).await?;
println!("Optimization settings:");
println!(" Zero-copy: {}", data["optimization_settings"]["zero_copy_enabled"]);
println!(" String interning: {}", data["optimization_settings"]["string_interning_enabled"]);
println!(" Memory pooling: {}", data["optimization_settings"]["memory_pooling_enabled"]);
println!(" Parallel processing: {}", data["optimization_settings"]["parallel_processing_enabled"]);
println!(" Aggressive caching: {}", data["optimization_settings"]["aggressive_caching_enabled"]);
println!(" Compile-time validation: {}", data["optimization_settings"]["compile_time_validation_enabled"]);
Ok(())
}
Runtime Optimization
use tusklang_rust::{parse, Parser};
use std::sync::Arc;
use tokio::sync::Semaphore;#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let mut parser = Parser::new();
// Runtime optimizations
let semaphore = Arc::new(Semaphore::new(100)); // Limit concurrent operations
parser.set_concurrency_limit(100);
parser.set_memory_limit(1024 1024 1024); // 1GB memory limit
let tsk_content = r#"
[runtime_optimization]
concurrency_limit: 100
memory_limit: "1GB"
cpu_limit: "4"
io_timeout: "30s"
max_retries: 3
backoff_strategy: "exponential"
"#;
let data = parser.parse(tsk_content).await?;
println!("Runtime optimization configured:");
println!(" Concurrency limit: {}", data["runtime_optimization"]["concurrency_limit"]);
println!(" Memory limit: {}", data["runtime_optimization"]["memory_limit"]);
println!(" CPU limit: {}", data["runtime_optimization"]["cpu_limit"]);
println!(" IO timeout: {}", data["runtime_optimization"]["io_timeout"]);
Ok(())
}
🎯 What You've Learned
1. Zero-copy parsing - Minimize memory allocations and copying 2. Memory optimization - Memory pools, garbage collection, and efficient data structures 3. Parallel processing - Multi-threaded parsing and FUJSEN execution 4. Intelligent caching - Multi-level and adaptive caching strategies 5. Database optimization - Connection pooling and query optimization 6. Performance monitoring - Real-time metrics and benchmarking 7. Optimization strategies - Compile-time and runtime optimizations
🚀 Next Steps
1. Profile your application - Use the benchmarking tools to identify bottlenecks 2. Implement optimizations - Apply the optimization techniques to your specific use case 3. Monitor performance - Set up continuous performance monitoring 4. Scale your application - Use the optimization strategies to handle increased load 5. Optimize for your environment - Tailor optimizations to your deployment environment
---
You now have complete performance optimization mastery with TuskLang Rust! From zero-copy parsing to intelligent caching, from parallel processing to memory optimization - TuskLang gives you the tools to build applications that scale with maximum efficiency and minimal resource usage.