🦀 Database Optimization in TuskLang with Rust
Database Optimization in TuskLang with Rust
🗄️ Database Optimization Foundation
Database optimization with TuskLang and Rust provides powerful tools for building high-performance database applications. This guide covers connection pooling, query optimization, caching strategies, and advanced database techniques.
🏗️ Database Architecture
Database Stack
[database_architecture]
connection_pooling: true
query_optimization: true
caching_strategies: true
transaction_management: true[database_components]
sqlx: "async_sql"
diesel: "orm_framework"
redis: "caching_layer"
postgres: "primary_database"
Database Configuration
[database_configuration]
connection_pool_size: 20
max_connections: 100
query_timeout: 30
enable_caching: true[database_implementation]
use sqlx::{Pool, Postgres, postgres::PgPoolOptions};
use std::collections::HashMap;
use tokio::sync::RwLock;
use std::time::{Duration, Instant};
// Database manager
pub struct DatabaseManager {
pool: Pool<Postgres>,
config: DatabaseConfig,
metrics: Arc<RwLock<DatabaseMetrics>>,
query_cache: Arc<RwLock<HashMap<String, CachedQuery>>>,
}
#[derive(Debug, Clone)]
pub struct DatabaseConfig {
pub connection_string: String,
pub max_connections: u32,
pub min_connections: u32,
pub connection_timeout: Duration,
pub query_timeout: Duration,
pub enable_query_cache: bool,
pub cache_ttl: Duration,
pub enable_metrics: bool,
}
#[derive(Debug, Clone)]
pub struct DatabaseMetrics {
pub total_queries: u64,
pub successful_queries: u64,
pub failed_queries: u64,
pub avg_query_time: Duration,
pub cache_hits: u64,
pub cache_misses: u64,
pub active_connections: u32,
pub idle_connections: u32,
}
#[derive(Debug, Clone)]
pub struct CachedQuery {
pub result: String,
pub created_at: Instant,
pub ttl: Duration,
}
impl DatabaseManager {
pub async fn new(config: DatabaseConfig) -> Result<Self, sqlx::Error> {
let pool = PgPoolOptions::new()
.max_connections(config.max_connections)
.min_connections(config.min_connections)
.connect_timeout(config.connection_timeout)
.acquire_timeout(config.connection_timeout)
.idle_timeout(config.connection_timeout)
.max_lifetime(config.connection_timeout * 2)
.connect(&config.connection_string)
.await?;
Ok(Self {
pool,
config,
metrics: Arc::new(RwLock::new(DatabaseMetrics {
total_queries: 0,
successful_queries: 0,
failed_queries: 0,
avg_query_time: Duration::ZERO,
cache_hits: 0,
cache_misses: 0,
active_connections: 0,
idle_connections: 0,
})),
query_cache: Arc::new(RwLock::new(HashMap::new())),
})
}
pub async fn execute_query(&self, query: &str, params: &[&(dyn sqlx::Encode<'_, Postgres> + Send + Sync)]) -> Result<sqlx::postgres::PgRow, DatabaseError> {
let start_time = Instant::now();
// Check cache first
if self.config.enable_query_cache {
let cache_key = self.generate_cache_key(query, params);
if let Some(cached_result) = self.get_cached_query(&cache_key).await {
self.record_cache_hit().await;
return Ok(cached_result);
}
}
// Execute query
let result = sqlx::query(query)
.bind_all(params)
.fetch_one(&self.pool)
.await;
let query_time = start_time.elapsed();
self.record_query_metrics(result.is_ok(), query_time).await;
match result {
Ok(row) => {
// Cache result if enabled
if self.config.enable_query_cache {
let cache_key = self.generate_cache_key(query, params);
self.cache_query(&cache_key, &row).await;
}
Ok(row)
}
Err(e) => Err(DatabaseError::QueryFailed { message: e.to_string() }),
}
}
pub async fn execute_transaction<F, T>(&self, f: F) -> Result<T, DatabaseError>
where
F: for<'a> FnOnce(&'a mut sqlx::Transaction<'a, Postgres>) -> std::pin::Pin<Box<dyn std::future::Future<Output = Result<T, DatabaseError>> + Send + 'a>>,
{
let mut transaction = self.pool.begin().await
.map_err(|e| DatabaseError::TransactionFailed { message: e.to_string() })?;
let result = f(&mut transaction).await;
match result {
Ok(value) => {
transaction.commit().await
.map_err(|e| DatabaseError::TransactionFailed { message: e.to_string() })?;
Ok(value)
}
Err(e) => {
transaction.rollback().await
.map_err(|_| DatabaseError::TransactionFailed { message: "Rollback failed".to_string() })?;
Err(e)
}
}
}
async fn generate_cache_key(&self, query: &str, params: &[&(dyn sqlx::Encode<'_, Postgres> + Send + Sync)]) -> String {
// Simple cache key generation - in production, use a proper hash
format!("{}:{}", query, params.len())
}
async fn get_cached_query(&self, cache_key: &str) -> Option<sqlx::postgres::PgRow> {
let cache = self.query_cache.read().await;
if let Some(cached) = cache.get(cache_key) {
if cached.created_at.elapsed() < cached.ttl {
return Some(cached.result.clone());
}
}
None
}
async fn cache_query(&self, cache_key: &str, result: &sqlx::postgres::PgRow) {
let mut cache = self.query_cache.write().await;
cache.insert(cache_key.to_string(), CachedQuery {
result: result.to_string(),
created_at: Instant::now(),
ttl: self.config.cache_ttl,
});
}
async fn record_cache_hit(&self) {
let mut metrics = self.metrics.write().await;
metrics.cache_hits += 1;
}
async fn record_query_metrics(&self, success: bool, query_time: Duration) {
let mut metrics = self.metrics.write().await;
metrics.total_queries += 1;
if success {
metrics.successful_queries += 1;
} else {
metrics.failed_queries += 1;
}
// Update average query time
let total_time = metrics.avg_query_time * (metrics.total_queries - 1) + query_time;
metrics.avg_query_time = total_time / metrics.total_queries;
}
pub async fn get_metrics(&self) -> DatabaseMetrics {
self.metrics.read().await.clone()
}
pub async fn clear_cache(&self) {
self.query_cache.write().await.clear();
}
}
#[derive(Debug, thiserror::Error)]
pub enum DatabaseError {
#[error("Query failed: {message}")]
QueryFailed { message: String },
#[error("Transaction failed: {message}")]
TransactionFailed { message: String },
#[error("Connection failed: {message}")]
ConnectionFailed { message: String },
#[error("Cache error: {message}")]
CacheError { message: String },
}
🔗 Connection Pooling
Advanced Connection Pool
[connection_pooling]
pool_management: true
connection_monitoring: true
load_balancing: true[pool_implementation]
use std::sync::atomic::{AtomicU32, Ordering};
use tokio::time::interval;
// Advanced connection pool
pub struct AdvancedConnectionPool {
pools: Vec<Pool<Postgres>>,
current_pool: AtomicU32,
config: PoolConfig,
health_checker: Arc<RwLock<HealthChecker>>,
}
#[derive(Debug, Clone)]
pub struct PoolConfig {
pub primary_connection_string: String,
pub replica_connection_strings: Vec<String>,
pub max_connections_per_pool: u32,
pub min_connections_per_pool: u32,
pub connection_timeout: Duration,
pub health_check_interval: Duration,
pub load_balancing_strategy: LoadBalancingStrategy,
}
#[derive(Debug, Clone)]
pub enum LoadBalancingStrategy {
RoundRobin,
LeastConnections,
Weighted,
}
pub struct HealthChecker {
pool_health: HashMap<usize, bool>,
last_check: HashMap<usize, Instant>,
}
impl AdvancedConnectionPool {
pub async fn new(config: PoolConfig) -> Result<Self, sqlx::Error> {
let mut pools = Vec::new();
// Create primary pool
let primary_pool = PgPoolOptions::new()
.max_connections(config.max_connections_per_pool)
.min_connections(config.min_connections_per_pool)
.connect_timeout(config.connection_timeout)
.connect(&config.primary_connection_string)
.await?;
pools.push(primary_pool);
// Create replica pools
for connection_string in &config.replica_connection_strings {
let pool = PgPoolOptions::new()
.max_connections(config.max_connections_per_pool)
.min_connections(config.min_connections_per_pool)
.connect_timeout(config.connection_timeout)
.connect(connection_string)
.await?;
pools.push(pool);
}
let health_checker = Arc::new(RwLock::new(HealthChecker {
pool_health: HashMap::new(),
last_check: HashMap::new(),
}));
let pool = Self {
pools,
current_pool: AtomicU32::new(0),
config,
health_checker: Arc::clone(&health_checker),
};
// Start health checking
pool.start_health_checker().await;
Ok(pool)
}
pub async fn get_connection(&self) -> Result<Pool<Postgres>, DatabaseError> {
match self.config.load_balancing_strategy {
LoadBalancingStrategy::RoundRobin => self.get_round_robin_connection().await,
LoadBalancingStrategy::LeastConnections => self.get_least_connections_pool().await,
LoadBalancingStrategy::Weighted => self.get_weighted_connection().await,
}
}
async fn get_round_robin_connection(&self) -> Result<Pool<Postgres>, DatabaseError> {
let mut attempts = 0;
let max_attempts = self.pools.len();
while attempts < max_attempts {
let pool_index = self.current_pool.fetch_add(1, Ordering::Relaxed) as usize % self.pools.len();
if self.is_pool_healthy(pool_index).await {
return Ok(self.pools[pool_index].clone());
}
attempts += 1;
}
Err(DatabaseError::ConnectionFailed { message: "No healthy pools available".to_string() })
}
async fn get_least_connections_pool(&self) -> Result<Pool<Postgres>, DatabaseError> {
let mut best_pool = None;
let mut min_connections = u32::MAX;
for (index, pool) in self.pools.iter().enumerate() {
if self.is_pool_healthy(index).await {
let size = pool.size();
if size < min_connections {
min_connections = size;
best_pool = Some(pool.clone());
}
}
}
best_pool.ok_or_else(|| DatabaseError::ConnectionFailed { message: "No healthy pools available".to_string() })
}
async fn get_weighted_connection(&self) -> Result<Pool<Postgres>, DatabaseError> {
// Simple weighted strategy - primary gets higher weight
if self.is_pool_healthy(0).await {
return Ok(self.pools[0].clone());
}
// Fallback to round-robin for replicas
for index in 1..self.pools.len() {
if self.is_pool_healthy(index).await {
return Ok(self.pools[index].clone());
}
}
Err(DatabaseError::ConnectionFailed { message: "No healthy pools available".to_string() })
}
async fn is_pool_healthy(&self, pool_index: usize) -> bool {
let health_checker = self.health_checker.read().await;
health_checker.pool_health.get(&pool_index).copied().unwrap_or(false)
}
async fn start_health_checker(&self) {
let health_checker = Arc::clone(&self.health_checker);
let pools = self.pools.clone();
let interval_duration = self.config.health_check_interval;
tokio::spawn(async move {
let mut interval = interval(interval_duration);
loop {
interval.tick().await;
let mut checker = health_checker.write().await;
let now = Instant::now();
for (index, pool) in pools.iter().enumerate() {
// Check if we need to perform health check
let last_check = checker.last_check.get(&index).copied().unwrap_or(Instant::now() - interval_duration);
if now.duration_since(last_check) >= interval_duration {
let is_healthy = Self::check_pool_health(pool).await;
checker.pool_health.insert(index, is_healthy);
checker.last_check.insert(index, now);
}
}
}
});
}
async fn check_pool_health(pool: &Pool<Postgres>) -> bool {
match sqlx::query("SELECT 1").fetch_one(pool).await {
Ok(_) => true,
Err(_) => false,
}
}
}
🔍 Query Optimization
Query Optimizer
[query_optimization]
query_analysis: true
index_optimization: true
query_rewriting: true[optimization_implementation]
use std::collections::HashMap;
// Query optimizer
pub struct QueryOptimizer {
query_stats: Arc<RwLock<HashMap<String, QueryStats>>>,
index_recommendations: Arc<RwLock<Vec<IndexRecommendation>>>,
slow_query_threshold: Duration,
}
#[derive(Debug, Clone)]
pub struct QueryStats {
pub query_hash: String,
pub execution_count: u64,
pub total_execution_time: Duration,
pub avg_execution_time: Duration,
pub min_execution_time: Duration,
pub max_execution_time: Duration,
pub last_executed: Instant,
}
#[derive(Debug, Clone)]
pub struct IndexRecommendation {
pub table_name: String,
pub column_name: String,
pub index_type: IndexType,
pub estimated_improvement: f64,
pub reason: String,
}
#[derive(Debug, Clone)]
pub enum IndexType {
BTree,
Hash,
GIN,
GIST,
}
impl QueryOptimizer {
pub fn new(slow_query_threshold: Duration) -> Self {
Self {
query_stats: Arc::new(RwLock::new(HashMap::new())),
index_recommendations: Arc::new(RwLock::new(Vec::new())),
slow_query_threshold,
}
}
pub async fn analyze_query(&self, query: &str, execution_time: Duration) -> QueryAnalysis {
let query_hash = self.hash_query(query);
// Update query statistics
{
let mut stats = self.query_stats.write().await;
let stat = stats.entry(query_hash.clone()).or_insert_with(|| QueryStats {
query_hash: query_hash.clone(),
execution_count: 0,
total_execution_time: Duration::ZERO,
avg_execution_time: Duration::ZERO,
min_execution_time: Duration::MAX,
max_execution_time: Duration::ZERO,
last_executed: Instant::now(),
});
stat.execution_count += 1;
stat.total_execution_time += execution_time;
stat.avg_execution_time = stat.total_execution_time / stat.execution_count;
stat.min_execution_time = stat.min_execution_time.min(execution_time);
stat.max_execution_time = stat.max_execution_time.max(execution_time);
stat.last_executed = Instant::now();
}
// Analyze query performance
let is_slow = execution_time > self.slow_query_threshold;
let recommendations = if is_slow {
self.generate_recommendations(query).await
} else {
Vec::new()
};
QueryAnalysis {
query_hash,
execution_time,
is_slow,
recommendations,
}
}
pub async fn optimize_query(&self, query: &str) -> String {
let mut optimized_query = query.to_string();
// Apply query optimizations
optimized_query = self.rewrite_select_star(&optimized_query);
optimized_query = self.add_limit_if_missing(&optimized_query);
optimized_query = self.optimize_joins(&optimized_query);
optimized_query = self.optimize_where_clause(&optimized_query);
optimized_query
}
fn hash_query(&self, query: &str) -> String {
// Simple hash - in production, use a proper cryptographic hash
use std::collections::hash_map::DefaultHasher;
use std::hash::{Hash, Hasher};
let mut hasher = DefaultHasher::new();
query.hash(&mut hasher);
format!("{:x}", hasher.finish())
}
async fn generate_recommendations(&self, query: &str) -> Vec<IndexRecommendation> {
let mut recommendations = Vec::new();
// Analyze WHERE clauses for potential indexes
if let Some(where_clause) = self.extract_where_clause(query) {
for column in self.extract_columns_from_where(&where_clause) {
recommendations.push(IndexRecommendation {
table_name: "unknown".to_string(), // Would need to parse table name
column_name: column,
index_type: IndexType::BTree,
estimated_improvement: 0.8,
reason: "Frequent filtering on this column".to_string(),
});
}
}
// Analyze JOIN clauses
if let Some(join_clause) = self.extract_join_clause(query) {
for column in self.extract_columns_from_join(&join_clause) {
recommendations.push(IndexRecommendation {
table_name: "unknown".to_string(),
column_name: column,
index_type: IndexType::BTree,
estimated_improvement: 0.9,
reason: "JOIN condition on this column".to_string(),
});
}
}
recommendations
}
fn rewrite_select_star(&self, query: &str) -> String {
// Replace SELECT * with specific columns when possible
if query.to_lowercase().contains("select *") {
// This would require schema information to be truly effective
query.replace("SELECT *", "SELECT id, name, created_at")
} else {
query.to_string()
}
}
fn add_limit_if_missing(&self, query: &str) -> String {
// Add LIMIT clause to prevent large result sets
if !query.to_lowercase().contains("limit") && query.to_lowercase().contains("select") {
format!("{} LIMIT 1000", query)
} else {
query.to_string()
}
}
fn optimize_joins(&self, query: &str) -> String {
// Optimize JOIN order (simplified)
query.to_string()
}
fn optimize_where_clause(&self, query: &str) -> String {
// Optimize WHERE clause order (simplified)
query.to_string()
}
fn extract_where_clause(&self, query: &str) -> Option<&str> {
let query_lower = query.to_lowercase();
if let Some(where_pos) = query_lower.find("where") {
if let Some(order_pos) = query_lower.find("order by") {
Some(&query[where_pos + 5..order_pos])
} else if let Some(limit_pos) = query_lower.find("limit") {
Some(&query[where_pos + 5..limit_pos])
} else {
Some(&query[where_pos + 5..])
}
} else {
None
}
}
fn extract_join_clause(&self, query: &str) -> Option<&str> {
let query_lower = query.to_lowercase();
if let Some(join_pos) = query_lower.find("join") {
if let Some(where_pos) = query_lower.find("where") {
Some(&query[join_pos..where_pos])
} else {
Some(&query[join_pos..])
}
} else {
None
}
}
fn extract_columns_from_where(&self, where_clause: &str) -> Vec<String> {
// Simple column extraction - would need proper SQL parsing in production
where_clause
.split_whitespace()
.filter(|word| word.contains('.') || word.chars().all(|c| c.is_alphanumeric() || c == '_'))
.map(|s| s.to_string())
.collect()
}
fn extract_columns_from_join(&self, join_clause: &str) -> Vec<String> {
// Simple column extraction for JOIN clauses
join_clause
.split_whitespace()
.filter(|word| word.contains('.') || word.chars().all(|c| c.is_alphanumeric() || c == '_'))
.map(|s| s.to_string())
.collect()
}
pub async fn get_slow_queries(&self) -> Vec<QueryStats> {
let stats = self.query_stats.read().await;
stats.values()
.filter(|stat| stat.avg_execution_time > self.slow_query_threshold)
.cloned()
.collect()
}
pub async fn get_index_recommendations(&self) -> Vec<IndexRecommendation> {
self.index_recommendations.read().await.clone()
}
}
#[derive(Debug)]
pub struct QueryAnalysis {
pub query_hash: String,
pub execution_time: Duration,
pub is_slow: bool,
pub recommendations: Vec<IndexRecommendation>,
}
💾 Caching Strategies
Multi-Level Caching
[caching_strategies]
l1_cache: true
l2_cache: true
distributed_cache: true[caching_implementation]
use redis::{Client, Connection, Commands};
use std::collections::HashMap;
// Multi-level cache
pub struct MultiLevelCache {
l1_cache: Arc<RwLock<HashMap<String, CacheEntry>>>,
l2_cache: Arc<RwLock<RedisCache>>,
config: CacheConfig,
}
#[derive(Debug, Clone)]
pub struct CacheConfig {
pub l1_max_size: usize,
pub l1_ttl: Duration,
pub l2_ttl: Duration,
pub enable_l1: bool,
pub enable_l2: bool,
pub redis_url: String,
}
#[derive(Debug, Clone)]
pub struct CacheEntry {
pub value: String,
pub created_at: Instant,
pub ttl: Duration,
pub access_count: u64,
}
pub struct RedisCache {
client: Client,
}
impl RedisCache {
pub fn new(redis_url: &str) -> Result<Self, redis::RedisError> {
let client = Client::open(redis_url)?;
Ok(Self { client })
}
pub async fn get(&self, key: &str) -> Result<Option<String>, redis::RedisError> {
let mut conn = self.client.get_async_connection().await?;
let result: Option<String> = conn.get(key).await?;
Ok(result)
}
pub async fn set(&self, key: &str, value: &str, ttl: Duration) -> Result<(), redis::RedisError> {
let mut conn = self.client.get_async_connection().await?;
let ttl_secs = ttl.as_secs() as usize;
conn.set_ex(key, value, ttl_secs).await?;
Ok(())
}
pub async fn delete(&self, key: &str) -> Result<(), redis::RedisError> {
let mut conn = self.client.get_async_connection().await?;
conn.del(key).await?;
Ok(())
}
}
impl MultiLevelCache {
pub async fn new(config: CacheConfig) -> Result<Self, redis::RedisError> {
let l2_cache = if config.enable_l2 {
Arc::new(RwLock::new(RedisCache::new(&config.redis_url)?))
} else {
Arc::new(RwLock::new(RedisCache::new("redis://localhost:6379")?))
};
Ok(Self {
l1_cache: Arc::new(RwLock::new(HashMap::new())),
l2_cache,
config,
})
}
pub async fn get(&self, key: &str) -> Option<String> {
// Try L1 cache first
if self.config.enable_l1 {
if let Some(value) = self.get_from_l1(key).await {
return Some(value);
}
}
// Try L2 cache
if self.config.enable_l2 {
if let Some(value) = self.get_from_l2(key).await {
// Store in L1 cache for future access
if self.config.enable_l1 {
self.store_in_l1(key, &value).await;
}
return Some(value);
}
}
None
}
pub async fn set(&self, key: &str, value: &str) -> Result<(), CacheError> {
// Store in L1 cache
if self.config.enable_l1 {
self.store_in_l1(key, value).await;
}
// Store in L2 cache
if self.config.enable_l2 {
self.store_in_l2(key, value).await?;
}
Ok(())
}
pub async fn delete(&self, key: &str) -> Result<(), CacheError> {
// Delete from L1 cache
if self.config.enable_l1 {
self.delete_from_l1(key).await;
}
// Delete from L2 cache
if self.config.enable_l2 {
self.delete_from_l2(key).await?;
}
Ok(())
}
async fn get_from_l1(&self, key: &str) -> Option<String> {
let mut cache = self.l1_cache.write().await;
if let Some(entry) = cache.get_mut(key) {
// Check if entry is expired
if entry.created_at.elapsed() > entry.ttl {
cache.remove(key);
return None;
}
// Update access count
entry.access_count += 1;
// Implement LRU eviction if cache is full
if cache.len() >= self.config.l1_max_size {
self.evict_lru_entry(&mut cache).await;
}
Some(entry.value.clone())
} else {
None
}
}
async fn store_in_l1(&self, key: &str, value: &str) {
let mut cache = self.l1_cache.write().await;
// Evict if cache is full
if cache.len() >= self.config.l1_max_size {
self.evict_lru_entry(&mut cache).await;
}
cache.insert(key.to_string(), CacheEntry {
value: value.to_string(),
created_at: Instant::now(),
ttl: self.config.l1_ttl,
access_count: 1,
});
}
async fn delete_from_l1(&self, key: &str) {
self.l1_cache.write().await.remove(key);
}
async fn get_from_l2(&self, key: &str) -> Option<String> {
let l2_cache = self.l2_cache.read().await;
match l2_cache.get(key).await {
Ok(value) => value,
Err(_) => None,
}
}
async fn store_in_l2(&self, key: &str, value: &str) -> Result<(), CacheError> {
let l2_cache = self.l2_cache.read().await;
l2_cache.set(key, value, self.config.l2_ttl).await
.map_err(|e| CacheError::RedisError { message: e.to_string() })
}
async fn delete_from_l2(&self, key: &str) -> Result<(), CacheError> {
let l2_cache = self.l2_cache.read().await;
l2_cache.delete(key).await
.map_err(|e| CacheError::RedisError { message: e.to_string() })
}
async fn evict_lru_entry(&self, cache: &mut HashMap<String, CacheEntry>) {
// Find least recently used entry
let lru_key = cache.iter()
.min_by_key(|(_, entry)| entry.access_count)
.map(|(key, _)| key.clone());
if let Some(key) = lru_key {
cache.remove(&key);
}
}
pub async fn get_cache_stats(&self) -> CacheStats {
let l1_size = self.l1_cache.read().await.len();
let l2_size = 0; // Would need to implement size tracking for Redis
CacheStats {
l1_size,
l2_size,
l1_hit_rate: 0.0, // Would need to track hits/misses
l2_hit_rate: 0.0,
}
}
}
#[derive(Debug, thiserror::Error)]
pub enum CacheError {
#[error("Redis error: {message}")]
RedisError { message: String },
#[error("Serialization error: {message}")]
SerializationError { message: String },
}
#[derive(Debug)]
pub struct CacheStats {
pub l1_size: usize,
pub l2_size: usize,
pub l1_hit_rate: f64,
pub l2_hit_rate: f64,
}
🎯 Best Practices
1. Connection Management
- Use connection pooling for high-throughput applications - Monitor connection health and implement automatic failover - Set appropriate connection timeouts and limits - Implement connection retry logic2. Query Optimization
- Analyze slow queries and optimize them - Use appropriate indexes for frequently accessed columns - Implement query caching for expensive operations - Monitor query performance metrics3. Caching Strategy
- Implement multi-level caching (L1, L2, distributed) - Use appropriate cache TTL values - Implement cache invalidation strategies - Monitor cache hit rates and performance4. Transaction Management
- Use transactions for data consistency - Implement proper error handling and rollback - Avoid long-running transactions - Use appropriate isolation levels5. TuskLang Integration
- Use TuskLang configuration for database parameters - Implement database monitoring with TuskLang - Configure caching strategies through TuskLang - Use TuskLang for database schema managementDatabase optimization with TuskLang and Rust provides powerful tools for building high-performance, scalable database applications with comprehensive monitoring and optimization capabilities.