title: "Performance Tuning Guide" description: "Comprehensive guide for optimizing the performance of Navius applications, including database, memory, caching, and network optimizations" category: "Guides" tags: ["performance", "optimization", "database", "caching", "memory", "profiling", "benchmarking"] last_updated: "April 5, 2025" version: "1.0"

Performance Tuning Guide

Overview

This guide provides comprehensive strategies for optimizing the performance of Navius applications. Performance tuning is essential for ensuring that your application responds quickly, uses resources efficiently, and can handle increasing loads as your user base grows.

Key Performance Areas

When optimizing a Navius application, focus on these key areas:

Database Performance - Optimizing queries and database access patterns
Caching Strategies - Implementing effective caching to reduce database load
Memory Management - Minimizing memory usage and preventing leaks
Concurrency - Optimizing async code execution and thread management
Network I/O - Reducing latency in network operations
Resource Utilization - Balancing CPU, memory, and I/O operations

Performance Measurement

Benchmarking Tools

Before optimizing, establish baseline performance metrics using these tools:

Criterion - Rust benchmarking library
wrk - HTTP benchmarking tool
Prometheus - Metrics collection and monitoring
Grafana - Visualization of performance metrics

Key Metrics to Track

Request latency (p50, p95, p99 percentiles)
Throughput (requests per second)
Error rates
Database query times
Memory usage
CPU utilization
Garbage collection pauses
Cache hit/miss ratios

Database Optimization

Query Optimization

Use the PostgreSQL query planner with EXPLAIN ANALYZE
Optimize indexes for common query patterns
Review and refine complex joins
Consider materialized views for complex aggregations

#![allow(unused)]
fn main() {
// Example: Using an index hint in a query
let users = sqlx::query_as::<_, User>("SELECT /*+ INDEX(users idx_email) */ * FROM users WHERE email LIKE $1")
    .bind(format!("{}%", email_prefix))
    .fetch_all(&pool)
    .await?;
}

Connection Pooling

Configure appropriate connection pool sizes
Monitor connection usage patterns
Implement backpressure mechanisms

#![allow(unused)]
fn main() {
// Optimal connection pooling configuration
let pool = PgPoolOptions::new()
    .max_connections(num_cpus::get() * 2) // Rule of thumb: 2x CPU cores
    .min_connections(5)
    .max_lifetime(std::time::Duration::from_secs(30 * 60)) // 30 minutes
    .idle_timeout(std::time::Duration::from_secs(10 * 60)) // 10 minutes
    .connect(&database_url)
    .await?;
}

Database Access Patterns

Implement the repository pattern for efficient data access
Use batch operations where appropriate
Consider read/write splitting for high-load applications

Caching Strategies

Multi-Level Caching

Implement the Navius two-tier caching pattern:

L1 Cache - In-memory cache for frequently accessed data
L2 Cache - Redis for distributed caching and persistence

#![allow(unused)]
fn main() {
// Two-tier cache configuration
cache:
  enabled: true
  providers:
    - name: "memory"
      type: "memory"
      max_items: 10000
      ttl_seconds: 60
    - name: "redis"
      type: "redis"
      connection_string: "redis://localhost:6379"
      ttl_seconds: 300
  default_provider: "memory"
  fallback_provider: "redis"
}

Optimizing Cache Usage

Cache expensive database operations
Use appropriate TTL values based on data volatility
Implement cache invalidation strategies
Consider cache warming for critical data

Memory Optimization

Rust Memory Management

Use appropriate data structures to minimize allocations
Leverage Rust's ownership model to prevent memory leaks
Consider using Arc instead of cloning large objects
Profile memory usage with tools like heaptrack or valgrind

Leak Prevention

Implement proper resource cleanup in drop implementations
Use structured concurrency patterns
Monitor memory growth over time

Concurrency Optimization

Async Runtime Configuration

Configure Tokio runtime with appropriate thread count
Use work-stealing runtime for balanced load distribution

#![allow(unused)]
fn main() {
// Configure the Tokio runtime
let runtime = tokio::runtime::Builder::new_multi_thread()
    .worker_threads(num_cpus::get())
    .enable_all()
    .build()
    .unwrap();
}

Task Management

Break large tasks into smaller, manageable chunks
Implement backpressure for task submission
Use appropriate buffer sizes for channels
Consider using spawn_blocking for CPU-intensive tasks

Network I/O Optimization

HTTP Client Configuration

Configure appropriate timeouts
Use connection pooling
Implement retry strategies with exponential backoff
Consider enabling HTTP/2 for multiplexing

#![allow(unused)]
fn main() {
// Efficient HTTP client configuration
let client = reqwest::Client::builder()
    .timeout(std::time::Duration::from_secs(30))
    .pool_max_idle_per_host(10)
    .connect_timeout(std::time::Duration::from_secs(5))
    .build()?;
}