Performance Engineering and Optimization

7.

Performance Analysis and Tuning

7.1.

Systematic Tuning Methodology

7.1.1.

Performance Tuning Process

7.1.1.1.

Establishing Baselines

7.1.1.2.

Iterative Improvement

7.1.1.3.

Measure-Profile-Identify-Tune-Repeat

7.1.2.

Performance Hypothesis Testing

7.1.3.

Change Management in Tuning

7.2.

Profiling Techniques

7.2.1.

7.2.1.1.

Hotspot Identification

7.2.1.2.

Call Graph Analysis

7.2.1.3.

Sampling vs Instrumentation

7.2.2.

Memory Profiling

7.2.2.1.

7.2.2.2.

7.2.2.3.

Memory Allocation Patterns

7.2.3.

7.2.3.1.

Disk I/O Analysis

7.2.3.2.

Network I/O Analysis

7.2.3.3.

File System Performance

7.2.4.

Concurrency Profiling

7.2.4.1.

Thread Analysis

7.2.4.2.

Lock Contention Detection

7.2.4.3.

Deadlock Detection

7.2.5.

Application-specific Profiling

7.3.

Application-Level Optimization

7.3.1.

Code Optimization

7.3.1.1.

Algorithmic Complexity Reduction

7.3.1.2.

Loop Optimization

7.3.1.3.

Reducing Object Creation

7.3.1.4.

Inlining and Compiler Optimizations

7.3.2.

Memory Management

7.3.2.1.

Garbage Collection Tuning

7.3.2.2.

Memory Leak Prevention

7.3.2.3.

7.3.2.4.

Memory Layout Optimization

7.3.3.

Concurrency and Parallelism

7.3.3.1.

Thread Pool Tuning

7.3.3.2.

Lock Contention Analysis

7.3.3.3.

Non-blocking Algorithms

7.3.3.4.

Parallel Processing Patterns

7.3.4.

Caching Optimization

7.3.4.1.

In-Memory Caching

7.3.4.2.

Distributed Caching

7.3.4.3.

Cache Invalidation Strategies

7.3.4.4.

Cache Hit Ratio Optimization

7.3.5.

I/O Optimization

7.3.5.1.

Asynchronous I/O

7.3.5.2.

Batch Processing

7.3.5.3.

Connection Pooling

7.4.

Database-Level Optimization

7.4.1.

Query Optimization

7.4.1.1.

Indexing Strategies

7.4.1.2.

Execution Plan Analysis

7.4.1.3.

Query Rewriting

7.4.2.

Connection Management

7.4.2.1.

Connection Pooling

7.4.2.2.

7.4.2.3.

Connection Reuse

7.4.3.

Database Configuration Tuning

7.4.3.1.

Buffer Pool Sizing

7.4.3.2.

Query Cache Settings

7.4.3.3.

Transaction Log Optimization

7.4.4.

Schema Optimization

7.4.4.1.

Denormalization for Performance

7.4.4.2.

Partitioning and Sharding

7.4.4.3.

Data Type Selection

7.4.5.

Database Maintenance

7.4.5.1.

Index Maintenance

7.4.5.2.

Statistics Updates

7.4.5.3.

Defragmentation

7.5.

Operating System and Platform Tuning

7.5.1.

Kernel Parameter Tuning

7.5.1.1.

TCP Stack Tuning

7.5.1.2.

File Descriptor Limits

7.5.1.3.

Memory Management Parameters

7.5.2.

Virtual Machine Tuning

7.5.2.1.

7.5.2.1.1.

7.5.2.1.2.

Garbage Collector Selection

7.5.2.1.3.

JIT Compiler Options

7.5.2.2.

7.5.2.3.

Runtime Optimization

7.5.3.

Container Resource Management

7.5.3.1.

CPU and Memory Quotas

7.5.3.2.

Resource Isolation

7.5.3.3.

Container Orchestration Tuning

7.5.4.

File System Optimization

7.6.

Network-Level Optimization

7.6.1.

Latency Reduction Techniques

7.6.1.1.

Protocol Optimization

7.6.1.2.

Connection Pooling

7.6.1.3.

Keep-alive Optimization

7.6.2.

Bandwidth Utilization

7.6.2.1.

7.6.2.2.

Efficient Data Transfer

7.6.2.3.

Protocol Selection

7.6.3.

Load Balancer Configuration

7.6.3.1.

Load Distribution Algorithms

7.6.3.2.

7.6.3.3.

Session Affinity

7.6.4.

Content Delivery Networks

7.6.4.1.

7.6.4.2.

Geo-distribution

7.6.4.3.

CDN Configuration

7.6.5.

Network Protocol Optimization

7.7.

Front-End and Web Performance Optimization

7.7.1.

Critical Rendering Path

7.7.1.1.

Render-blocking Resources

7.7.1.2.

DOM Optimization

7.7.1.3.

CSS Optimization

7.7.2.

Asset Optimization

7.7.2.1.

7.7.2.2.

7.7.2.3.

Image Optimization

7.7.3.

Reducing HTTP Requests

7.7.3.1.

Resource Bundling

7.7.3.2.

7.7.3.3.

HTTP/2 Optimization

7.7.4.

Browser Caching

7.7.4.1.

Cache-Control Headers

7.7.4.2.

Service Workers

7.7.4.3.

Application Cache

7.7.5.

Core Web Vitals

7.7.5.1.

Largest Contentful Paint

7.7.5.2.

First Input Delay

7.7.5.3.

Cumulative Layout Shift

7.7.6.

Progressive Web App Performance

7.7.7.

Mobile Performance Optimization

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8. Performance in Modern Architectures