Web Performance Optimization

2.

Core Performance Metrics and Measurement

2.1.

Understanding Key Metrics

2.1.1.

Time to First Byte (TTFB)

2.1.1.1.

Server Processing Time

2.1.1.2.

Network Latency Components

2.1.1.3.

DNS Resolution Time

2.1.1.4.

Connection Establishment

2.1.1.5.

SSL Negotiation

2.1.2.

First Contentful Paint (FCP)

2.1.2.1.

Definition and Importance

2.1.2.2.

Visual Feedback to Users

2.1.2.3.

Factors Affecting FCP

2.1.3.

Largest Contentful Paint (LCP)

2.1.3.1.

Definition and Measurement

2.1.3.2.

Identifying LCP Elements

2.1.3.3.

Common LCP Elements

2.1.3.4.

Optimizing LCP Performance

2.1.4.

First Input Delay (FID)

2.1.4.1.

Definition and Context

2.1.4.2.

Measuring Input Latency

2.1.4.3.

Causes of High FID

2.1.4.4.

Main Thread Blocking

2.1.5.

Interaction to Next Paint (INP)

2.1.5.1.

Definition as FID Successor

2.1.5.2.

Measuring Responsiveness

2.1.5.3.

Event Processing Time

2.1.5.4.

Rendering Delays

2.1.6.

Cumulative Layout Shift (CLS)

2.1.6.1.

Definition and Impact

2.1.6.2.

Layout Shift Score Calculation

2.1.6.3.

Causes of Layout Shifts

2.1.6.4.

Preventing Layout Instability

2.1.7.

2.1.7.1.

Visual Progress Measurement

2.1.7.2.

Calculation Methodology

2.1.7.3.

Use Cases and Limitations

2.1.8.

Total Blocking Time (TBT)

2.1.8.1.

Main Thread Blocking Measurement

2.1.8.2.

Relationship to FID

2.1.9.

Time to Interactive (TTI)

2.1.9.1.

Full Interactivity Measurement

2.1.9.2.

Factors Affecting TTI

2.2.

Google's Core Web Vitals

2.2.1.

Overview and Importance

2.2.2.

Largest Contentful Paint (LCP)

2.2.2.1.

Good vs Poor Thresholds

2.2.2.2.

Optimization Strategies

2.2.3.

First Input Delay (FID) and Interaction to Next Paint (INP)

2.2.3.1.

Measurement Differences

2.2.3.2.

Threshold Values

2.2.3.3.

Improvement Techniques

2.2.4.

Cumulative Layout Shift (CLS)

2.2.4.1.

2.2.4.2.

Prevention Methods

2.2.5.

Evolution and Updates

2.2.5.1.

Historical Changes

2.2.5.2.

Future Considerations

2.3.

Types of Performance Data

2.3.1.

Lab Data (Synthetic Monitoring)

2.3.1.1.

Controlled Environment Testing

2.3.1.2.

Use Cases and Benefits

2.3.1.3.

Limitations and Considerations

2.3.1.4.

Repeatability and Consistency

2.3.2.

Field Data (Real User Monitoring)

2.3.2.1.

Real-World Performance Measurement

2.3.2.2.

Collecting RUM Data

2.3.2.3.

Interpreting Field Data

2.3.2.4.

Variability and Statistical Significance

2.3.3.

Combining Lab and Field Data

2.3.3.1.

Complementary Insights

2.3.3.2.

Data Correlation Techniques

2.4.

Performance Analysis Tools

2.4.1.

Browser Developer Tools

2.4.1.1.

Chrome DevTools Performance Tab

2.4.1.2.

Network Tab Analysis

2.4.1.3.

Lighthouse Integration

2.4.1.4.

Memory and CPU Profiling

2.4.2.

2.4.2.1.

Audit Categories

2.4.2.2.

Performance Score Calculation

2.4.2.3.

Opportunities and Diagnostics

2.4.2.4.

CI/CD Integration

2.4.3.

2.4.3.1.

Test Configuration Options

2.4.3.2.

Waterfall Chart Analysis

2.4.3.3.

2.4.3.4.

Advanced Testing Features

2.4.4.

Google PageSpeed Insights

2.4.4.1.

Field vs Lab Data Presentation

2.4.4.2.

Core Web Vitals Reporting

2.4.4.3.

Optimization Suggestions

2.4.5.

2.4.5.1.

Performance Reports

2.4.5.2.

Recommendations Analysis

2.4.5.3.

Historical Tracking

2.4.6.

Chrome User Experience Report (CrUX)

2.4.6.1.

Real User Data Access

2.4.6.2.

2.4.6.3.

BigQuery Integration

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1. Introduction to Web Performance

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3. The Critical Rendering Path