UsefulLinks
Computer Science
Containerization and Orchestration
Java on Kubernetes
1. Introduction to Java on Kubernetes
2. Foundational Concepts
3. Containerizing Java Applications
4. Deploying Java Applications to Kubernetes
5. Resource Management and Performance
6. Cloud-Native Java Frameworks
7. Observability and Monitoring
8. Security Considerations
9. Advanced Deployment Patterns
10. Performance Optimization
11. Troubleshooting and Debugging
12. Best Practices and Patterns
7.
Observability and Monitoring
7.1.
Health Checks and Probes
7.1.1.
Kubernetes Probe Types
7.1.1.1.
Liveness Probes
7.1.1.2.
Readiness Probes
7.1.1.3.
Startup Probes
7.1.2.
Probe Configuration
7.1.2.1.
HTTP Probes
7.1.2.2.
TCP Probes
7.1.2.3.
Command Probes
7.1.2.4.
Probe Parameters
7.1.3.
Java Application Health Endpoints
7.1.3.1.
Spring Boot Actuator Health
7.1.3.2.
Custom Health Checks
7.1.3.3.
Health Check Best Practices
7.1.4.
Probe Troubleshooting
7.1.4.1.
Common Probe Issues
7.1.4.2.
Debugging Techniques
7.1.4.3.
Performance Impact
7.2.
Logging Strategies
7.2.1.
Container Logging Patterns
7.2.1.1.
Stdout/Stderr Logging
7.2.1.2.
Log File Handling
7.2.1.3.
Sidecar Logging
7.2.2.
Structured Logging
7.2.2.1.
JSON Log Format
7.2.2.2.
Log Field Standardization
7.2.2.3.
Log Parsing and Indexing
7.2.3.
Java Logging Frameworks
7.2.3.1.
Logback Configuration
7.2.3.2.
Log4j2 Configuration
7.2.3.3.
SLF4J Integration
7.2.4.
Log Aggregation
7.2.4.1.
Centralized Logging Architecture
7.2.4.2.
Log Shipping Methods
7.2.4.3.
Log Storage and Retention
7.2.5.
Log Analysis
7.2.5.1.
Log Search and Filtering
7.2.5.2.
Log Correlation
7.2.5.3.
Error Detection and Alerting
7.3.
Metrics Collection
7.3.1.
Metrics Fundamentals
7.3.1.1.
Metric Types
7.3.1.2.
Metric Naming Conventions
7.3.1.3.
Metric Labels and Tags
7.3.2.
Java Metrics Libraries
7.3.2.1.
Micrometer Integration
7.3.2.2.
Prometheus Java Client
7.3.2.3.
Custom Metrics Development
7.3.3.
JVM Metrics
7.3.3.1.
Memory Metrics
7.3.3.2.
Garbage Collection Metrics
7.3.3.3.
Thread Metrics
7.3.3.4.
Class Loading Metrics
7.3.4.
Application Metrics
7.3.4.1.
Business Logic Metrics
7.3.4.2.
Performance Metrics
7.3.4.3.
Error Rate Metrics
7.3.5.
Metrics Exposition
7.3.5.1.
Prometheus Endpoints
7.3.5.2.
Metrics Scraping
7.3.5.3.
Push vs Pull Models
7.3.6.
Metrics Storage and Visualization
7.3.6.1.
Time Series Databases
7.3.6.2.
Dashboard Creation
7.3.6.3.
Alerting Rules
7.4.
Distributed Tracing
7.4.1.
Tracing Concepts
7.4.1.1.
Spans and Traces
7.4.1.2.
Trace Context Propagation
7.4.1.3.
Sampling Strategies
7.4.2.
OpenTelemetry
7.4.2.1.
Tracing API
7.4.2.2.
SDK Configuration
7.4.2.3.
Exporters and Backends
7.4.3.
Java Tracing Implementation
7.4.3.1.
Manual Instrumentation
7.4.3.2.
Automatic Instrumentation
7.4.3.3.
Framework Integration
7.4.4.
Trace Analysis
7.4.4.1.
Trace Visualization
7.4.4.2.
Performance Bottleneck Identification
7.4.4.3.
Error Correlation
7.5.
Monitoring Infrastructure
7.5.1.
Prometheus Setup
7.5.1.1.
Prometheus Configuration
7.5.1.2.
Service Discovery
7.5.1.3.
Recording Rules
7.5.1.4.
Alerting Rules
7.5.2.
Grafana Dashboards
7.5.2.1.
Dashboard Design
7.5.2.2.
Visualization Types
7.5.2.3.
Template Variables
7.5.3.
Alerting Systems
7.5.3.1.
Alert Manager Configuration
7.5.3.2.
Notification Channels
7.5.3.3.
Alert Routing
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8. Security Considerations