Monitoring Metrics for Vue 3 Front-End Applications
Monitoring a Vue 3 front-end application is crucial for ensuring user experience and application performance. Front-end monitoring typically involves several key aspects:
- Performance Monitoring
- Page Load Time: Records the time it takes to fully load the entire page.
- First Contentful Paint (FCP): The time it takes for the page to start providing visual feedback.
- First Meaningful Paint (FMP): The time it takes for the primary content of the page to render.
- First Input Delay (FID): The time between the user's first interaction and the page's response.
- Cumulative Layout Shift (CLS): The sum of page layout changes that impact user experience.
- User Experience Monitoring
- User Behavior Tracking: Tracking user actions such as clicks, page visits, navigation paths, and more.
- Form Interactions: Monitoring the usage and completion rates of forms.
- Resource Loading
- Loading of Static Resources: Monitoring the loading times of resources like CSS, JavaScript, images, etc.
- Ajax Request Monitoring: Tracking the success rate and response times of API requests.
- Error Monitoring
- JavaScript Errors: Capturing and logging front-end JavaScript errors.
- Resource Loading Errors: Detecting errors when resources like images, CSS, or JavaScript files fail to load.
- Browser and Device Compatibility
- Cross-Browser Behavior: Monitoring application performance on different browsers and versions.
- Responsive Design Effectiveness: Assessing how the application behaves on different devices and screen sizes.
Monitoring Tools and Technologies
- Google Analytics: Used for user behavior analysis.
- Lighthouse / PageSpeed Insights: Utilized for web page performance analysis.
- Sentry: For error tracking and monitoring.
- LogRocket: Records and replays user sessions for issue analysis.
- Chrome DevTools: Used for performance analysis and debugging.
Monitoring these metrics can help you optimize user experience, improve application performance, and promptly identify and address front-end issues.
Monitoring Nginx Ingress Gateway Core Metrics
Monitoring the Nginx Ingress Gateway core is crucial for ensuring traffic management and routing in a microservices architecture. When it comes to such a gateway component, you typically need to keep an eye on the following metrics:
Traffic Monitoring
- Request Throughput: The number of requests processed per second.
- Request Distribution: Distribution of requests across different services or routes.
Performance Metrics
- Response Time: The time it takes to process a request.
- Connection Count: The current active connection count.
- Concurrency: The number of simultaneous requests being handled.
Error and Status Codes
- HTTP Error Rate: The ratio of 4xx and 5xx responses.
- Client and Server Errors: Separately record errors caused by clients and servers.
Resource Usage
- CPU and Memory Usage: Resource utilization on the gateway nodes.
- Bandwidth Usage: Inbound and outbound network traffic.
Security Monitoring
- SSL/TLS Certificate Status: Validity and expiration time of certificates.
- Attacks and Threat Attempts: Detection of DDoS attacks and malicious requests.
Availability and Health Checks
- Service Uptime: The gateway component's uptime.
- Health Check Status: Results of health checks for backend services.
Monitoring Tools and Technologies
- Prometheus: Used for collecting and storing metric data.
- Grafana: Utilized for visualizing monitoring data.
- Nginx log analysis tools: Such as the ELK Stack (Elasticsearch, Logstash, Kibana) or Graylog for log collection and analysis.
- Alertmanager: Integrated with Prometheus for alert management.
- Custom scripts or tools: Used for certificate status checks or custom health checks.
Implementation Steps
- Configure Nginx Logs: Ensure that Nginx access and error logs are correctly configured for collecting critical information.
- Use Prometheus Exporter: For example, use the Nginx Prometheus Exporter to expose Nginx metrics to Prometheus.
- Set up Grafana Dashboards: Create dashboards in Grafana to display Nginx performance metrics.
- Configure Alerting Rules: Configure alerts based on key metrics in Prometheus or Alertmanager.
By monitoring these metrics and using the mentioned tools, you can ensure the stable and reliable operation of Nginx-based Ingress Gateway in a microservices architecture and promptly identify and respond to potential issues.
Monitoring Items for Java Applications Using Spring Boot Framework
For a Java application based on the Spring Boot framework, key monitoring aspects include metrics, logs, and link tracing. After collecting data using OpenTelemetry, various methods can be used for querying and analysis. Here are the focal points and example codes for these aspects:
Performance Metrics (Metrics):
In a Spring Boot application, the following metrics might be of interest:
- Response Time: The time taken for the service to respond to client requests.
- Throughput: The number of requests processed per unit of time.
- JVM Metrics: Heap memory usage, garbage collection frequency, and duration.
- Request Latency: Response time for HTTP requests.
- Request Throughput: The number of requests processed per unit time.
- System Resource Usage: Such as CPU usage, memory usage.
- Database Operation Metrics: Like query times, update times.
Business Metrics:
- For instance, the number of user registrations, transaction volumes, etc.
Logs (Logs):
Key aspects to focus on for logs might include:
- Error Logs: Detailed information on exceptions and errors.
- Warning Logs: Potential issues, like resources nearing limits.
- Access Logs: Detailed information on user requests.
- System Events: Start-ups, shutdowns, configuration changes, etc.
Link Tracing (Traces)
Link tracing typically focuses on the following aspects:
- Request Path: The complete path of a request through the system.
- Inter-Service Calls: The calling relationships among services in a microservices architecture.
- Performance Bottlenecks: Identifying performance bottlenecks in the system.
Application Health and Availability:
- Service health check status.
- Database connection status.
Query and Analysis
- Metrics Query: Metrics exported by OpenTelemetry can be queried using Prometheus or similar tools.
- Log Analysis: Logs can be queried and analyzed through Elasticsearch or similar log management systems.
- Link Tracing Analysis: Tools like Jaeger, Zipkin, etc., can be used for visualizing and analyzing link data.
These code examples provide a basic conceptual framework. In practical applications, you need to adjust and expand these examples according to specific business logic and monitoring requirements.
Gateway Monitoring
As the entry point in a microservices architecture, the gateway requires monitoring of:
- Request Latency: Time taken by the gateway to process requests.
- Request and Response Size: The volume of data incoming and outgoing.
- Error Rate: The proportion of error responses at the gateway level.
- Throughput: The number of requests processed by the gateway per unit time.
- Concurrent Connections.
Configuration Center Monitoring
The configuration center, a key component in microservices architecture, requires monitoring of:
- Configuration Read Count: The number of times services read configurations from the center.
- Configuration Change Events: The frequency and content of configuration updates.
- Response Time: Time taken by the center to process requests.
- Availability and Health Status.
Registry Center Monitoring
The registry center, central to service discovery and management, requires monitoring of:
- Service Registration/Deregistration Events: The number of times service instances register and deregister.
- Heartbeat Loss: Instances failing to send regular heartbeats.
- Availability and Response Time: The health status and performance of the registry center.
Tools and Technologies
- Spring Actuator: Provides health and performance metrics of applications.
- Micrometer: Collects application metric data.
- Prometheus: For storing and querying metric data.
- Grafana: For visualizing metrics.
- Elasticsearch Logstash Kibana (ELK Stack): For log management and analysis.
- Zipkin/Jaeger: For link tracing analysis.
These monitoring items and tools help better understand and manage the performance and health of Spring Boot-based microservices applications and their key components.
Monitoring Items for Dubbo Framework Applications
Dubbo is a high-performance Java RPC framework widely used in microservices architectures. Monitoring Dubbo applications involves various aspects, including service performance, availability, system resource usage, and inter-service communication. Below are some key monitoring items:
Service Performance Metrics
- Call Latency: The time taken by the service to respond to requests.
- Service Throughput: The number of service requests processed per unit time.
- Service Success Rate: The ratio of successfully processed requests.
- Average Load: The average load on service nodes.
Service Availability
- Service Health Status: Whether the service is running normally.
- Service Circuit Breaker Status: Checking if the circuit breaker is triggered to prevent system overload.
- Service Degradation: Service degradation operations and their reasons.
System Resource Usage
- JVM Metrics: Heap memory usage, GC frequency, and duration.
- CPU Usage: CPU usage of the service nodes.
- Memory Usage: The amount of memory used by the service.
- Disk I/O and Network I/O: Disk and network input/output conditions.
Inter-Service Communication
- Service Call Links: Calling relationships and pathways between services.
- Service Dependency Graph: The dependency graph between services
Python Backend Service Monitoring Project
For Python-based backend services, the key purpose of monitoring is to ensure the health, performance, and reliability of the service. Here are some crucial monitoring items:
- Application Performance Metrics
- Response Time: Time taken to process requests.
- Request Throughput: Number of requests processed per unit time.
- Error Rate: Proportion of failed requests.
- System Resource Usage
- CPU Usage: Percentage of CPU occupied by the service.
- Memory Usage: Amount of memory used by the service.
- Disk I/O: Frequency and volume of disk read/write operations.
- Network I/O: Amount of network data transfer.
- Application Health and Availability
- Service Status: For example, through health check endpoints.
- Database Connections: Status and usage rate of the database connection pool.
- Business Related Metrics
- User Activity: Such as the number of concurrent online users.
- Business Process Performance: Performance metrics for specific business processes (e.g., order processing).
- Logs and Error Monitoring
- Error Logs: Capture and analyze error logs for quick problem identification.
- Access Logs: Logs of user requests for analyzing user behavior and request patterns.
- External Dependency Monitoring
- External API Calls: Monitoring calls to external services, such as response time, failure rate, etc.
- Middleware Performance: Performance and health status of middleware like message queues, caching systems.
Monitoring Tools and Technologies
- Prometheus: For collecting and storing metric data.
- Grafana: For visualization of metric data.
- Elasticsearch Logstash Kibana (ELK Stack): For log collection, storage, and analysis.
- New Relic / Datadog: Comprehensive performance monitoring and APM (Application Performance Management) solutions.
- Sentry: Error tracking and monitoring.
- Flask/Django Monitoring Plugins: If your application is built with Flask or Django, specific plugins are available for monitoring application performance.
Monitoring these metrics helps in timely identification and resolution of issues in backend services, optimizing performance, and ensuring high availability and reliability.
GO Backend Service Monitoring Project
Effective monitoring strategies for Go (Golang) based backend services are crucial for ensuring the health, performance, and reliability of the service. Here are some important monitoring items:
- Application Performance Metrics
- Response Time: Time taken to process requests.
- Request Throughput: Number of requests processed per unit time.
- Error Rate: Proportion of failed requests.
- System Resource Usage
- CPU Usage: Percentage of CPU occupied by the service.
- Memory Usage: Amount of memory used by the service.
- Disk I/O: Frequency and volume of disk read/write operations.
- Network I/O: Amount of network data transfer.
- Application Health and Availability
- Service Status: For example, through health check endpoints.
- Goroutine Count: Number of running Goroutines.
- GC Metrics: Frequency and duration of garbage collection.
- Business Related Metrics
- User Activity: Such as the number of concurrent online users.
- Business Process Performance: Performance metrics for specific business processes (e.g., order processing).
- Logs and Error Monitoring
- Error Logs: Capture and analyze error logs for quick problem identification.
- Access Logs: Logs of user requests for analyzing user behavior and request patterns.
- External Dependency Monitoring
- External API Calls: Monitoring calls to external services, such as response time, failure rate, etc.
- Middleware Performance: Performance and health status of middleware like message queues, caching systems.
Monitoring Tools and Technologies
- Prometheus: For collecting and storing metric data.
- Grafana: For visualization of metric data.
- Elasticsearch Logstash Kibana (ELK Stack): For log collection, storage, and analysis.
- Go Profiling Tools: Utilizing Go's pprof package for performance analysis.
- Jaeger or Zipkin: For distributed tracing and monitoring.
- Promtail/Loki: For log aggregation and monitoring.
Monitoring these metrics helps in timely identification and resolution of issues in backend services, optimizing performance, and ensuring high availability and reliability. Go's lightweight and high-performance characteristics make it particularly important for monitoring these metrics, especially when handling large numbers of concurrent requests.
