Observable Platform 4.1: Web Frontend/Backend/Gateway Alert Configuration Refere

Web Frontend Alert Configuration Reference

This section provides guidance on configuring alerts for Web frontend applications, including log/metrics exporters, Prometheus monitoring rules (in YAML format), alerting rules, and recommendations for suitable Grafana dashboard configurations.

Vue3 Web Frontend Log/Metrics Exporters

• Log/Metrics Exporters

Utilize frontend monitoring tools such as the Performance API to collect performance metrics (page load time, FCP, FMP, FID, CLS).

Capture and record JavaScript errors using window.onerror and window.addEventListener('error', ...).

Record the loading status of static resources and Ajax requests using the Navigation Timing API and Resource Timing API.

Integrate user behavior tracking tools (e.g., Google Analytics or custom event tracking logic) to monitor user clicks, page visits, and navigation paths.

For form interactions, use custom event listeners to capture and export metrics.

Vue3 Web Frontend Prometheus Monitoring Rules (YAML)

• Monitoring Rulesgroups: - name: vuejs_frontend_performance_metrics rules: - record: vuejs_page_load_time expr: rate(page_load_time_seconds_sum{job="vuejs_frontend"}[5m]) / rate(page_load_time_seconds_count{job="vuejs_frontend"}[5m]) - record: vuejs_fcp_time expr: rate(first_contentful_paint_seconds{job="vuejs_frontend"}[5m]) - record: vuejs_fmp_time expr: rate(first_meaningful_paint_seconds{job="vuejs_frontend"}[5m]) - record: vuejs_fid_time expr: rate(first_input_delay_seconds{job="vuejs_frontend"}[5m]) - record: vuejs_cls_score expr: rate(cumulative_layout_shift_score{job="vuejs_frontend"}[5m])

Vue3 Web Frontend Prometheus Alerting Rules (YAML)

Alerting Rules

groups:
- name: vuejs_frontend_alerts
  rules:
  - alert: HighPageLoadTime
    expr: vuejs_page_load_time > 3
    for: 1m
    labels:
      severity: warning
    annotations:
      summary: "High page load time on Vue.js frontend"
      description: "Vue.js frontend is experiencing a high page load time."

  - alert: HighCLS
    expr: vuejs_cls_score > 0.1 # Adjust based on your UX requirements
    for: 1m
    labels:
      severity: warning
    annotations:
      summary: "High Cumulative Layout Shift on Vue.js frontend"
      description: "Vue.js frontend is experiencing high cumulative layout shift, impacting user experience."

Vue3 Grafana Dashboard

For Grafana dashboards, I recommend visiting the Grafana Dashboards website and searching for dashboards related to "Vue.js" or "Web Performance." You will find a variety of community-contributed dashboards specifically designed for monitoring frontend applications. These dashboards typically include key metrics such as page load time, FCP, FMP, FID, CLS, and more.

A typical example might be a dashboard designed specifically for frontend performance monitoring, providing visual analysis of page load times, user interactions, and resource loading. You can add these dashboards to your Grafana instance by importing the dashboard ID or downloading the JSON file directly from the website.

Nginx Gateway Alert Configuration Reference

For your Nginx gateway alert configuration requirements, I will provide an overview of open-source Nginx log/metrics exporters, Prometheus monitoring and alerting rules (in YAML format), and Grafana dashboard configurations.

Open-Source Nginx Log/Metrics Exporters

Nginx Log Exporter

Prometheus Nginx Log Exporter: This tool parses Nginx logs and exports data in Prometheus format.

mtail: A log-based metrics extraction tool that can be used to extract and export metrics from Nginx logs.

Nginx Metric Exporter

Nginx Prometheus Exporter: An official exporter provided by Nginx that collects metrics from Nginx's status module and exports them to Prometheus.

Nginx Prometheus Monitoring Rules (YAML)

Here is an example of monitoring rules in YAML format:

Copy code
groups:
- name: nginx_metrics
  rules:
  - alert: HighRequestLatency
    expr: nginx_http_request_duration_seconds{job="nginx"} > 0.5
    for: 1m
    labels:
      severity: warning
    annotations:
      summary: "High request latency on Nginx"
      description: "Nginx server {{ $labels.instance }} has a high request latency of more than 0.5 seconds."

  - alert: HighErrorRate
    expr: rate(nginx_http_requests_total{status=~"5.."}[2m]) / rate(nginx_http_requests_total[2m]) > 0.05
    for: 1m
    labels:
      severity: critical
    annotations:
      summary: "High error rate on Nginx"
      description: "Nginx server {{ $labels.instance }} is experiencing a high error rate."

Nginx Prometheus Alerting Rules (YAML)

Alerting rules are similar to monitoring rules but focus on situations that may require immediate response. Here is an example of alerting rules:

groups:
- name: nginx_alerts
  rules:
  - alert: NginxDown
    expr: up{job="nginx"} == 0
    for: 1m
    labels:
      severity: critical
    annotations:
      summary: "Nginx down"
      description: "Nginx server {{ $labels.instance }} is down."

  - alert: HighTraffic
    expr: sum(rate(nginx_http_requests_total[5m])) by (instance) > 1000
    for: 1m
    labels:
      severity: warning
    annotations:
      summary: "High traffic on Nginx"
      description: "Nginx server {{ $labels.instance }} is experiencing high traffic."

Nginx Grafana Dashboard

For Nginx Grafana dashboards, you can find many community-contributed pre-built dashboards on the official Grafana website designed specifically for monitoring Nginx. You can directly import these dashboards and customize them as needed. For example:

Nginx HTTP Dashboard: Provides visualizations of HTTP requests, error rates, throughput, and more.

Nginx Performance Dashboard: Focuses on performance metrics such as response times and connection counts.

Configure these tools and dashboards according to your specific requirements and environment. It's important to tailor your monitoring and alerting settings to your actual operational environment and business needs.

Python Backend Service Alert Configuration Reference

Based on the detailed information you provided regarding application performance metrics, system resource usage, application health and availability, business-related metrics, and log and error monitoring, I will update the Python backend service's log/metrics exporter configuration, Prometheus monitoring rules (in YAML format), alerting rules, and recommend a suitable Grafana dashboard configuration.

Python Backend Service Log/Metrics Exporters

Log/Metrics Exporters

Use the logging module to record and export logs.

Utilize the prometheus_client library to export performance metrics such as response times, request throughput, and error rates.

Monitor system resource usage (CPU, memory, disk I/O, network I/O) using the psutil library or node_exporter (Prometheus).

Monitor application health and availability by implementing regular health check endpoints (e.g., /health in Flask or Django) and using custom exporters for monitoring.

For business-related metrics such as user activity or specific business process performance, you can create custom metrics based on business logic and export them via Prometheus.

Python Backend Service Prometheus Monitoring Rules (YAML)

Monitoring Rules

groups:
- name: python_backend_metrics
  rules:
  - record: python_http_requests_duration_seconds
    expr: rate(python_http_requests_duration_seconds_sum{job="python_backend"}[5m]) / rate(python_http_requests_duration_seconds_count{job="python_backend"}[5m])

  - record: python_http_requests_total
    expr: sum(rate(http_requests_total{job="python_backend"}[5m])) by (method, endpoint)

  - record: python_memory_usage_bytes
    expr: process_resident_memory_bytes{job="python_backend"}

  - record: python_cpu_usage_percentage
    expr: rate(process_cpu_seconds_total{job="python_backend"}[5m])

Python Backend Service Prometheus Alerting Rules (YAML)

Alerting Rules

Copy code
groups:
- name: python_backend_alerts
  rules:
  - alert: HighResponseTime
    expr: python_http_requests_duration_seconds > 1
    for: 1m
    labels:
      severity: warning
    annotations:
      summary: "High response time in Python backend"
      description: "Python backend service is experiencing a high response time."

  - alert: HighMemoryUsage
    expr: python_memory_usage_bytes > 500000000 # Adjust based on your environment
    for: 1m
    labels:
      severity: critical
    annotations:
      summary: "High memory usage in Python backend"
      description: "Python backend service is using high memory."

Java Backend Service

Prometheus Monitoring Rules (YAML)

Monitoring Rules

groups:
- name: java_backend_metrics
  rules:
  - record: java_http_requests_duration_seconds
    expr: rate(java_http_requests_duration_seconds_sum{job="java_backend"}[5m]) / rate(java_http_requests_duration_seconds_count{job="java_backend"}[5m])

  - record: java_http_requests_total
    expr: sum(rate(http_requests_total{job="java_backend"}[5m])) by (method, uri)

  - record: java_jvm_memory_usage
    expr: jvm_memory_used_bytes{job="java_backend"}

  - record: java_gc_duration_seconds
    expr: rate(jvm_gc_pause_seconds_sum{job="java_backend"}[5m])

Prometheus Alerting Rules (YAML)

Alerting Rules

groups:
- name: java_backend_alerts
  rules:
    - alert: HighResponseTime
      expr: java_http_requests_duration_seconds > 2
      for: 1m
      labels:
        severity: warning
      annotations:
        summary: "High response time in Java backend"
        description: "Java backend service is experiencing a high response time."
    - alert: HighMemoryUsage
      expr: java_jvm_memory_usage > 1000000000 # Adjust based on your environment
      for: 1m
      labels:
        severity: critical
      annotations:
        summary: "High JVM memory usage in Java backend"
        description: "Java backend service is using high JVM memory."
    - alert: HighGCDuration
      expr: java_gc_duration_seconds > 0.2 # Adjust the threshold based on your requirements
      for: 1m
      labels:
        severity: critical
      annotations:
        summary: "High GC duration in Java backend"
        description: "Java backend service is experiencing high garbage collection duration."

Grafana Dashboard - Java Application Monitoring

For Grafana dashboards, I recommend visiting the Grafana Dashboards website and searching for dashboards related to "Java," "Spring Boot," or "Dubbo." You will find a variety of community-contributed dashboards specifically designed for monitoring Java applications. These dashboards typically include key metrics such as JVM usage, HTTP request metrics, system resource usage, and more.

A typical example is the "JVM (Micrometer)" dashboard (usually dashboard ID 4701), which provides comprehensive JVM monitoring metrics suitable for Spring Boot applications monitored using Micrometer or similar libraries.

Choose the appropriate dashboard based on your specific requirements and environment configuration, and you may need to make some adjustments based on your specific settings. You can add these dashboards to your Grafana instance by importing the dashboard ID or downloading the JSON file directly from the website.