Flask Monitoring Dashboard

This dashboard provides comprehensive monitoring of Flask web application performance, offering detailed visibility into request rates, response times, error tracking, and latency analysis. Built on top of Flask Prometheus metrics, it enables teams to track and optimize their Flask-based web applications.

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What This Dashboard Monitors

This dashboard tracks essential Flask application performance metrics to help you:

Request Rate Monitoring: Track incoming request volume and throughput patterns
Response Time Analysis: Monitor application response times and latency percentiles
Error Rate Tracking: Identify and analyze application errors and failure patterns
Performance Optimization: Analyze request duration distributions for performance tuning
Endpoint Analysis: Monitor specific Flask routes and their performance characteristics

Metrics Included

Request Volume and Rate

Requests Per Second

Description: Rate of successful HTTP requests (status 200) processed by the Flask application
Metric Source: flask_http_request_duration_seconds.count with status filter
Use Case: Monitor application load and traffic patterns
Grouping: By path for endpoint-specific analysis
Critical for: Capacity planning and load balancing decisions

Response Time Analysis

Average Response Time

Description: Mean response time for successful requests across Flask endpoints
Metric Source: flask_http_request_duration_seconds.sum divided by count
Use Case: Track overall application performance and identify slow endpoints
Grouping: By path to identify performance bottlenecks per route
Optimization: Higher values indicate need for performance optimization

Request Duration P90

Description: 90th percentile response time for successful requests
Metric Source: PromQL histogram quantile calculation from duration buckets
Query: histogram_quantile(0.9, rate(flask_http_request_duration_seconds.bucket[30s]))
Use Case: Monitor tail latency and ensure consistent user experience
SLA Monitoring: Critical for performance SLA compliance

Error Monitoring

Errors Per Second

Description: Rate of all HTTP requests regardless of status code
Metric Source: flask_http_request_duration_seconds.count without status filtering
Use Case: Track overall request volume including errors for error rate calculation
Analysis: Compare with successful requests to calculate error percentage
Alerting: Essential for identifying application issues and outages

Performance Insights

Request Rate by Endpoint

Visualization: Time-series graph showing request patterns
Filtering: Focused on successful requests (HTTP 200 status)
Benefits: Identify popular endpoints and traffic distribution
Capacity Planning: Understand which routes require optimization

Latency Distribution

Analysis: P90 latency tracking for performance monitoring
Histogram Data: Utilizes Flask's duration histogram buckets
Performance Tuning: Identify endpoints with inconsistent response times
User Experience: Monitor tail latency impact on user satisfaction

Dashboard Variables

This dashboard can be enhanced with filtering variables for:

instance: Filter by specific Flask application instances
path: Focus on particular Flask routes or endpoints
method: Filter by HTTP methods (GET, POST, PUT, DELETE)
status: Monitor specific HTTP status codes

Flask Metrics Integration

Prometheus Flask Exporter

Required: Flask application instrumented with prometheus_flask_exporter
Metrics Collection: Automatic HTTP request duration and count metrics
Installation: pip install prometheus-flask-exporter
Integration: Simple decorator-based instrumentation

Metric Details

flask_http_request_duration_seconds: Histogram of request durations
Labels: Includes method, status, and path for detailed analysis
Bucket Configuration: Configurable latency buckets for histogram analysis