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Performance Monitoring Standards

This document outlines comprehensive standards for application performance monitoring across Bayat projects. These standards ensure consistent approaches to monitoring, metrics collection, alerting, and performance optimization.

Purpose

Performance monitoring is critical for ensuring application reliability, user satisfaction, and operational efficiency. This document provides:

  1. Standardized Metrics: Define consistent performance metrics across applications
  2. Monitoring Implementation: Guidelines for implementing monitoring solutions
  3. Alerting Thresholds: Standards for setting meaningful alert thresholds
  4. Visualization: Guidelines for performance dashboards
  5. Response Procedures: Standards for responding to performance issues

Monitoring Foundations

Core Principles

Monitoring implementations should adhere to these principles:

  1. User-Centric: Focus on metrics that impact user experience
  2. Comprehensive: Monitor all critical components and services
  3. Actionable: Provide context for effective troubleshooting
  4. Efficient: Minimize monitoring overhead
  5. Proactive: Enable detection of issues before users are impacted

Implementation Phases

Performance monitoring should be implemented in these phases:

  1. Foundation: Core infrastructure and service metrics
  2. Application: Application-specific performance metrics
  3. User Experience: End-user experience monitoring
  4. Business Impact: Correlation with business metrics
  5. Optimization: Advanced analysis for optimization

Standardized Metrics

Frontend Metrics

All web and mobile applications should monitor:

  1. Loading Times:

    • Time to First Byte (TTFB)
    • First Contentful Paint (FCP)
    • Largest Contentful Paint (LCP)
    • First Input Delay (FID)
    • Cumulative Layout Shift (CLS)
    • Time to Interactive (TTI)

  2. Interaction Metrics:

    • User Interaction to Next Paint
    • Response time for key actions
    • Frame rate during animations

  3. Resource Metrics:

    • JavaScript execution time
    • Memory consumption
    • Network request volume and timing
    • Asset loading performance

Backend Metrics

Backend services should monitor:

  1. Response Times:

    • Average response time by endpoint
    • 95th and 99th percentile response times
    • Time spent in database queries
    • Time spent in external service calls

  2. Throughput:

    • Requests per second
    • Transactions per second
    • Data processing volume

  3. Resource Utilization:

    • CPU usage
    • Memory usage
    • Disk I/O
    • Network I/O

Database Metrics

Databases should monitor:

  1. Query Performance:

    • Query execution time
    • Slow query count
    • Index utilization

  2. Throughput:

    • Queries per second
    • Transactions per second
    • Connection count

  3. Resource Utilization:

    • Storage usage and growth
    • Cache hit ratio
    • Lock contention

Infrastructure Metrics

Infrastructure components should monitor:

  1. Compute Resources:

    • CPU utilization
    • Memory utilization
    • Container resource usage

  2. Network:

    • Bandwidth usage
    • Latency
    • Packet loss
    • Connection counts

  3. Storage:

    • IOPS
    • Throughput
    • Latency
    • Capacity utilization

Monitoring Implementation

Standard Tooling

Recommended monitoring tools by category:

  1. Infrastructure Monitoring:

    • Prometheus with Grafana
    • Datadog Infrastructure
    • New Relic Infrastructure

  2. Application Performance Monitoring:

    • New Relic APM
    • Datadog APM
    • Dynatrace
    • Elastic APM

  3. Real User Monitoring:

    • Google Analytics 4
    • New Relic Browser
    • Datadog RUM
    • LogRocket

  4. Synthetic Monitoring:

    • Pingdom
    • Checkly
    • Datadog Synthetics

  5. Log Management:

    • ELK Stack (Elasticsearch, Logstash, Kibana)
    • Datadog Logs
    • Splunk

Instrumentation Standards

Code Instrumentation

Implement instrumentation using these guidelines:

  1. Automatic Instrumentation:

    • Use APM agents for automatic instrumentation where possible
    • Configure appropriate sampling rates

  2. Custom Instrumentation:

    • Instrument critical business transactions
    • Use consistent naming conventions for custom metrics
    • Capture relevant contextual information
// Example of custom instrumentation in Node.js
const metrics = require('metrics-library');

async function processOrder(order) {
  const timer = metrics.startTimer('order_processing');
  try {
    // Order processing logic
    metrics.increment('orders_processed', 1, {
      type: order.type,
      paymentMethod: order.paymentMethod
    });
    return result;
  } catch (error) {
    metrics.increment('orders_failed', 1, {
      type: order.type,
      error: error.name
    });
    throw error;
  } finally {
    timer.end();
  }
}

Infrastructure Instrumentation

Guidelines for infrastructure monitoring:

  1. Host-Level Metrics:

    • Deploy monitoring agents on all hosts
    • Collect system metrics at 15-second intervals

  2. Container Metrics:

    • Monitor container-specific metrics
    • Track container lifecycle events

  3. Cloud Resource Metrics:

    • Integrate with cloud provider monitoring
    • Monitor provisioned and utilized resources

Data Retention

Define standard retention periods:

  1. High-Resolution Metrics: 15 days
  2. Aggregated Hourly Metrics: 90 days
  3. Aggregated Daily Metrics: 13 months
  4. Critical Business Metrics: 3 years

Performance Baseline and Alerting

Baseline Establishment

Guidelines for establishing performance baselines:

  1. Collection Period: Collect metrics for at least 2 weeks
  2. Seasonality: Account for daily, weekly, and monthly patterns
  3. Documentation: Document baseline values with context
  4. Review Cycle: Review baselines quarterly

Alerting Thresholds

Standard threshold guidelines:

  1. Static Thresholds:

    • Critical services: Alert at 80% of capacity
    • Non-critical services: Alert at 90% of capacity
    • Response time: Alert at 2x baseline

  2. Dynamic Thresholds:

    • Implement anomaly detection where possible
    • Alert on significant deviation from baseline
    • Use historical patterns to adjust thresholds

  3. Business Impact Thresholds:

    • Set thresholds based on acceptable business impact
    • Define SLOs and alert on SLO risk

Alert Priority Levels

Define standard alert priority levels:

Level Description Response Time Notification Method
P0 Critical - severe business impact Immediate Call, SMS, email, chat
P1 High - significant user impact < 15 minutes SMS, email, chat
P2 Medium - degraded performance < 30 minutes Email, chat
P3 Low - minor issues Next business day Email, ticket

Visualization and Dashboards

Dashboard Hierarchy

Implement a hierarchy of dashboards:

  1. Executive Dashboard: Business-level metrics and SLOs
  2. Service Dashboard: Service-level health and performance
  3. Resource Dashboard: Detailed resource utilization
  4. Component Dashboard: Specific component performance

Dashboard Components

Each dashboard should include:

  1. Status Summary: Overall health at a glance
  2. Critical Metrics: Key performance indicators
  3. Trends: Historical performance trends
  4. Alerts: Recent and active alerts
  5. Dependencies: Dependency health and performance

Standard Visualizations

Guidelines for standard visualizations:

  1. Time Series:

    • Use consistent time ranges
    • Show percentiles where applicable
    • Include baseline or threshold references

  2. Heatmaps:

    • Use for distribution visualization
    • Apply consistent color schemes

  3. Service Maps:

    • Show service dependencies
    • Indicate performance and health

Response Procedures

Incident Classifications

Standard incident classifications:

Classification Description Example
Performance Degradation Service operating but with reduced performance Response times 2x normal
Partial Outage Service partially unavailable Specific features unavailable
Complete Outage Service entirely unavailable Service returning 5xx errors

Incident Response Protocol

Define standard incident response protocol:

  1. Detection: Confirm alert validity
  2. Assessment: Determine impact and scope
  3. Mitigation: Take immediate action to reduce impact
  4. Resolution: Implement full resolution
  5. Review: Conduct post-incident analysis

Post-Incident Analysis

Requirements for post-incident analysis:

  1. Timeline: Detailed incident timeline
  2. Root Cause: Thorough analysis of root causes
  3. Impact Assessment: Quantify business and user impact
  4. Mitigation Steps: Actions taken to resolve
  5. Prevention Plan: Steps to prevent recurrence

Performance Testing

Testing Requirements

Performance testing requirements:

  1. Load Testing: Verify performance under expected load
  2. Stress Testing: Determine breaking points
  3. Endurance Testing: Verify long-term stability
  4. Spike Testing: Verify handling of sudden load increases

Testing Frequency

Minimum testing frequency requirements:

  1. New Applications: Before initial release
  2. Major Changes: Before deploying significant changes
  3. Periodic: Quarterly for critical applications
  4. Seasonal: Before anticipated usage peaks

Testing Metrics

Metrics to collect during performance testing:

  1. Throughput: Requests/transactions per second
  2. Response Time: Average and percentile response times
  3. Error Rate: Percentage of failed requests
  4. Resource Utilization: CPU, memory, disk, network
  5. Saturation Point: Load at which performance degrades

Implementation Checklist

Use this checklist when implementing monitoring for a new application:

  • Define key performance indicators
  • Select appropriate monitoring tools
  • Configure infrastructure monitoring
  • Implement application instrumentation
  • Establish performance baseline
  • Configure alerting with appropriate thresholds
  • Create standard dashboards
  • Document response procedures
  • Conduct initial performance testing
  • Schedule regular review of monitoring effectiveness

Advanced Monitoring

Machine Learning for Monitoring

Guidelines for implementing advanced monitoring:

  1. Anomaly Detection:

    • Implement for key metrics
    • Train on at least 30 days of data
    • Adjust sensitivity based on false positive rate

  2. Predictive Alerting:

    • Implement for critical resources
    • Alert on predicted issues before they occur
    • Include confidence level with predictions

  3. Correlation Analysis:

    • Identify related metrics and events
    • Surface potential root causes
    • Reduce alert noise

Integrated Monitoring

Standards for integrated monitoring:

  1. Cross-Stack Correlation:

    • Link frontend, backend, and infrastructure issues
    • Trace transactions across service boundaries
    • Correlate logs with performance metrics

  2. Business Impact Analysis:

    • Link performance to business metrics
    • Quantify cost of performance issues
    • Prioritize improvements by business impact

Related Documents

  • \1\2) - General monitoring guidelines
  • \1\2) - Performance testing in CI/CD
  • \1\2) - SRE approach to performance
  • \1\2) - Frontend performance optimization
  • \1\2) - Database performance guidelines