RFC: Enhancing Site Simulator with Real-Time Monitoring, Analytics, and Traceability

[jwalsh]

Motivation:

The current site simulator provides valuable insights into potential system behavior under various simulated scenarios. However, it lacks seamless integration with real-time monitoring and analytics tools, making it challenging to:

• Proactively identify performance bottlenecks and scalability limitations.
• Validate system behavior under different load patterns and user interactions.
• Optimize resource allocation and infrastructure scaling.
• Test new features and deployments in a controlled environment.
• Trace individual simulated user journeys and correlate them with specific requests in production.
• Pinpoint the origin of requests and debug issues arising during simulation.

Proposed Solution:

1. Instrument the simulator:

   • Collect metrics about simulated user actions, response times, error rates, and resource utilization.
   • Expose these metrics in a format compatible with Prometheus for scraping.

2. Integrate with Prometheus and Grafana:

   • Configure Prometheus to scrape metrics from the simulator and the production system.
   • Create Grafana dashboards to visualize and analyze collected metrics in real-time.
   • Set up combined views to compare simulator and production data side-by-side.
   • Configure alerts based on simulator metrics to proactively identify potential issues.

3. Enhance traceability with forced refId:

   • Generate a unique refId for each simulated user or scenario.
   • Include refId as a custom HTTP header in every request generated by the simulator.
   • Capture and log refId in the production system.
   • Utilize refId in analytics and monitoring tools to correlate simulator-generated requests with their corresponding metrics and logs.

4. Flag simulator requests in production:

   • Implement a mechanism (e.g., custom HTTP header, log tagging) to flag requests originating from the simulator.
   • Filter out flagged requests in production analytics and reporting to avoid skewing real-world data.

High-Level Project Structure and Responsibilities:

• Simulator:

  • Generates unique refId and includes it in request headers.
  • Collects and exposes metrics to Prometheus.
  • (Optional) Logs refId and simulation details locally.

• Production System:

  • Captures and logs refId from incoming requests.
  • Exposes metrics to Prometheus.
  • Ensures refId propagation through system components.

• Monitoring and Analytics:

  • Prometheus: Scrapes metrics from the simulator and production.
  • Grafana: Visualizes and analyzes metrics, enabling correlation and comparison using refId.
  • Logging infrastructure: Stores logs containing refId for further analysis.

Benefits:

• Enhanced visibility into system behavior under simulated and real-world conditions.
• Proactive identification and resolution of performance bottlenecks and issues.
• Improved decision-making regarding system optimization, resource allocation, and feature deployments.
• Reduced risk of production issues by testing in a controlled environment.
• Clear traceability between simulated user actions and their impact on the system.

Considerations:

• Simulator realism: Ensure accurate reflection of real-world user behavior and load patterns.
• Metric selection: Choose meaningful metrics for insightful analysis.
• Dashboard design: Create clear and informative Grafana dashboards.
• Alerting strategy: Define appropriate alerting thresholds and notifications.
• refId generation and propagation: Ensure uniqueness and consistent handling.
• Data privacy: Protect sensitive data when using refId.
• Performance overhead: Evaluate the impact of adding and processing refId.

By implementing this enhanced simulator integration, we can leverage the power of simulation, real-time monitoring, and analytics to proactively improve system performance, reliability, and user experience, while maintaining clear traceability and actionable insights.

[jwalsh]

1. Server (Django):

# views.py

from django.http import JsonResponse
from .models import UserJourney 

def get_simulation_data(request):
    # Fetch UserJourney data from the database, potentially filtered or aggregated
    data = ... 
    return JsonResponse(data, safe=False)

# models.py

from django.db import models

class UserJourney(models.Model):
    ref_id = models.CharField(max_length=255)  # Store the refId
    user = models.CharField(max_length=255)
    time_on_site = models.FloatField()
    exit_time = models.DateTimeField()
    conversion = models.BooleanField()
    # Other relevant fields...

2. Database (PostgreSQL - Example):

CREATE TABLE user_journey (
    id SERIAL PRIMARY KEY,
    ref_id VARCHAR(255) NOT NULL,
    user VARCHAR(255) NOT NULL,
    time_on_site FLOAT NOT NULL,
    exit_time TIMESTAMP NOT NULL,
    conversion BOOLEAN NOT NULL
    -- Other relevant columns...
);

3. Monitoring UI (React):

import React, { useState, useEffect } from 'react';
import axios from 'axios';
import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, Legend } from 'recharts'; // Example charting library

function MonitoringUI() {
  const [simulatorData, setSimulatorData] = useState([]);
  const [productionData, setProductionData] = useState([]);

  useEffect(() => {
    // Fetch data from server endpoints (adjust as needed)
    const fetchSimulatorData = async () => { ... };
    const fetchProductionData = async () => { ... };

    // Set up intervals for periodic data fetching
    const simulatorInterval = setInterval(fetchSimulatorData, 5000);
    const productionInterval = setInterval(fetchProductionData, 5000);

    // Clean up intervals on component unmount
    return () => {
      clearInterval(simulatorInterval);
      clearInterval(productionInterval);
    };
  }, []);

  return (
    <div>
      {/* Display charts and tables using simulatorData and productionData */}
      <LineChart ...> {/* Example chart for simulator data */} </LineChart>
      <LineChart ...> {/* Example chart for production data */} </LineChart>
      <table> ... </table> {/* Example table for combined data */}
    </div>
  );
}

4. Clients (Python):

import requests
import uuid

class Client:
    def __init__(self, simulation_scope):
        # ... 

    def simulate_user_interaction(self, user):
        ref_id = str(uuid.uuid4())  # Generate refId

        # Perform site interactions, include ref_id in headers
        headers = {'X-Simulator-RefId': ref_id}
        response = requests.get("https://your-site.com", headers=headers)

        # ... (track user journey, store data in the database, etc.)

Key Points:

• The server provides endpoints to fetch simulation data, potentially allowing filtering or aggregation based on refId or other criteria.
• The database stores UserJourney records, including the refId for traceability.
• The monitoring UI fetches data from the server and visualizes it using charts and tables, potentially enabling comparison between simulator and production data.
• Clients generate a refId, include it in requests, and interact with the site, storing user journey data in the database.

Remember that this is a high-level structure; actual implementations would involve more detailed logic for data handling, visualization, and interaction with external systems like Prometheus.