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박준희

Posted on • Originally published at aicoreutility.com

Node.js Backend: Visualizing the Observer Pattern and Improving Data Processing Performance

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Improving Node.js Backend: Visualizing Observer Pattern and Enhancing Data Processing Performance

I noticed a deficiency in visualizing observer functionality and handling data within the user interface and backend logic. I tried a few things to fix this, and I'd like to share the process.

Attempts and Pitfalls

Initially, I focused on visualizing nationwide spread phenomena. The idea was to show the spread process by adjusting the activity time for each province using a slider. However, I realized this approach made it difficult to properly represent complex interactions.

// Attempt 1: Visualizing Spread (Conceptual Code)
function visualizeSpread(simulationData, timeSliderValue) {
  const currentTimeData = simulationData.filter(d => d.time <= timeSliderValue);
  // Logic to visualize spread on the map based on currentTimeData
  console.log(`Visualizing spread at time: ${timeSliderValue}`);
  // ... actual visualization code ...
}
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Next, I tried to implement a "conflicting intertwined chains" feature to visualize the self-reinforcing loops between the government and citizens on the ground. The idea was interesting, but I was stumped on how to structure and process the data.

// Attempt 2: Conflicting Intertwined Chains (Conceptual Code)
function createConflictingChains(governmentActions, citizenReactions) {
  const chains = [];
  // Analyze interactions between governmentActions and citizenReactions to create chains
  // Example: Government Policy A -> Citizen Reaction B -> Government Policy C (amplified by Reaction B)
  console.log("Attempting to create conflicting chains...");
  // ... actual logic ...
  return chains;
}
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Critically, when I tried to add functionality to retroactively extract these conflicting chains and separate mega-calls, the data processing volume became unmanageable. I wasted a significant amount of time dealing with unexpected performance degradation and increased complexity. After 3 hours of struggling, I realized that simple visualization couldn't adequately capture a complex system.

The Cause

Ultimately, the problem lay in the data processing and visualization methods between the user interface and the backend logic. The existing approach didn't sufficiently reflect the complexity of spread phenomena or interactions, and data processing efficiency was low. In particular, there was a lack of mechanisms needed to effectively model and visualize dynamic interactions like self-reinforcing loops.

The Solution

I improved the user interface and backend logic to enhance the visualization and data processing capabilities of the 'observer' feature. While keeping the visualization of nationwide spread phenomena with a provincial activity time slider, I newly implemented the 'conflicting intertwined chains' feature to represent the self-reinforcing loops between the government and citizens.

// Solution: Improved Data Processing and Visualization Logic (Conceptual Code)
class ObserverVisualizer {
  constructor(backendService) {
    this.backendService = backendService;
  }

  async visualizeSpreadOverTime(simulationId) {
    const spreadData = await this.backendService.getSpreadData(simulationId);
    // Visualize with the provincial activity time slider using spreadData
    console.log("Visualizing spread with improved logic.");
    // ... actual visualization implementation ...
  }

  async visualizeConflictingChains(interactionData) {
    const processedChains = await this.backendService.processAndExtractChains(interactionData);
    // Visualize processedChains as 'conflicting intertwined chains'
    console.log("Visualizing conflicting chains and mega calls.");
    // ... actual visualization implementation ...
  }
}

// Example of calling the actual backend service
const backend = new BackendService(); // Actual backend service instance
const visualizer = new ObserverVisualizer(backend);

// Visualize nationwide spread phenomena
visualizer.visualizeSpreadOverTime('some-simulation-id');

// Visualize government-citizen interactions
visualizer.visualizeConflictingChains(collectedInteractionData);
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Furthermore, I enhanced data processing efficiency by adding functionality to retroactively extract these conflicting chains and separate mega-calls. This allowed for a clearer understanding of the dynamic interactions within complex systems.

Results

  • Effectively visualized nationwide spread phenomena through a provincial activity time slider.
  • Successfully implemented a visualization feature for 'conflicting intertwined chains' representing self-reinforcing loops between the government and citizens.
  • Increased data processing efficiency by adding retroactive extraction of conflicting chains and mega-call separation.

Takeaways — To Avoid the Same Pitfalls

  • [ ] When visualizing complex interactions, go beyond simple data representation and adopt modeling that can reflect the dynamic characteristics of the system.
  • [ ] When implementing feedback mechanisms like self-reinforcing loops, thorough consideration of data structure design and processing logic must come first.
  • [ ] For large-scale data processing, it's crucial to identify potential performance bottlenecks in advance and apply efficient algorithms and data structures.
  • [ ] The integration between the user interface and backend logic should be achieved through clear API design and consistent data flow.

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