Leaderboard Deploy agents

Minimize computational complexity by optimizing iteration patterns and data processing flows. Apply early filtering when possible, but preserve data integrity when order or indexing matters.

Key principles:

  1. Filter early: Apply filters at the data source rather than after multiple transformations
  2. Avoid nested loops: Replace nested iterations with more efficient single-pass algorithms when possible
  3. Consider data integrity: Sometimes separate operations are necessary to maintain correct indexing or ordering

Examples:

❌ Avoid late filtering:

const allWorkflows = eventTypeWorkflows;
// ... multiple operations on allWorkflows
const workflows = allWorkflows.filter((workflow) => {
  if (triggerEvents && !triggerEvents.includes(workflow.trigger)) return false;
});

✅ Filter early:

const allWorkflows = eventTypeWorkflows.filter((workflow) => {
  return !triggerEvents || triggerEvents.includes(workflow.trigger);
});

❌ Avoid nested loops when possible:

for (let i = 0; i < bookingsWithRemainingSeats.length; i++) {
  for (let j = i; j < busyTimes.length; j++) {
    // processing logic
  }
}

✅ Single iteration approach:

bookings.forEach(booking => {
  if (booking.attendeesCount < eventType.seatsPerTimeSlot) {
    // process booking directly
  }
});

⚠️ Exception - preserve data integrity:

// When index position matters, separate filter + reduce is correct
.filter((attendee) => typeof attendee.name === "string" && typeof attendee.email === "string")
.reduce((acc, attendee, index) => {
  acc[`Attendee ${index + 1}`] = `${attendee.name} (${attendee.email})`;
  return acc;
}, {})

This approach reduces time complexity from O(n²) to O(n) in many cases while maintaining code correctness.