Bamboohr Rate Limits

BambooHR Rate Limits

Overview

BambooHR does not publish exact rate limits, but the API returns 503 Service Unavailable with a Retry-After header when you exceed them. This skill covers detection, backoff, request optimization, and queue-based throttling.

Prerequisites

• BambooHR API client configured
• Understanding of async/await patterns

Instructions

Step 1: Understand BambooHR Rate Limiting Behavior

BambooHR rate limiting details:

Key insight: BambooHR uses 503 (not 429) for rate limiting. Failed authentication attempts also count toward rate limits, so ensure your API key is valid before making many requests.

Step 2: Implement Retry-After Aware Backoff

import { BambooHRApiError } from './client';

interface RetryConfig {
  maxRetries: number;
  baseDelayMs: number;
  maxDelayMs: number;
}

const DEFAULT_RETRY: RetryConfig = {
  maxRetries: 5,
  baseDelayMs: 1000,
  maxDelayMs: 60_000,
};

async function withBambooHRRetry<T>(
  operation: () => Promise<T>,
  config = DEFAULT_RETRY,
): Promise<T> {
  for (let attempt = 0; attempt <= config.maxRetries; attempt++) {
    try {
      return await operation();
    } catch (err) {
      if (attempt === config.maxRetries) throw err;
      if (!(err instanceof BambooHRApiError)) throw err;
      if (!err.retryable) throw err; // Only retry 429, 503, 500, 502

      // Honor BambooHR's Retry-After header
      let delay: number;
      if (err.meta.retryAfter) {
        delay = parseInt(err.meta.retryAfter, 10) * 1000;
      } else {
        // Exponential backoff with jitter
        const exponential = config.baseDelayMs * Math.pow(2, attempt);
        const jitter = Math.random() * config.baseDelayMs;
        delay = Math.min(exponential + jitter, config.maxDelayMs);
      }

      console.warn(
        `BambooHR rate limited (attempt ${attempt + 1}/${config.maxRetries}). ` +
        `Waiting ${(delay / 1000).toFixed(1)}s...`
      );
      await new Promise(r => setTimeout(r, delay));
    }
  }
  throw new Error('unreachable');
}

Step 3: Queue-Based Rate Limiting

import PQueue from 'p-queue';

// BambooHR unofficial guidance: stay under ~10 requests/second
const bamboohrQueue = new PQueue({
  concurrency: 3,         // Max 3 concurrent requests
  interval: 1000,         // Per 1-second window
  intervalCap: 8,         // Max 8 requests per second
});

async function rateLimitedRequest<T>(operation: () => Promise<T>): Promise<T> {
  return bamboohrQueue.add(() => withBambooHRRetry(operation));
}

// Usage — all requests go through the queue
const employees = await rateLimitedRequest(() => client.getDirectory());
const report = await rateLimitedRequest(() => client.customReport(['firstName', 'lastName']));

// Bulk operations automatically throttled
const employeeDetails = await Promise.all(
  employeeIds.map(id =>
    rateLimitedRequest(() => client.getEmployee(id, ['firstName', 'lastName', 'jobTitle']))
  ),
);

Step 4: Reduce Request Volume

Use custom reports instead of individual GETs:

// BAD: N+1 requests (one per employee)
const employees = await client.getDirectory();
for (const emp of employees.employees) {
  const detail = await client.getEmployee(emp.id, ['salary', 'department']);
  // 500 employees = 501 requests
}

// GOOD: 1 request using custom report
const report = await client.customReport([
  'firstName', 'lastName', 'department', 'jobTitle', 'hireDate',
]);
// 1 request, all employee data

Use incremental sync:

// BAD: Full directory pull every time
const allEmployees = await client.getDirectory();

// GOOD: Only changed employees since last sync
const changed = await client.request<any>(
  'GET', `/employees/changed/?since=${lastSyncTimestamp}`,
);
// Only fetch details for employees that actually changed

Use table changed endpoint:

// GET /employees/changed/tables/{tableName}?since=...
const changedJobs = await client.request<any>(
  'GET', `/employees/changed/tables/jobInfo?since=${lastSyncTimestamp}`,
);

Step 5: Monitor Rate Limit Usage

class BambooHRRateLimitMonitor {
  private requestLog: { timestamp: number; status: number }[] = [];
  private rateLimitHits = 0;

  recordRequest(status: number) {
    this.requestLog.push({ timestamp: Date.now(), status });

    // Only keep last 5 minutes
    const cutoff = Date.now() - 5 * 60 * 1000;
    this.requestLog = this.requestLog.filter(r => r.timestamp > cutoff);

    if (status === 503 || status === 429) {
      this.rateLimitHits++;
    }
  }

  getStats() {
    const recent = this.requestLog;
    return {
      requestsLast5Min: recent.length,
      requestsPerSecond: (recent.length / 300).toFixed(2),
      rateLimitHits: this.rateLimitHits,
      errorRate: recent.filter(r => r.status >= 400).length / Math.max(recent.length, 1),
    };
  }

  shouldBackOff(): boolean {
    const stats = this.getStats();
    return stats.errorRate > 0.1 || parseFloat(stats.requestsPerSecond) > 8;
  }
}

Output

• Retry logic honoring Retry-After header
• Queue-based throttling preventing rate limit hits
• Request volume reduction via custom reports and incremental sync
• Rate limit monitoring with stats

Error Handling

Enterprise Considerations

• Multi-tenant rate limits: Each company domain has independent rate limits
• Batch jobs: Run large syncs during off-peak hours (nights/weekends)
• Contact BambooHR: For enterprise-volume needs, request rate limit increases through support
• Webhook alternatives: Use webhooks for real-time changes instead of polling (see bamboohr-webhooks-events)

Resources

• BambooHR API Technical Overview
• p-queue Documentation

Next Steps

For security configuration, see bamboohr-security-basics.

Source: jeremylongshore/claude-code-plugins-plus-skills → plugins/saas-packs/bamboohr-pack/skills/bamboohr-rate-limits/SKILL.md