Bamboohr performance tuning

BambooHR Performance Tuning

Overview

Optimize BambooHR API performance through request reduction, caching, incremental sync, and connection pooling. The biggest wins come from eliminating N+1 query patterns using custom reports and the changed-since endpoint.

Prerequisites

• BambooHR API client configured
• Redis or in-memory cache available (optional)
• Performance monitoring in place

Instructions

Step 1: Eliminate N+1 Queries with Custom Reports

The single biggest performance improvement: use POST /reports/custom instead of individual employee GETs.

// BAD: 501 API calls for 500 employees
const dir = await client.getDirectory();                      // 1 call
for (const emp of dir.employees) {
  await client.getEmployee(emp.id, ['salary', 'hireDate']);   // 500 calls
}

// GOOD: 1 API call for all employees with all needed fields
const report = await client.customReport([
  'firstName', 'lastName', 'department', 'jobTitle',
  'hireDate', 'workEmail', 'status', 'location',
  'supervisor', 'employeeNumber',
]);
// 1 call, returns all employees with all fields

Performance impact: 500x reduction in API calls. Custom reports return all active employees in one request.

Step 2: Incremental Sync with Changed-Since

import { readFileSync, writeFileSync } from 'fs';

const LAST_SYNC_FILE = '.bamboohr-last-sync';

async function incrementalSync(client: BambooHRClient): Promise<string[]> {
  // Read last sync timestamp
  let lastSync: string;
  try {
    lastSync = readFileSync(LAST_SYNC_FILE, 'utf-8').trim();
  } catch {
    lastSync = new Date(Date.now() - 24 * 60 * 60 * 1000).toISOString(); // Default: 24h ago
  }

  // GET /employees/changed/?since=... — returns only changed employee IDs
  const changed = await client.request<{
    employees: Record<string, { id: string; lastChanged: string }>;
  }>('GET', `/employees/changed/?since=${lastSync}`);

  const changedIds = Object.keys(changed.employees || {});
  console.log(`${changedIds.length} employees changed since ${lastSync}`);

  if (changedIds.length === 0) return [];

  // Fetch only changed employees' details
  // For large sets, use custom report with filter; for small sets, individual GETs
  if (changedIds.length > 20) {
    // Bulk: use custom report (returns all, then filter client-side)
    const report = await client.customReport([
      'firstName', 'lastName', 'department', 'status',
    ]);
    const changedData = report.employees.filter(e =>
      changedIds.includes(e.id?.toString()),
    );
    // Process changedData...
  } else {
    // Small set: individual GETs are fine
    for (const id of changedIds) {
      const emp = await client.getEmployee(id, ['firstName', 'lastName', 'department', 'status']);
      // Process emp...
    }
  }

  // Save sync timestamp
  writeFileSync(LAST_SYNC_FILE, new Date().toISOString());
  return changedIds;
}

Also available for table data:

// GET /employees/changed/tables/{tableName}?since=...
const changedJobs = await client.request<any>(
  'GET', `/employees/changed/tables/jobInfo?since=${lastSync}`,
);
// Returns { employees: { "123": { lastChanged: "..." }, ... } }

Step 3: Response Caching

import { LRUCache } from 'lru-cache';

// BambooHR directory data changes infrequently — cache aggressively
const cache = new LRUCache<string, any>({
  max: 500,
  ttl: 5 * 60 * 1000, // 5 minutes for directory data
});

async function cachedRequest<T>(
  key: string,
  fetcher: () => Promise<T>,
  ttlMs?: number,
): Promise<T> {
  const cached = cache.get(key) as T | undefined;
  if (cached) {
    console.log(`Cache hit: ${key}`);
    return cached;
  }

  const result = await fetcher();
  cache.set(key, result, { ttl: ttlMs });
  return result;
}

// Usage
const directory = await cachedRequest(
  'directory',
  () => client.getDirectory(),
  5 * 60 * 1000, // Cache for 5 min
);

// Single employee — shorter cache
const employee = await cachedRequest(
  `employee:${id}`,
  () => client.getEmployee(id, fields),
  60 * 1000, // Cache for 1 min
);

Redis caching for multi-instance deployments:

import Redis from 'ioredis';

const redis = new Redis(process.env.REDIS_URL);

async function redisCached<T>(
  key: string,
  fetcher: () => Promise<T>,
  ttlSec = 300,
): Promise<T> {
  const cached = await redis.get(`bamboohr:${key}`);
  if (cached) return JSON.parse(cached);

  const result = await fetcher();
  await redis.setex(`bamboohr:${key}`, ttlSec, JSON.stringify(result));
  return result;
}

// Invalidate on webhook
async function invalidateCache(employeeId: string) {
  await redis.del(`bamboohr:employee:${employeeId}`);
  await redis.del('bamboohr:directory'); // Directory includes this employee
}

Step 4: Connection Pooling

import { Agent } from 'https';

// Reuse TCP connections for BambooHR API calls
const keepAliveAgent = new Agent({
  keepAlive: true,
  maxSockets: 5,        // Max 5 parallel connections
  maxFreeSockets: 2,
  timeout: 30_000,
  keepAliveMsecs: 10_000,
});

// Pass to fetch via undici or node-fetch
// For native fetch in Node 20+, connection pooling is automatic

Step 5: Request Batching with DataLoader

import DataLoader from 'dataloader';

// Batch individual employee GETs into a custom report
const employeeLoader = new DataLoader<string, Record<string, string>>(
  async (ids) => {
    // One custom report instead of N individual GETs
    const report = await client.customReport([
      'id', 'firstName', 'lastName', 'department', 'jobTitle',
    ]);

    const byId = new Map(report.employees.map(e => [e.id, e]));
    return ids.map(id => byId.get(id) || new Error(`Employee ${id} not found`));
  },
  {
    maxBatchSize: 100,
    batchScheduleFn: cb => setTimeout(cb, 50), // Batch window: 50ms
    cache: true,
  },
);

// Usage — automatically batched into one API call
const [emp1, emp2, emp3] = await Promise.all([
  employeeLoader.load('1'),
  employeeLoader.load('2'),
  employeeLoader.load('3'),
]);

Step 6: Performance Monitoring

class BambooHRMetrics {
  private requests: { duration: number; status: number; endpoint: string }[] = [];

  record(endpoint: string, status: number, durationMs: number) {
    this.requests.push({ duration: durationMs, status, endpoint });

    // Keep last 1000 requests
    if (this.requests.length > 1000) this.requests.shift();
  }

  summary() {
    const durations = this.requests.map(r => r.duration).sort((a, b) => a - b);
    const errors = this.requests.filter(r => r.status >= 400);

    return {
      totalRequests: this.requests.length,
      errorRate: (errors.length / Math.max(this.requests.length, 1) * 100).toFixed(1) + '%',
      p50: durations[Math.floor(durations.length * 0.5)] || 0,
      p95: durations[Math.floor(durations.length * 0.95)] || 0,
      p99: durations[Math.floor(durations.length * 0.99)] || 0,
      topEndpoints: this.topEndpoints(),
    };
  }

  private topEndpoints() {
    const counts = new Map<string, number>();
    for (const r of this.requests) {
      counts.set(r.endpoint, (counts.get(r.endpoint) || 0) + 1);
    }
    return [...counts.entries()].sort((a, b) => b[1] - a[1]).slice(0, 5);
  }
}

Output

• N+1 queries eliminated via custom reports (500x reduction)
• Incremental sync using changed-since endpoint
• Multi-tier caching (LRU in-memory + Redis)
• Connection pooling with keep-alive
• DataLoader-based request batching
• Performance metrics with p50/p95/p99

Performance Reference

Optimization	Before	After	Improvement
Custom reports vs N+1	501 calls	1 call	500x
Incremental sync	Full pull	Delta only	10-100x
Directory caching (5 min)	Every request	1/5 min	50x
Connection pooling	New conn/request	Reused	2-3x latency

Error Handling

Issue	Cause	Solution
Cache stampede	All caches expire simultaneously	Stagger TTLs with jitter
Stale data	Cache TTL too long	Invalidate on webhook events
DataLoader timeout	Custom report too slow	Reduce batch size
Memory pressure	LRU cache too large	Set max entries limit

Resources

• BambooHR API Technical Overview
• DataLoader Documentation
• LRU Cache Documentation

Next Steps

For cost optimization, see bamboohr-cost-tuning.