Navan performance tuning

Navan Performance Tuning

Overview

Navan's REST API has no bulk endpoints or GraphQL — every data fetch is a separate HTTP request. High-volume integrations syncing thousands of bookings, expenses, or user records quickly become bottlenecked by sequential API calls, redundant fetches, and naive pagination. This skill provides concrete optimization patterns: response caching with data-type-specific TTLs, parallel request execution with concurrency controls, cursor-based pagination handling, and HTTP connection reuse. Each pattern targets the real constraint: minimizing total API calls while staying under rate limits.

Prerequisites

• Active Navan integration with OAuth 2.0 credentials (client_credentials grant)
• Node.js 18+ (for native fetch and AbortController)
• Understanding of your data volume — bookings/day, users, expense reports/month
• API base URL: https://api.navan.com/v1

Instructions

Step 1 — Implement Response Caching with Data-Appropriate TTLs

Different Navan data types change at different rates. Cache accordingly:

interface CacheEntry<T> {
  data: T;
  expires_at: number;
  etag?: string;
}

// TTLs based on data volatility
const CACHE_TTL: Record<string, number> = {
  'users':    3600_000,   // 1 hour — user profiles rarely change
  'policies': 86400_000,  // 24 hours — travel policies change infrequently
  'bookings': 300_000,    // 5 minutes — bookings update frequently
  'expenses': 600_000,    // 10 minutes — expenses change during approval flow
};

const cache = new Map<string, CacheEntry<unknown>>();

async function cachedFetch<T>(
  endpoint: string,
  token: string
): Promise<T> {
  const cacheKey = endpoint;
  const entry = cache.get(cacheKey) as CacheEntry<T> | undefined;

  // Return cached data if still valid
  if (entry && entry.expires_at > Date.now()) {
    return entry.data;
  }

  // Determine TTL from endpoint path
  const dataType = endpoint.split('?')[0].split('/')[0];
  const ttl = CACHE_TTL[dataType] ?? 300_000; // Default 5 minutes

  const response = await fetch(`https://api.navan.com/v1/${endpoint}`, {
    headers: {
      'Authorization': `Bearer ${token}`,
      'Content-Type': 'application/json',
    },
  });

  if (!response.ok) {
    throw new Error(`Navan API ${response.status}: ${endpoint}`);
  }

  const data = await response.json() as T;
  cache.set(cacheKey, {
    data,
    expires_at: Date.now() + ttl,
    etag: response.headers.get('etag') ?? undefined,
  });

  return data;
}

Step 2 — Parallel Fetch with Concurrency Throttling

Fetch multiple resources concurrently without overwhelming rate limits:

async function parallelFetch<T>(
  endpoints: string[],
  token: string,
  concurrency: number = 5
): Promise<T[]> {
  const results: T[] = [];
  const queue = [...endpoints];

  async function worker(): Promise<void> {
    while (queue.length > 0) {
      const endpoint = queue.shift()!;
      try {
        const data = await cachedFetch<T>(endpoint, token);
        results.push(data);
      } catch (err) {
        const status = (err as Error).message.match(/(\d{3})/)?.[1];
        if (status === '429') {
          // Rate limited — put it back and pause
          queue.unshift(endpoint);
          await new Promise(r => setTimeout(r, 2000));
        } else {
          throw err;
        }
      }
    }
  }

  // Launch workers up to concurrency limit
  const workers = Array.from(
    { length: Math.min(concurrency, endpoints.length) },
    () => worker()
  );
  await Promise.all(workers);
  return results;
}

// Usage: fetch 20 user profiles concurrently (5 at a time)
const userIds = ['u_001', 'u_002', /* ... */ 'u_020'];
const profiles = await parallelFetch(
  userIds.map(id => `users/${id}`),
  token,
  5  // Max 5 concurrent requests
);

Step 3 — Efficient Cursor-Based Pagination

Page through large result sets without missing or duplicating records:

async function* paginateAll<T>(
  endpoint: string,
  token: string,
  pageSize: number = 50
): AsyncGenerator<T[]> {
  let page = 0;

  while (true) {
    const params = new URLSearchParams({
      page: String(page),
      size: String(pageSize),
    });

    const url = `https://api.navan.com/v1/${endpoint}?${params}`;
    const response = await fetch(url, {
      headers: { 'Authorization': `Bearer ${token}` },
    });

    if (!response.ok) {
      throw new Error(`Navan API ${response.status} on ${endpoint}`);
    }

    const body = await response.json();
    const items: T[] = body.data ?? [];

    if (items.length === 0) break;
    yield items;

    if (items.length < pageSize) break; // Last page
    page++;
  }
}

// Usage: process all bookings in pages of 50
let totalProcessed = 0;
for await (const page of paginateAll('bookings', token, 50)) {
  await processBatch(page);
  totalProcessed += page.length;
  console.log(`Processed ${totalProcessed} bookings`);
}

Step 4 — HTTP Connection Reuse

Keep TCP connections alive across multiple API calls:

import { Agent } from 'undici';

// Create a connection pool for Navan API
const navanAgent = new Agent({
  keepAliveTimeout: 30_000,    // Keep idle connections for 30s
  keepAliveMaxTimeout: 60_000, // Max connection lifetime 60s
  connections: 10,             // Max 10 concurrent connections
  pipelining: 1,               // No HTTP pipelining (REST API)
});

// Use the agent for all Navan requests
const response = await fetch('https://api.navan.com/v1/bookings', {
  headers: { 'Authorization': `Bearer ${token}` },
  dispatcher: navanAgent,
});

Output

Optimized Navan API integration with:

• 60-80% fewer API calls through intelligent caching
• 5-10x faster sync jobs via parallel execution
• Zero missed records with robust cursor pagination
• Lower latency from connection reuse and keep-alive

Error Handling

HTTP Code	Meaning	Performance Action
200	Success	Cache the response with appropriate TTL
304	Not Modified	Use cached version (ETag match)
401	Token expired	Refresh token, retry once, do not cache
429	Rate limited	Exponential backoff: 1s, 2s, 4s — max 3 retries
500	Server error	Retry once after 5s, skip on second failure
503	Service unavailable	Pause all workers for 30s, then resume

Examples

Before and after optimization for a 10,000-booking sync:

Before (naive sequential):
  API calls: 10,000 (one per booking)
  Time: 45 minutes
  Rate limit hits: 12

After (cached + parallel + paginated):
  API calls: 200 (pages of 50)
  Time: 4 minutes
  Rate limit hits: 0

Cache invalidation on webhook event:

// When Navan sends a booking.updated webhook, invalidate that booking
function handleWebhook(event: { type: string; booking_id: string }) {
  if (event.type === 'booking.updated') {
    cache.delete(`bookings/${event.booking_id}`);
  }
}

Resources

• Navan Help Center — API documentation and rate limit details
• Navan Integrations — Data connectors (Fivetran, Airbyte) as alternatives to direct API
• undici Connection Pooling — Node.js HTTP client with pool management

Next Steps

• Add navan-rate-limits for detailed rate limit handling strategies
• Add navan-cost-tuning to optimize the business cost side alongside API performance
• See navan-observability to measure the impact of these optimizations