Flexport Performance Tuning

Flexport Performance Tuning

Overview

Optimize Flexport API integration performance. The API is rate-limited and serves logistics data that changes infrequently (shipments update hourly, products rarely). Cache aggressively for reads, batch writes, and use maximum page sizes.

Prerequisites

• A redacted baseline for latency, error rate, queue age, and budget/rate use; define freshness requirements per data class.
• Synthetic staging data, bounded concurrency, cache invalidation rules, and a rollback switch.

Output

Publish a performance receipt with baseline/post-change aggregate metrics, cache/concurrency settings, freshness rule, policy owner, and rollback result. Do not put logistics payloads or credentials into telemetry.

Error Handling

• Reduce concurrency and honor throttle responses rather than increasing retries during a limit breach.
• Invalidate or disable a cache when access, freshness, or data-isolation controls cannot be demonstrated.
• Quarantine unexpected results for review and revert the canary before a broad rollout.

Examples

Replay a fictional shipment list in staging at low concurrency, enable a bounded cache, and compare only aggregate latency and cache-hit metrics. Simulate a permission change to ensure the cache does not serve data to an unauthorized consumer; revert if it does.

Instructions

Step 1: Maximize Page Size

// Default per=25. Use per=100 (max) to reduce API calls by 4x
async function fetchAllShipments(): Promise<Shipment[]> {
  const all: Shipment[] = [];
  let page = 1;
  while (true) {
    const res = await flexport(`/shipments?per=100&page=${page}`);
    all.push(...res.data.records);
    if (res.data.records.length < 100) break;
    page++;
  }
  return all;
  // 1000 shipments = 10 API calls instead of 40
}

Step 2: Cache Responses

import { LRUCache } from 'lru-cache';

const cache = new LRUCache<string, any>({
  max: 500,
  ttl: 5 * 60 * 1000,  // 5 min for shipment data
});

// Products change rarely — cache longer
const productCache = new LRUCache<string, any>({
  max: 1000,
  ttl: 60 * 60 * 1000,  // 1 hour
});

async function cachedFlexport(path: string, ttlCache = cache): Promise<any> {
  const cached = ttlCache.get(path);
  if (cached) return cached;
  const data = await flexport(path);
  ttlCache.set(path, data);
  return data;
}

Step 3: Parallel Requests with Concurrency Control

import PQueue from 'p-queue';

const queue = new PQueue({ concurrency: 5, interval: 1000, intervalCap: 10 });

// Fetch details for multiple shipments in parallel
async function enrichShipments(ids: string[]) {
  return Promise.all(
    ids.map(id => queue.add(() => flexport(`/shipments/${id}`)))
  );
}

Step 4: Webhook-Driven Cache Invalidation

// Instead of polling, invalidate cache on webhook events
async function handleWebhook(event: any) {
  if (event.type.startsWith('shipment.')) {
    cache.delete(`/shipments/${event.data.shipment_id}`);
    cache.delete('/shipments');  // Invalidate list cache
  }
  if (event.type.startsWith('product.')) {
    productCache.delete(`/products/${event.data.product_id}`);
  }
}

Performance Targets

Resources

• Flexport API Reference
• lru-cache
• p-queue

Next Steps

For cost optimization, see flexport-cost-tuning.