Customer.io Load & Scale

Overview

Load testing and scaling strategies for high-volume Customer.io integrations: k6 load test scripts, scaling architecture selection based on volume tier, Kubernetes HPA autoscaling, message queue buffering, and rate-limit-aware batch processing.

Scaling Architecture by Volume

Daily Events	Architecture	Key Components
< 100K	Direct API	Singleton client, retry, connection pooling
100K - 1M	Batched API	Event queue, batch processor, rate limiter
1M - 10M	Queue-backed	Redis/Kafka queue, worker pool, backpressure
> 10M	Distributed	Multiple workspaces, sharded queues, regional routing

Customer.io rate limit is ~100 req/sec per workspace. Plan your architecture around this.

Instructions

Step 1: k6 Load Test Script

// load-tests/customerio.js
// Run: k6 run --vus 10 --duration 60s load-tests/customerio.js
import http from "k6/http";
import { check, sleep } from "k6";
import { Counter, Trend } from "k6/metrics";

const SITE_ID = __ENV.CUSTOMERIO_SITE_ID;
const API_KEY = __ENV.CUSTOMERIO_TRACK_API_KEY;
const BASE_URL = "https://track.customer.io/api/v1";
const AUTH = `${SITE_ID}:${API_KEY}`;

const identifyLatency = new Trend("cio_identify_latency");
const trackLatency = new Trend("cio_track_latency");
const errors = new Counter("cio_errors");

export const options = {
  scenarios: {
    identify_load: {
      executor: "ramping-arrival-rate",
      startRate: 10,
      timeUnit: "1s",
      preAllocatedVUs: 20,
      maxVUs: 50,
      stages: [
        { duration: "30s", target: 50 },   // Ramp to 50/sec
        { duration: "60s", target: 80 },   // Hold at 80/sec (near limit)
        { duration: "30s", target: 10 },   // Cool down
      ],
    },
  },
  thresholds: {
    cio_identify_latency: ["p(95)<500", "p(99)<2000"],
    cio_track_latency: ["p(95)<500", "p(99)<2000"],
    cio_errors: ["count<50"],
  },
};

export default function () {
  const userId = `k6-load-${__VU}-${__ITER}`;
  const headers = {
    "Content-Type": "application/json",
    Authorization: `Basic ${encoding.b64encode(AUTH)}`,
  };

  // Identify
  const identifyRes = http.put(
    `${BASE_URL}/customers/${userId}`,
    JSON.stringify({
      email: `${userId}@loadtest.example.com`,
      _load_test: true,
      created_at: Math.floor(Date.now() / 1000),
    }),
    { headers }
  );

  identifyLatency.add(identifyRes.timings.duration);
  check(identifyRes, { "identify 200": (r) => r.status === 200 }) || errors.add(1);

  // Track event
  const trackRes = http.post(
    `${BASE_URL}/customers/${userId}/events`,
    JSON.stringify({
      name: "load_test_event",
      data: { iteration: __ITER, vu: __VU },
    }),
    { headers }
  );

  trackLatency.add(trackRes.timings.duration);
  check(trackRes, { "track 200": (r) => r.status === 200 }) || errors.add(1);

  sleep(0.1); // Small delay between iterations
}

// Cleanup function — suppress test users after test
export function teardown() {
  console.log("Load test complete. Clean up k6-load-* users in CIO dashboard.");
}

Run:

k6 run --env CUSTOMERIO_SITE_ID="$CUSTOMERIO_SITE_ID" \
       --env CUSTOMERIO_TRACK_API_KEY="$CUSTOMERIO_TRACK_API_KEY" \
       load-tests/customerio.js

Step 2: Queue-Based Architecture

// services/cio-queue-worker.ts
import { Queue, Worker, QueueEvents } from "bullmq";
import { TrackClient, RegionUS } from "customerio-node";
import Bottleneck from "bottleneck";

const REDIS_URL = process.env.REDIS_URL ?? "redis://localhost:6379";

// Rate limiter: 80 requests per second (leave headroom under 100/sec limit)
const limiter = new Bottleneck({
  maxConcurrent: 15,
  reservoir: 80,
  reservoirRefreshAmount: 80,
  reservoirRefreshInterval: 1000,
});

const eventQueue = new Queue("cio:events", {
  connection: { url: REDIS_URL },
  defaultJobOptions: {
    attempts: 5,
    backoff: { type: "exponential", delay: 2000 },
    removeOnComplete: { count: 10000 },
    removeOnFail: { count: 50000 },
  },
});

// Producer — your application enqueues events here
export async function enqueueEvent(
  type: "identify" | "track",
  userId: string,
  data: Record<string, any>
): Promise<void> {
  await eventQueue.add(type, { userId, data, enqueuedAt: Date.now() });
}

// Consumer — workers process events with rate limiting
export function startEventWorkers(concurrency = 10): void {
  const cio = new TrackClient(
    process.env.CUSTOMERIO_SITE_ID!,
    process.env.CUSTOMERIO_TRACK_API_KEY!,
    { region: RegionUS }
  );

  const worker = new Worker(
    "cio:events",
    async (job) => {
      await limiter.schedule(async () => {
        if (job.name === "identify") {
          await cio.identify(job.data.userId, job.data.data);
        } else {
          await cio.track(job.data.userId, job.data.data);
        }
      });
    },
    {
      connection: { url: REDIS_URL },
      concurrency,
    }
  );

  worker.on("failed", (job, err) => {
    console.error(`CIO event failed: ${job?.id} — ${err.message}`);
  });

  // Monitor queue health
  const events = new QueueEvents("cio:events", {
    connection: { url: REDIS_URL },
  });

  setInterval(async () => {
    const counts = await eventQueue.getJobCounts();
    console.log(
      `CIO queue: waiting=${counts.waiting} active=${counts.active} ` +
      `failed=${counts.failed} completed=${counts.completed}`
    );
  }, 30000);
}

Step 3: Kubernetes HPA Autoscaling

# k8s/hpa.yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: cio-worker-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: cio-event-worker
  minReplicas: 2
  maxReplicas: 20
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70
    - type: Pods
      pods:
        metric:
          name: cio_queue_depth
        target:
          type: AverageValue
          averageValue: "500"
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 60
      policies:
        - type: Pods
          value: 4
          periodSeconds: 60
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
        - type: Pods
          value: 2
          periodSeconds: 120

Step 4: Batch Sender for Bulk Operations

// lib/cio-batch-sender.ts
import { TrackClient, RegionUS } from "customerio-node";
import Bottleneck from "bottleneck";

export async function batchSend(
  operations: Array<{
    type: "identify" | "track";
    userId: string;
    data: Record<string, any>;
  }>,
  ratePerSec = 80
): Promise<{ succeeded: number; failed: number }> {
  const cio = new TrackClient(
    process.env.CUSTOMERIO_SITE_ID!,
    process.env.CUSTOMERIO_TRACK_API_KEY!,
    { region: RegionUS }
  );

  const limiter = new Bottleneck({
    maxConcurrent: 15,
    reservoir: ratePerSec,
    reservoirRefreshAmount: ratePerSec,
    reservoirRefreshInterval: 1000,
  });

  let succeeded = 0;
  let failed = 0;

  const promises = operations.map((op, i) =>
    limiter.schedule(async () => {
      try {
        if (op.type === "identify") {
          await cio.identify(op.userId, op.data);
        } else {
          await cio.track(op.userId, op.data);
        }
        succeeded++;
      } catch {
        failed++;
      }
      if ((succeeded + failed) % 1000 === 0) {
        console.log(`Progress: ${succeeded + failed}/${operations.length}`);
      }
    })
  );

  await Promise.all(promises);
  return { succeeded, failed };
}

Install: npm install bottleneck bullmq

Load Test Checklist

• Test against staging workspace (NEVER production)
• Start at 10% of target rate, ramp up gradually
• Monitor 429 error rate during test
• Check Customer.io dashboard for processing lag
• Verify cleanup of test users after load test
• Document baseline latency and throughput numbers
• Set up alerts before running at production scale

Error Handling

Issue	Solution
429 during load test	Reduce rate, check limiter config
Queue backlog growing	Scale workers, increase concurrency
Memory pressure	Limit batch and queue sizes, enable GC
k6 VU exhaustion	Increase preAllocatedVUs and maxVUs

Resources

• k6 Documentation
• Bottleneck npm
• BullMQ Documentation
• Kubernetes HPA

Next Steps

After load testing, proceed to customerio-known-pitfalls for anti-patterns to avoid.