jeremylongshore

customerio-load-scale

@jeremylongshore/customerio-load-scale
jeremylongshore
1,004
123 forks
Updated 1/18/2026
View on GitHub

Implement Customer.io load testing and scaling. Use when preparing for high traffic, load testing, or scaling integrations for enterprise workloads. Trigger with phrases like "customer.io load test", "customer.io scale", "customer.io high volume", "customer.io performance test".

Installation

$skills install @jeremylongshore/customerio-load-scale
Claude Code
Cursor
Copilot
Codex
Antigravity

Details

Pathplugins/saas-packs/customerio-pack/skills/customerio-load-scale/SKILL.md
Branchmain
Scoped Name@jeremylongshore/customerio-load-scale

Usage

After installing, this skill will be available to your AI coding assistant.

Verify installation:

skills list

Skill Instructions


name: customerio-load-scale description: | Implement Customer.io load testing and scaling. Use when preparing for high traffic, load testing, or scaling integrations for enterprise workloads. Trigger with phrases like "customer.io load test", "customer.io scale", "customer.io high volume", "customer.io performance test". allowed-tools: Read, Write, Edit, Bash(kubectl:), Bash(curl:) version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io

Customer.io Load & Scale

Overview

Load testing and scaling strategies for high-volume Customer.io integrations.

Prerequisites

  • Customer.io integration working
  • Load testing tools (k6, Artillery)
  • Staging environment with test workspace

Capacity Planning

Customer.io Rate Limits

EndpointLimitNotes
Track API (identify/track)100 req/secPer workspace
App API (transactional)100 req/secPer workspace
Webhooks (outbound)VariesBased on plan

Scaling Targets

VolumeArchitectureNotes
< 1M events/daySingle serviceDirect API calls
1-10M events/dayQueue-basedMessage queue buffer
> 10M events/dayDistributedMultiple workers

Instructions

Step 1: Load Test Script (k6)

// load-tests/customerio.js
import http from 'k6/http';
import { check, sleep } from 'k6';
import { Rate, Trend } from 'k6/metrics';

const errorRate = new Rate('errors');
const identifyDuration = new Trend('identify_duration');
const trackDuration = new Trend('track_duration');

const BASE_URL = 'https://track.customer.io/api/v1';
const AUTH = __ENV.CUSTOMERIO_AUTH; // base64(site_id:api_key)

export const options = {
  scenarios: {
    identify_load: {
      executor: 'ramping-rate',
      startRate: 10,
      timeUnit: '1s',
      preAllocatedVUs: 50,
      stages: [
        { target: 50, duration: '1m' },
        { target: 100, duration: '2m' },
        { target: 100, duration: '5m' },
        { target: 0, duration: '1m' },
      ],
      exec: 'identifyScenario',
    },
    track_load: {
      executor: 'ramping-rate',
      startRate: 10,
      timeUnit: '1s',
      preAllocatedVUs: 50,
      stages: [
        { target: 50, duration: '1m' },
        { target: 100, duration: '2m' },
        { target: 100, duration: '5m' },
        { target: 0, duration: '1m' },
      ],
      exec: 'trackScenario',
    },
  },
  thresholds: {
    'errors': ['rate<0.01'],
    'identify_duration': ['p95<500'],
    'track_duration': ['p95<500'],
  },
};

export function identifyScenario() {
  const userId = `load-test-${__VU}-${__ITER}`;
  const payload = JSON.stringify({
    email: `${userId}@loadtest.com`,
    _load_test: true,
    created_at: Math.floor(Date.now() / 1000),
  });

  const start = new Date();
  const res = http.post(
    `${BASE_URL}/customers/${userId}`,
    payload,
    {
      headers: {
        'Authorization': `Basic ${AUTH}`,
        'Content-Type': 'application/json',
      },
    }
  );
  identifyDuration.add(new Date() - start);

  const success = check(res, {
    'identify status is 200': (r) => r.status === 200,
  });
  errorRate.add(!success);

  sleep(0.1);
}

export function trackScenario() {
  const userId = `load-test-${__VU}-${__ITER}`;
  const payload = JSON.stringify({
    name: 'load_test_event',
    data: {
      source: 'k6',
      timestamp: new Date().toISOString(),
    },
  });

  const start = new Date();
  const res = http.post(
    `${BASE_URL}/customers/${userId}/events`,
    payload,
    {
      headers: {
        'Authorization': `Basic ${AUTH}`,
        'Content-Type': 'application/json',
      },
    }
  );
  trackDuration.add(new Date() - start);

  const success = check(res, {
    'track status is 200': (r) => r.status === 200,
  });
  errorRate.add(!success);

  sleep(0.1);
}

Step 2: Horizontal Scaling

# k8s/scaled-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: customerio-worker
spec:
  replicas: 3
  selector:
    matchLabels:
      app: customerio-worker
  template:
    metadata:
      labels:
        app: customerio-worker
    spec:
      containers:
        - name: worker
          image: customerio-worker:latest
          resources:
            requests:
              cpu: "500m"
              memory: "256Mi"
            limits:
              cpu: "1000m"
              memory: "512Mi"
          env:
            - name: CONCURRENCY
              value: "10"
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: customerio-worker-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: customerio-worker
  minReplicas: 3
  maxReplicas: 20
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70
    - type: External
      external:
        metric:
          name: pubsub.googleapis.com|subscription|num_undelivered_messages
          selector:
            matchLabels:
              resource.labels.subscription_id: customerio-events
        target:
          type: AverageValue
          averageValue: 1000

Step 3: Message Queue Architecture

// lib/scaled-processor.ts
import { Kafka, Consumer, EachMessagePayload } from 'kafkajs';
import { TrackClient, RegionUS } from '@customerio/track';

const kafka = new Kafka({
  clientId: 'customerio-worker',
  brokers: process.env.KAFKA_BROKERS!.split(',')
});

const consumer = kafka.consumer({
  groupId: 'customerio-workers',
  sessionTimeout: 30000,
  heartbeatInterval: 3000
});

const client = new TrackClient(
  process.env.CUSTOMERIO_SITE_ID!,
  process.env.CUSTOMERIO_API_KEY!,
  { region: RegionUS }
);

interface CustomerIOEvent {
  type: 'identify' | 'track';
  userId: string;
  payload: any;
}

async function processMessage(message: EachMessagePayload): Promise<void> {
  const event: CustomerIOEvent = JSON.parse(message.message.value!.toString());

  if (event.type === 'identify') {
    await client.identify(event.userId, event.payload);
  } else if (event.type === 'track') {
    await client.track(event.userId, {
      name: event.payload.event,
      data: event.payload.properties
    });
  }
}

async function start(): Promise<void> {
  await consumer.connect();
  await consumer.subscribe({ topic: 'customerio-events', fromBeginning: false });

  await consumer.run({
    partitionsConsumedConcurrently: 10,
    eachMessage: async (payload) => {
      try {
        await processMessage(payload);
      } catch (error) {
        console.error('Processing error:', error);
        // Dead letter or retry logic
      }
    }
  });
}

start().catch(console.error);

Step 4: Rate Limiter for Fair Usage

// lib/rate-limiter.ts
import Bottleneck from 'bottleneck';

// Respect Customer.io's 100 req/sec limit
// Leave headroom for other services
const limiter = new Bottleneck({
  reservoir: 80, // 80 tokens
  reservoirRefreshAmount: 80,
  reservoirRefreshInterval: 1000, // per second
  maxConcurrent: 20,
  minTime: 10 // Minimum 10ms between requests
});

// Track rate limit events
limiter.on('depleted', () => {
  console.warn('Rate limiter depleted, requests queued');
});

limiter.on('error', (error) => {
  console.error('Rate limiter error:', error);
});

export async function rateLimitedIdentify(
  client: TrackClient,
  userId: string,
  attributes: Record<string, any>
): Promise<void> {
  return limiter.schedule(() => client.identify(userId, attributes));
}

export async function rateLimitedTrack(
  client: TrackClient,
  userId: string,
  event: string,
  data?: Record<string, any>
): Promise<void> {
  return limiter.schedule(() =>
    client.track(userId, { name: event, data })
  );
}

// Get limiter stats
export function getLimiterStats() {
  return {
    running: limiter.running(),
    queued: limiter.queued(),
    done: limiter.done(),
    reservoir: limiter.reservoir
  };
}

Step 5: Batch Processing

// lib/batch-sender.ts
interface BatchConfig {
  maxBatchSize: number;
  maxWaitMs: number;
  concurrency: number;
}

class BatchSender {
  private batch: Array<{ userId: string; operation: 'identify' | 'track'; data: any }> = [];
  private timer: NodeJS.Timer | null = null;
  private processing = false;

  constructor(
    private client: TrackClient,
    private config: BatchConfig = { maxBatchSize: 100, maxWaitMs: 1000, concurrency: 10 }
  ) {}

  add(userId: string, operation: 'identify' | 'track', data: any): void {
    this.batch.push({ userId, operation, data });

    if (this.batch.length >= this.config.maxBatchSize) {
      this.flush();
    } else if (!this.timer) {
      this.timer = setTimeout(() => this.flush(), this.config.maxWaitMs);
    }
  }

  async flush(): Promise<void> {
    if (this.processing || this.batch.length === 0) return;

    if (this.timer) {
      clearTimeout(this.timer);
      this.timer = null;
    }

    this.processing = true;
    const items = this.batch.splice(0, this.config.maxBatchSize);

    // Process in parallel with limited concurrency
    for (let i = 0; i < items.length; i += this.config.concurrency) {
      const chunk = items.slice(i, i + this.config.concurrency);
      await Promise.allSettled(chunk.map(item => this.processItem(item)));
    }

    this.processing = false;
  }

  private async processItem(item: { userId: string; operation: string; data: any }): Promise<void> {
    if (item.operation === 'identify') {
      await this.client.identify(item.userId, item.data);
    } else {
      await this.client.track(item.userId, {
        name: item.data.event,
        data: item.data.properties
      });
    }
  }
}

Step 6: Load Test Execution

#!/bin/bash
# scripts/run-load-test.sh

# Set credentials
export CUSTOMERIO_AUTH=$(echo -n "$CIO_SITE_ID:$CIO_API_KEY" | base64)

# Run k6 load test
k6 run \
  --out json=results.json \
  --out influxdb=http://localhost:8086/k6 \
  load-tests/customerio.js

# Generate report
k6 run --summary-export=summary.json load-tests/customerio.js

echo "Load test complete. Results in results.json"

Scaling Checklist

  • Rate limits understood
  • Load tests written
  • Horizontal scaling configured
  • Message queue buffering
  • Rate limiting implemented
  • Batch processing enabled
  • Monitoring during tests

Error Handling

IssueSolution
Rate limited (429)Reduce concurrency
Timeout errorsIncrease timeout
Queue backlogScale workers

Resources

Next Steps

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

More by jeremylongshore

View all
rabbitmq-queue-setup
1,004

Rabbitmq Queue Setup - Auto-activating skill for Backend Development. Triggers on: rabbitmq queue setup, rabbitmq queue setup Part of the Backend Development skill category.

model-evaluation-suite
1,004

evaluating-machine-learning-models: This skill allows Claude to evaluate machine learning models using a comprehensive suite of metrics. It should be used when the user requests model performance analysis, validation, or testing. Claude can use this skill to assess model accuracy, precision, recall, F1-score, and other relevant metrics. Trigger this skill when the user mentions "evaluate model", "model performance", "testing metrics", "validation results", or requests a comprehensive "model evaluation".

neural-network-builder
1,004

building-neural-networks: This skill allows Claude to construct and configure neural network architectures using the neural-network-builder plugin. It should be used when the user requests the creation of a new neural network, modification of an existing one, or assistance with defining the layers, parameters, and training process. The skill is triggered by requests involving terms like "build a neural network," "define network architecture," "configure layers," or specific mentions of neural network types (e.g., "CNN," "RNN," "transformer").

oauth-callback-handler
1,004

Oauth Callback Handler - Auto-activating skill for API Integration. Triggers on: oauth callback handler, oauth callback handler Part of the API Integration skill category.