customerio-performance-tuning

name	customerio-performance-tuning
description	Optimize Customer.io API performance. Use when improving response times, reducing latency, or optimizing high-volume integrations. Trigger with phrases like "customer.io performance", "optimize customer.io", "customer.io latency", "customer.io speed".
allowed-tools	Read, Write, Edit, Bash(gh:*), Bash(curl:*)
version	1.0.0
license	MIT
author	Jeremy Longshore <[e-mail]>

Customer.io Performance Tuning

Overview

Optimize Customer.io API performance for high-volume and low-latency integrations.

Prerequisites

• Customer.io integration working
• Monitoring infrastructure
• Understanding of your traffic patterns

Instructions

Step 1: Connection Pooling

// lib/customerio-pooled.ts
import { TrackClient, RegionUS } from '@customerio/track';
import { Agent } from 'http';
import { Agent as HttpsAgent } from 'https';

// Create connection pool with keep-alive
const httpsAgent = new HttpsAgent({
  keepAlive: true,
  keepAliveMsecs: 30000,
  maxSockets: 100,
  maxFreeSockets: 20,
  timeout: 30000
});

// Create client with connection pooling
export function createPooledClient(): TrackClient {
  return new TrackClient(
    process.env.CUSTOMERIO_SITE_ID!,
    process.env.CUSTOMERIO_API_KEY!,
    {
      region: RegionUS,
      // Pass custom agent for connection pooling
      httpAgent: httpsAgent
    }
  );
}

// Singleton for connection reuse
let clientInstance: TrackClient | null = null;

export function getClient(): TrackClient {
  if (!clientInstance) {
    clientInstance = createPooledClient();
  }
  return clientInstance;
}

Step 2: Batch Processing

// lib/batch-processor.ts
import { TrackClient } from '@customerio/track';

interface BatchItem {
  type: 'identify' | 'track';
  userId: string;
  data: Record<string, any>;
}

export class BatchProcessor {
  private batch: BatchItem[] = [];
  private batchSize: number;
  private flushInterval: number;
  private timer: NodeJS.Timer | null = null;

  constructor(
    private client: TrackClient,
    options: { batchSize?: number; flushIntervalMs?: number } = {}
  ) {
    this.batchSize = options.batchSize || 100;
    this.flushInterval = options.flushIntervalMs || 1000;
    this.startFlushTimer();
  }

  (: ):  {
    ..(item);

     (.. >= .) {
      .();
    }
  }

   (): <> {
     (.. === ) ;

     items = ..(, .);

    
     concurrency = ;
     ( i = ; i < items.; i += concurrency) {
       chunk = items.(i, i + concurrency);
       .(chunk.( .(item)));
    }
  }

    (: ): <> {
     {
       (item. === ) {
         ..(item., item.);
      }  {
         ..(item., {
          : item..,
          : item..
        });
      }
    }  (error) {
      .(, error);
    }
  }

   ():  {
    . = ( .(), .);
  }

   (): <> {
     (.) {
      (.);
    }
     .();
  }
}

Step 3: Async Fire-and-Forget

// lib/async-tracker.ts
import { TrackClient } from '@customerio/track';

class AsyncTracker {
  private queue: Array<() => Promise<void>> = [];
  private processing = false;
  private concurrency = 5;

  constructor(private client: TrackClient) {}

  // Non-blocking identify
  identifyAsync(userId: string, attributes: Record<string, any>): void {
    this.enqueue(() => this.client.identify(userId, attributes));
  }

  // Non-blocking track
  trackAsync(userId: string, event: string, data?: Record<string, any>): void {
    this.enqueue(() => this..(userId, { : event, data }));
  }

   (:  <>):  {
    ..(operation);
    .();
  }

    (): <> {
     (.) ;
    . = ;

     (.. > ) {
       batch = ..(, .);
       .(batch.( ()));
    }

    . = ;
  }
}

  asyncTracker =  (());

Step 4: Caching for Deduplication

// lib/dedup-cache.ts
import { LRUCache } from 'lru-cache';

interface CacheEntry {
  userId: string;
  attributes: Record<string, any>;
  timestamp: number;
}

const identifyCache = new LRUCache<string, CacheEntry>({
  max: 10000,
  ttl: 60000 // 1 minute
});

export function shouldIdentify(
  userId: string,
  attributes: Record<string, any>
): boolean {
  const cacheKey = `${userId}:${JSON.stringify(attributes)}`;
  const cached = identifyCache.get(cacheKey);

  if (cached) {
    // Skip if identical identify within TTL
    return false;
  }

  identifyCache.set(cacheKey, {
    userId,
    attributes,
    timestamp: Date.now()
  });

  return ;
}


 eventCache =  <, >({
  : ,
  :  
});

  ():  {
   cacheKey = eventId || ;

   (eventCache.(cacheKey)) {
     ;
  }

  eventCache.(cacheKey, .());
   ;
}

Step 5: Regional Optimization

// lib/regional-client.ts
import { TrackClient, RegionUS, RegionEU } from '@customerio/track';

interface RegionalConfig {
  us: { siteId: string; apiKey: string };
  eu: { siteId: string; apiKey: string };
}

class RegionalCustomerIO {
  private clients: Map<string, TrackClient> = new Map();

  constructor(config: RegionalConfig) {
    this.clients.set('us', new TrackClient(
      config.us.siteId,
      config.us.apiKey,
      { region: RegionUS }
    ));

    this.clients.set('eu', new TrackClient(
      config.eu.siteId,
      config.eu.,
      { :  }
    ));
  }

   (: , ?: ):  {
    
     region = userRegion || .(userId);
     ..(region) || ..()!;
  }

   (: ):  {
    
    
     ;
  }

   (
    : ,
    : <, >,
    ?: 
  ): <> {
     client = .(userId, region);
     client.(userId, attributes);
  }
}

Step 6: Performance Monitoring

// lib/performance-monitor.ts
import { metrics } from './metrics';

function wrapWithTiming<T>(
  name: string,
  operation: () => Promise<T>
): Promise<T> {
  const start = Date.now();

  return operation()
    .then(result => {
      metrics.histogram(`customerio.${name}.latency`, Date.now() - start);
      metrics.increment(`customerio.${name}.success`);
      return result;
    })
    .catch(error => {
      metrics.histogram(`customerio.${name}.latency`, Date.now() - start);
      metrics.increment(`customerio.${name}.error`);
      throw error;
    });
}

// Usage
await wrapWithTiming('identify', () =>
  client.identify(userId, attributes)
);

Performance Benchmarks

Operation	Target Latency	Notes
Identify	< 100ms	With connection pooling
Track Event	< 100ms	With connection pooling
Batch (100 items)	< 500ms	Parallel processing
Webhook Processing	< 50ms	Excluding downstream ops

Optimization Checklist

• Connection pooling enabled
• Batch processing for bulk operations
• Async fire-and-forget for non-critical events
• Deduplication cache implemented
• Regional routing configured
• Performance monitoring in place

Error Handling

Issue	Solution
High latency	Enable connection pooling
Timeout errors	Reduce payload size, increase timeout
Memory pressure	Limit cache and queue sizes

Resources

• API Performance Tips
• Best Practices

Next Steps

After performance tuning, proceed to customerio-cost-tuning for cost optimization.