Skills Development Rate Limiting and Backoff For APIs

Rate Limiting and Backoff For APIs

v20260423
customerio-rate-limits
Implements robust mechanisms for handling API rate limits (e.g., 429 errors) when interacting with services like Customer.io. It utilizes the Token Bucket algorithm for precise throttling and combines it with exponential backoff, jitter, and queue processing (p-queue) to ensure high reliability and prevent API overloads.
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Overview

Customer.io Rate Limits

Overview

Understand Customer.io's API rate limits and implement proper throttling: token bucket limiters, exponential backoff with jitter, queue-based processing, and 429 response handling.

Rate Limit Reference

API Endpoint Limit Scope
Track API identify, track, trackAnonymous ~100 req/sec Per workspace
Track API Batch operations ~100 req/sec Per workspace
App API Transactional email/push ~100 req/sec Per workspace
App API Broadcasts, queries ~10 req/sec Per workspace

These are approximate. Customer.io uses sliding window rate limiting. When exceeded, you get a 429 Too Many Requests response.

Instructions

Step 1: Token Bucket Rate Limiter

// lib/rate-limiter.ts
export class TokenBucket {
  private tokens: number;
  private lastRefill: number;

  constructor(
    private readonly maxTokens: number = 80,  // Stay under 100/sec limit
    private readonly refillRate: number = 80   // Tokens per second
  ) {
    this.tokens = maxTokens;
    this.lastRefill = Date.now();
  }

  private refill(): void {
    const now = Date.now();
    const elapsed = (now - this.lastRefill) / 1000;
    this.tokens = Math.min(this.maxTokens, this.tokens + elapsed * this.refillRate);
    this.lastRefill = now;
  }

  async acquire(): Promise<void> {
    this.refill();
    if (this.tokens >= 1) {
      this.tokens -= 1;
      return;
    }
    // Wait until a token is available
    const waitMs = ((1 - this.tokens) / this.refillRate) * 1000;
    await new Promise((r) => setTimeout(r, Math.ceil(waitMs)));
    this.tokens = 0;
    this.lastRefill = Date.now();
  }
}

Step 2: Exponential Backoff with Jitter

// lib/backoff.ts
interface BackoffOptions {
  maxRetries: number;
  baseDelayMs: number;
  maxDelayMs: number;
  jitter: number;         // 0 to 1
}

const DEFAULTS: BackoffOptions = {
  maxRetries: 4,
  baseDelayMs: 1000,
  maxDelayMs: 60000,
  jitter: 0.25,
};

export async function withBackoff<T>(
  fn: () => Promise<T>,
  opts: Partial<BackoffOptions> = {}
): Promise<T> {
  const { maxRetries, baseDelayMs, maxDelayMs, jitter } = { ...DEFAULTS, ...opts };
  let lastErr: Error | undefined;

  for (let attempt = 0; attempt <= maxRetries; attempt++) {
    try {
      return await fn();
    } catch (err: any) {
      lastErr = err;
      const status = err.statusCode ?? err.status;

      // Don't retry 4xx errors (except 429)
      if (status >= 400 && status < 500 && status !== 429) throw err;

      if (attempt === maxRetries) break;

      // Check Retry-After header (429 responses)
      const retryAfter = err.headers?.["retry-after"];
      let delay: number;

      if (retryAfter) {
        delay = parseInt(retryAfter) * 1000;
      } else {
        delay = Math.min(baseDelayMs * Math.pow(2, attempt), maxDelayMs);
      }

      // Add jitter to prevent thundering herd
      delay += delay * jitter * Math.random();
      console.warn(`CIO retry ${attempt + 1}/${maxRetries} in ${Math.round(delay)}ms`);
      await new Promise((r) => setTimeout(r, delay));
    }
  }
  throw lastErr;
}

Step 3: Rate-Limited Client

// lib/customerio-rate-limited.ts
import { TrackClient, RegionUS } from "customerio-node";
import { TokenBucket } from "./rate-limiter";
import { withBackoff } from "./backoff";

export class RateLimitedCioClient {
  private client: TrackClient;
  private limiter: TokenBucket;

  constructor(siteId: string, apiKey: string, ratePerSec: number = 80) {
    this.client = new TrackClient(siteId, apiKey, { region: RegionUS });
    this.limiter = new TokenBucket(ratePerSec, ratePerSec);
  }

  async identify(userId: string, attrs: Record<string, any>): Promise<void> {
    await this.limiter.acquire();
    return withBackoff(() => this.client.identify(userId, attrs));
  }

  async track(userId: string, event: { name: string; data?: any }): Promise<void> {
    await this.limiter.acquire();
    return withBackoff(() => this.client.track(userId, event));
  }

  async trackAnonymous(event: {
    anonymous_id: string;
    name: string;
    data?: any;
  }): Promise<void> {
    await this.limiter.acquire();
    return withBackoff(() => this.client.trackAnonymous(event));
  }

  async suppress(userId: string): Promise<void> {
    await this.limiter.acquire();
    return withBackoff(() => this.client.suppress(userId));
  }

  async destroy(userId: string): Promise<void> {
    await this.limiter.acquire();
    return withBackoff(() => this.client.destroy(userId));
  }
}

Step 4: Queue-Based Processing with p-queue

For sustained high volume, use p-queue for cleaner concurrency control:

// lib/customerio-queued.ts
import PQueue from "p-queue";
import { TrackClient, RegionUS } from "customerio-node";

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

// Process at most 80 requests per second with max 10 concurrent
const queue = new PQueue({
  concurrency: 10,
  interval: 1000,
  intervalCap: 80,
});

// Queue operations instead of calling directly
export function queueIdentify(userId: string, attrs: Record<string, any>) {
  return queue.add(() => cio.identify(userId, attrs));
}

export function queueTrack(userId: string, name: string, data?: any) {
  return queue.add(() => cio.track(userId, { name, data }));
}

// Monitor queue health
setInterval(() => {
  console.log(
    `CIO queue: pending=${queue.pending} size=${queue.size}`
  );
}, 10000);

Install: npm install p-queue

Step 5: Bulk Import Strategy

For large data imports (>10K users), avoid hitting rate limits with controlled batching:

// scripts/bulk-import.ts
import { RateLimitedCioClient } from "../lib/customerio-rate-limited";

async function bulkImport(users: { id: string; attrs: Record<string, any> }[]) {
  const client = new RateLimitedCioClient(
    process.env.CUSTOMERIO_SITE_ID!,
    process.env.CUSTOMERIO_TRACK_API_KEY!,
    50  // Conservative rate — 50/sec for imports
  );

  let processed = 0;
  let errors = 0;

  for (const user of users) {
    try {
      await client.identify(user.id, user.attrs);
      processed++;
    } catch (err: any) {
      errors++;
      console.error(`Failed user ${user.id}: ${err.message}`);
    }

    if (processed % 1000 === 0) {
      console.log(`Progress: ${processed}/${users.length} (${errors} errors)`);
    }
  }

  console.log(`Done: ${processed} processed, ${errors} errors`);
}

Error Handling

Scenario Strategy
429 received Respect Retry-After header, fall back to exponential backoff
Burst traffic spike Token bucket absorbs burst, queue holds overflow
Sustained high volume Use p-queue with interval limiting
Bulk import Use conservative rate (50/sec) with progress logging
Downstream timeout Don't count as rate limit — retry normally

Resources

Next Steps

After implementing rate limits, proceed to customerio-security-basics for security best practices.

Info
Category Development
Name customerio-rate-limits
Version v20260423
Size 6.83KB
Updated At 2026-04-28
Language