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customerio-rate-limits Implement Customer.io rate limiting and backoff.
Use when handling high-volume API calls, implementing
retry logic, or hitting 429 errors.
Trigger: "customer.io rate limit", "customer.io throttle",
"customer.io 429", "customer.io backoff", "customer.io too many requests".
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GitHub リポジトリを開く name customerio-rate-limits description Implement Customer.io rate limiting and backoff.
Use when handling high-volume API calls, implementing
retry logic, or hitting 429 errors.
Trigger: "customer.io rate limit", "customer.io throttle",
"customer.io 429", "customer.io backoff", "customer.io too many requests".
allowed-tools Read, Write, Edit, Bash(npm:*), Bash(npx:*), Glob, Grep version 1.14.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","customer-io","api","rate-limiting"] compatibility Designed for Claude Code, also compatible with Codex and OpenClaw
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
export class TokenBucket {
private tokens : number ;
private lastRefill : number ;
constructor (
private readonly maxTokens : number = 80 ,
private readonly refillRate : number = 80
) {
this .tokens = maxTokens;
this .lastRefill = Date
now
private
refill
void
const
Date
now
const
this
lastRefill
1000
this
tokens
Math
min
this
maxTokens
this
tokens
this
refillRate
this
lastRefill
async
acquire
Promise
void
this
refill
if
this
tokens
1
this
tokens
1
return
const
1
this
tokens
this
refillRate
1000
await
new
Promise
(r ) =>
setTimeout
Math
ceil
this
tokens
0
this
lastRefill
Date
now
Step 2: Exponential Backoff with Jitter
interface BackoffOptions {
maxRetries : number ;
baseDelayMs : number ;
maxDelayMs : number ;
jitter : number ;
}
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 ;
if (status >= 400 && status < 500 && status !== 429 ) throw err;
if (attempt === maxRetries) break ;
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);
}
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
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:
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 }
);
const queue = new PQueue ({
concurrency : 10 ,
interval : 1000 ,
intervalCap : 80 ,
});
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 }));
}
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:
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
);
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 receivedRespect 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.
