What is the maximum message size for Cloudflare Queues?

The maximum message size is 128 KB. For larger payloads, it is recommended to store the data in Cloudflare R2 and send the object key as the message body.

When should I use explicit acknowledgement (ack)?

Explicit ack() should be used for non-idempotent operations like database writes or financial transactions to ensure a message is only marked as successful after the specific operation completes.

Why is a Dead Letter Queue (DLQ) important?

Without a DLQ, messages that fail after the maximum number of retries are permanently deleted. A DLQ allows you to capture and inspect failed messages for debugging or manual reprocessing.

Does Cloudflare Queues guarantee FIFO ordering?

No, Cloudflare Queues does not guarantee first-in-first-out (FIFO) ordering. You should design your consumers to handle messages out of order and ensure operations are idempotent.

Cloudflare Queues Integration

Name: Cloudflare Queues Integration
Author: jezweb

byjezweb

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Cloud Infrastructure

Builds and manages asynchronous message queues for Cloudflare Workers to enable robust background processing and task orchestration.

About

This skill provides specialized patterns and best practices for implementing Cloudflare Queues within the Cloudflare Workers ecosystem. It simplifies the creation of producers and consumers, handles batch processing, configures dead letter queues for production-grade reliability, and implements complex retry logic with exponential backoff. It is essential for developers building scalable, non-blocking background tasks, handling high-throughput message streams, or managing long-running processes that exceed standard Worker execution limits.

Key Features

Dead Letter Queue (DLQ) configuration for error handling
Asynchronous message producer and consumer implementation
Exponential backoff and custom retry strategies
Wrangler CLI command automation for queue management
High-throughput batch processing and concurrency management
91 GitHub stars

Use Cases

Processing high-volume data streams with batching and retries
Decoupling expensive background tasks from user-facing API responses
Implementing reliable task scheduling and event-driven architectures

About

Key Features

Dead Letter Queue (DLQ) configuration for error handling
Asynchronous message producer and consumer implementation
Exponential backoff and custom retry strategies
Wrangler CLI command automation for queue management
High-throughput batch processing and concurrency management
91 GitHub stars

Use Cases

Processing high-volume data streams with batching and retries
Decoupling expensive background tasks from user-facing API responses
Implementing reliable task scheduling and event-driven architectures