JoobQ

A high-performance, production-ready asynchronous job queue system for Crystal

Features • Installation • Quick Start • Documentation • API Reference

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Overview

JoobQ is an enterprise-grade asynchronous job queue system built for the Crystal programming language. Designed for both simplicity and scalability, JoobQ provides a robust framework for managing background job processing in applications of any size—from startup MVPs to large-scale distributed systems.

Why Choose JoobQ?

JoobQ combines the performance advantages of Crystal with the reliability of Redis to deliver a job queue system that doesn't compromise on speed, reliability, or developer experience.

Performance

• Processes up to 35,000 jobs per second on modern hardware
• Built on Crystal's efficient concurrency model
• Optimized Redis operations for minimal latency

Reliability

• Automatic retry mechanisms with exponential backoff
• Dead letter queue for failed job analysis
• Job expiration and timeout handling
• Graceful shutdown preventing job loss

Developer Experience

• Clean, intuitive API following Crystal idioms
• Comprehensive OpenAPI 3.0 specification
• Type-safe job definitions
• Minimal configuration required

Flexibility

• Priority-based queue processing
• Cron-like recurring job scheduling
• Dynamic throttling and rate limiting
• Middleware support for cross-cutting concerns

Key Features

• 🚀 High Performance: Process tens of thousands of jobs per second with Crystal's native performance
• ⚡ Priority Queues: Configure worker allocation based on queue priority
• 🔄 Retry Logic: Built-in exponential backoff and customizable retry strategies
• ⏰ Scheduling: Cron-style recurring jobs and delayed execution
• 🎯 Throttling: Fine-grained rate limiting to prevent system overload
• 📊 Monitoring: Comprehensive REST API for metrics and job management
• 🔌 OpenAPI Integration: Complete API specification for client generation
• 🛡️ Error Tracking: Detailed error monitoring and reporting
• ♻️ Dead Letter Queue: Automatic handling of failed jobs
• 🔒 Type Safety: Compile-time guarantees for job definitions

Table of Contents

• JoobQ
  • Overview
    • Why Choose JoobQ?
    • Key Features
    • Table of Contents
  • Getting Started
    • Prerequisites
    • Quick Installation
  • Installation
  • Usage
    • Environment Variables
    • Configuration
    • Configuration Properties
    • Example Configuration with All Properties
    • Defining Queues
    • Queue Throttling
      • Queue Throttle Limit Property
      • How They Work Together
      • Summary
    • Defining Jobs
      • Best Practices for Defining Jobs
        • Idempotency
        • Simple Primitive Types for Arguments
        • Number of Arguments
  • JoobQ HTTP Server
    • Rest API
    • GET /joobq/jobs/registry
    • POST /joobq/jobs
  • REST API & OpenAPI Specification
    • API Endpoints
    • OpenAPI Specification
    • Quick Start with the API
    • Client Generation
    • Interactive Documentation
  • Performance
    • Performance Comparison
      • Disclaimer
  • Contributing
  • Testing
  • Deployment
  • License
  • Acknowledgments

Getting Started

Prerequisites

Before you begin, ensure you have the following installed:

• Crystal (version 0.34.0 or higher)
• Redis (version 5.0 or higher recommended)

Quick Installation

1. Clone the repository (for development):

   git clone https://github.com/azutoolkit/joobq.git
   cd joobq

2. Install dependencies:

   shards install

3. Start Redis (if not already running):

   redis-server

Installation

Add the following to your shard.yml:

dependencies:
  joobq:
    github: azutoolkit/joobq

Then run:

shards install

Usage

require "joobq"

Environment Variables

REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_POOL_SIZE=500
REDIS_POOL_TIMEOUT=5.0
REDIS_PASS=somepass
REDIS_TIMEOUT=0.2

Configuration

JoobQ can be configured using the JoobQ.configure method. Here is an example configuration:

JoobQ.configure do
  queue "default", 10, EmailJob

  scheduler do
    cron("*/1 * * * *") { # Do Something }
    every 1.hour, EmailJob, email_address: "[email protected]"
  end
end

Configuration Properties

The JoobQ::Configure struct provides several properties that can be customized to configure the JoobQ system:

• default_queue: The name of the default queue. Defaults to "default".
• retries: The number of retries for a job. Defaults to 3.
• expires: The expiration time for a job. Defaults to 3.days.
• timeout: The maximum execution time allowed for a job. Defaults to 2.seconds.
• failed_ttl: The time-to-live for failed jobs. Defaults to 3.milliseconds.
• dead_letter_ttl: The time-to-live for jobs in the dead letter queue. Defaults to 7.days.
• job_registry: An instance of JobSchemaRegistry for managing job schemas.
• store: The store instance used for job storage and retrieval. Defaults to RedisStore.

Example Configuration with All Properties

JoobQ.configure do |config|
  config.default_queue = "default"
  config.retries = 5
  config.expires = 2.days
  config.timeout = 5.seconds
  config.failed_ttl = 1.minute
  config.dead_letter_ttl = 14.days

  queue "default", 10, EmailJob
  queue "priority", 5, PriorityJob, throttle: { limit: 100, period: 1.minute }

  scheduler do
    cron("*/1 * * * *") { # Do Something }
    every 1.hour, EmailJob, email_address: "[email protected]"
  end
end

Defining Queues

Queues are of type Hash(String, Queue(T)) where the name of the key matches the name of the Queue.

Example:

JoobQ.configure do
  queue name: "single", workers: 10, job: Job1, throttle: { limit: 20, period: 1.minute }
  queue "example", 10, ExampleJob | FailJob

  # Scheduling Recurring Jobs
  scheduler do
    cron("*/1 * * * *") { # Do Something }
    cron("*/5 20-23 * * *") { # Do Something }
    every 1.hour, ExampleJob, x: 1
  end
end

Queue Throttling

The worker throttle mechanism in JoobQ works in conjunction with the Queue Throttle Limit property to manage the rate at which jobs are processed. Here's how it works:

Queue Throttle Limit Property

The Queue Throttle limit property sets a maximum number of jobs that can be processed within a specific time frame. This helps to control the load on the system and ensures that the job processing rate does not exceed a certain threshold.

How They Work Together

• Job Availability and Throttle Limit: The worker checks the queue for available jobs. If the number of jobs processed within the specified time frame reaches the throttle limit, the worker will wait until it is allowed to process more jobs. This prevents the system from being overwhelmed by too many jobs at once.

• Job Expiration: Before processing a job, the worker checks if the job has expired. If the job has expired, it is marked as expired and not processed. This ensures that only valid jobs are processed, reducing unnecessary work.

• Controlled Shutdown: The worker can be stopped gracefully by sending a termination signal. This allows for a controlled shutdown, ensuring that no jobs are abruptly terminated.

Example:

Here is an example of how you might configure the Queue Throttle limit property:

JoobQ.configure do
  queue "default", 10, EmailJob, throttle: { limit: 100, period: 60.seconds }
end

In this example, the throttle limit is set to 100 jobs per 60 seconds. This means that the worker will process up to 100 jobs every 60 seconds. If the limit is reached, the worker will wait until the next period to continue processing jobs.

Summary

The worker throttle mechanism, combined with the Queue Throttle limit property, ensures that job processing is controlled and efficient. By managing job availability, expiration, and processing rate, JoobQ provides a robust system for handling job queues without overwhelming the system.

Defining Jobs

To define Jobs, include the JoobQ::Job module, and implement the perform method.

struct EmailJob
  include JoobQ::Job

  # Name of the queue to be processed by
  @queue   = "default"
  # Number Of Retries for this job
  @retries = 0
  # Job Expiration
  @expires = 1.days.total_seconds.to_i

  # Initialize as normal with or without named tuple arguments
  def initialize(@email_address : String)
  end

  def perform
    # Logic to handle job execution
  end
end

Executing Job:

# Enqueue the job (Async)
EmailJob.enqueue(email_address: "[email protected]")

# Batch enqueue jobs
EmailJob.batch_enqueue([job1, job2, job3])

# Perform Immediately
EmailJob.new(email_address: "[email protected]").perform

# Delayed
EmailJob.delay(for: 1.hour, email_address: "[email protected]")
EmailJob.enqueue_at(time: 3.minutes.from_now, email_address: "[email protected]")

# Recurring at given interval
EmailJob.schedule(every: 1.second, email_address: "[email protected]")

Best Practices for Defining Jobs

When defining jobs in JoobQ, it's important to follow certain best practices to ensure reliability and maintainability. Here are some key recommendations:

Idempotency

Jobs must be idempotent. This means that running the same job multiple times should produce the same result. Idempotency is crucial for ensuring that jobs can be retried safely without causing unintended side effects. To achieve idempotency:

• Avoid modifying external state directly within the job.
• Use unique identifiers to track job execution and prevent duplicate processing.
• Ensure that any side effects (e.g., database updates, API calls) are safe to repeat.

Simple Primitive Types for Arguments

Job arguments must be simple primitive types such as integers, strings, and booleans. This ensures that the job data can be easily serialized and deserialized, and reduces the risk of errors during job execution. Complex objects or data structures should be avoided as job arguments.

Number of Arguments

Keep the number of arguments for jobs to a minimum. Having too many arguments can make the job definition complex and harder to maintain. As a best practice:

• Limit the number of arguments to 3-5.
• Group related arguments into a single object if necessary.
• Use default values for optional arguments to simplify job invocation.

By following these best practices, you can ensure that your jobs are reliable, maintainable, and easy to work with in the JoobQ framework.

JoobQ HTTP Server

The APIServer class provides a REST API to interact with the JoobQ job queue system. It listens for HTTP requests and handles job enqueuing, job registry retrieval, and queue metrics.

To start the API server.

APIServer.start

Rest API

JoobQ provides a REST API to interact with the job queue. Below are the available endpoints:

GET /joobq/jobs/registry

This endpoint returns all available registered jobs and their JSON schemas that can be enqueued via the REST API.

Request:

GET /joobq/jobs/registry HTTP/1.1
Host: localhost:8080

Response:

{
  "EmailJob": {
    "type": "object",
    "properties": {
      "email_address": {
        "type": "string"
      }
    },
    "required": ["email_address"]
  }
}

POST /joobq/jobs

This endpoint allows users to enqueue jobs.

Request:

POST /joobq/jobs HTTP/1.1
Host: localhost:8080
Content-Type: application/json
Content-Length: 175

{
    "jid": "a13324f4-bdd8-4cf5-b566-c0c9c312f68b",
    "queue": "queue:test",
    "retries": 3,
    "expires": {{timestamp_plus_30}},
    "status": "enqueued",
    // Job initialization arguments
    "x": 10
}

Response:

{
  "status": "Job enqueued",
  "queue": "default",
  "job_id": "some-unique-job-id"
}

REST API & OpenAPI Specification

JoobQ provides a comprehensive REST API for job management, monitoring, and debugging. The API is fully documented with an OpenAPI 3.0 specification that enables easy client generation and integration.

API Endpoints

• Job Management: Enqueue jobs, retrieve job registry
• Queue Operations: Reprocess busy jobs, monitor queue health
• Error Monitoring: Track errors, get statistics, filter by type/queue
• Health Checks: System status and monitoring

OpenAPI Specification

The complete OpenAPI 3.0 specification is available in the docs/ directory:

• Specification: docs/openapi.yaml
• Documentation: docs/API_SPECIFICATION.md
• Client Examples: examples/crystal_client_example.cr

Quick Start with the API

# Health check
curl http://localhost:8080/joobq/health/check

# Enqueue a job
curl -X POST http://localhost:8080/joobq/jobs \
  -H "Content-Type: application/json" \
  -d '{
    "queue": "email",
    "job_type": "EmailJob",
    "data": {
      "email": "[email protected]",
      "subject": "Welcome"
    }
  }'

# Get error statistics
curl http://localhost:8080/joobq/errors/stats

# Reprocess busy jobs
curl -X POST http://localhost:8080/joobq/queues/default/reprocess

Client Generation

Generate clients for your preferred language:

# Install OpenAPI Generator
brew install openapi-generator

# Generate clients
openapi-generator generate -i docs/openapi.yaml -g crystal -o clients/crystal/
openapi-generator generate -i docs/openapi.yaml -g typescript-axios -o clients/typescript/
openapi-generator generate -i docs/openapi.yaml -g python -o clients/python/

Interactive Documentation

View the interactive API documentation:

docker run -p 8080:8080 -e SWAGGER_JSON=/docs/openapi.yaml -v $(pwd)/docs:/docs swaggerapi/swagger-ui

Performance

JoobQ is designed for high performance and scalability. In our benchmarks, JoobQ has easily achieved processing rates of 35,000 jobs per second. This performance is made possible by leveraging Crystal's concurrency model and efficient job handling mechanisms.

Performance Comparison

To provide a clearer picture of JoobQ's performance, we have compared it with other popular job queue processing tools in various programming languages, including Sidekiq (Ruby), Celery (Python), Laravel Queue (PHP), and Quartz (Java). The results are summarized in the table below:

Job Queue Tool	Language	Jobs per Second
JoobQ	Crystal	35,000
Sidekiq	Ruby	23,500
Celery	Python	15,000
Laravel Queue	PHP	10,000
Quartz	Java	25,000

JoobQ Hardware benchmarks performed

Hardware Overview:
  Model Name:             MacBook Pro
  Model Identifier:       Mac15,10
  Model Number:           MRX53LL/A
  Chip:                   Apple M3 Max
  Total Number of Cores: 14 (10 performance and 4 efficiency)
  Memory:                 36 GB

As shown in the table, JoobQ outperforms many of the popular job queue processing tools, making it an excellent choice for high-throughput job processing needs.

Disclaimer

JoobQ out of the box provides great performance, achieving processing rates of up to 35,000 jobs per second in our benchmarks. However, it's important to note that with the right environment and settings, any job scheduler can be performant. Factors such as hardware specifications, network conditions, and job complexity can significantly impact the performance of job queue processing tools. Therefore, it's essential to optimize your environment and configurations to achieve the best possible performance for your specific use case.

Contributing

1. Fork it (https://github.com/azutoolkit/joobq/fork)
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create a new Pull Request

Testing

To run the tests, use the following command:

crystal spec

Deployment

To deploy JoobQ, ensure that you have a running Redis instance. You can use the provided docker-compose.yml to set up Redis.

docker-compose up -d

License

The MIT License (MIT). Please see License File for more information.

Acknowledgments

Elias J. Perez - creator and maintainer Crystal Language Redis