Ruby SDK¶
The official Ruby SDK for SnerdMQ. Ditch Sidekiq and Redis for lightweight, persistent background jobs.
Installation¶
1. Install the gem:
2. Download the Rust engine:
Quickstart¶
require 'snerdmq'
queue = Snerdmq::SnerdQueue.new
# Register a handler
queue.register_handler("send_email") do |data|
puts "Sending email to #{data['to']}..."
# Raise an exception to trigger retry
end
# Start listening (non-blocking threads)
queue.start_listening
# Enqueue a job
queue.enqueue(
task_id: "email-123",
task_type: "send_email",
data: { "to" => "user@example.com", "subject" => "Welcome!" },
max_retries: 3,
retry_after_hours: 0.5,
)
# Handle permanently failed tasks
queue.register_max_retry_handler('send_email') do |data|
puts "Failed after all retries: #{data.inspect}"
end
# Keep main thread alive
sleep
API Reference¶
Snerdmq::SnerdQueue.new(storage_path: nil)¶
Creates a new queue instance and spawns the background daemon.
queue.register_handler(task_type) { |data| ... }¶
Registers a block handler for a task type.
queue.enqueue(...)¶
Enqueues a new background job.
queue.enqueue(
task_id: "unique-id",
task_type: "task_type",
data: { "key" => "value" },
max_retries: 3,
retry_after_hours: 0.5,
rate_limit_group: "api_group",
max_per_minute: 50,
auto_dedupe: true,
urgency_score: 0.9,
execute_at: "2026-12-31T23:59:00Z",
cron: "0 8 * * *",
webhook_url: "https://example.com/webhook",
max_execution_seconds: 300,
)
queue.register_max_retry_handler(task_type) { |data| ... }¶
Registers a block handler for permanently failed tasks (Dead Letter Queue).
queue.start_dashboard(port: 9090)¶
Starts the built-in React dashboard.
queue.yield_progress(message)¶
Streams a progress update from within a handler.
Dashboard¶
Queue Topology¶
Recommended: one queue, all job types (singleton). Each SDK client spawns its own Rust daemon and exclusively owns its storage directory (.snerdata by default). Register every job type on one client and serve a single shared dashboard:
queue = Snerdmq::SnerdQueue.new
# Two job types sharing the same queue, daemon, and dashboard
queue.register_handler("process_image") do |data|
puts "Processing image: #{data['image_id']}"
end
queue.register_handler("send_otp_email") do |data|
puts "Sending OTP to: #{data['to']}"
end
queue.start_dashboard(port: 8080) # one dashboard shows every job type
All job types share the same job log, retry/DLQ pipeline, rate-limit state, and stats.
One queue per storage directory
The daemon takes an exclusive OS-level lock on its storage directory at startup. A second client on the same storage fails fast ("Another daemon is already running on storage ...") instead of double-executing jobs. This also applies across processes — with Puma/Unicorn clustered workers, every worker needs its own storage_path.
Need isolation between workloads? Give each queue its own storage directory — they become fully independent engines (own job log, rate limits, dashboard on its own port):
images = Snerdmq::SnerdQueue.new(storage_path: ".snerdata-images")
emails = Snerdmq::SnerdQueue.new(storage_path: ".snerdata-emails")
images.start_dashboard(port: 8080)
emails.start_dashboard(port: 8081)
Distributed Scaling¶
Scaling horizontally means one queue per server, each with its own storage — the load balancer routes requests, and every server processes the jobs it enqueued:
# Each server runs its own daemon on its own storage dir (local disk works fine)
queue = Snerdmq::SnerdQueue.new(
storage_path: "/var/data/snerd"
)
A shared network drive (AWS EFS or NFS) is still a good home for that storage when a single instance needs durable state across container restarts.
Advanced Orchestration (v0.3.0 Features)¶
SnerdMQ v0.3.0 introduced powerful new primitives for managing complex background jobs. Below are realistic, production-like scenarios showing how to utilize these features in Ruby:
# 1. Sharded Queues
# Context: A developer needs to scale their deployment across 4 servers to handle massive load, but they don't want to use Redis.
# How to use: Tell the daemon how many shards to claim on boot. The queue handles the OS-level locking automatically.
queue = SnerdQueue.new(max_local_shards: 4)
# 2. Worker Pools
# Context: A system has both slow AI generation tasks and fast transactional emails. We want to prevent AI tasks from starving the email workers.
# Enqueue an AI task to a dedicated pool
queue.enqueue(
task_id: 'ai-gen-123',
task_type: 'ai_generation',
data: { prompt: 'A majestic horse' },
pool: 'ai-pool'
)
# Enqueue an email task to a fast, urgent pool
queue.enqueue(
task_id: 'email-123',
task_type: 'send_email',
data: { to: 'user@example.com' },
pool: 'urgent'
)
# 3. Job Chaining (DAGs)
# Context: A video processing pipeline where a video must be transcoded, then uploaded to S3, and finally an email notification must be sent.
# Step 1: Transcode
queue.enqueue(task_id: 'transcode-1', task_type: 'transcode_video', data: { file: 'raw.mp4' })
# Step 2: Upload (Waits for Step 1)
queue.enqueue(
task_id: 'upload-1',
task_type: 'upload_s3',
data: { file: 'processed.mp4' },
trigger_after_ids: ['transcode-1']
)
# Step 3: Notify (Waits for Step 2)
queue.enqueue(
task_id: 'notify-1',
task_type: 'send_email',
data: { status: 'done' },
trigger_after_ids: ['upload-1']
)
# 4. Cron & Scheduled Jobs
# Context: A system needs to run a database cleanup script every night at midnight.
queue.enqueue(
task_id: 'db-cleanup',
task_type: 'cleanup_job',
data: { table: 'sessions' },
cron: '0 0 * * *'
)
# 5. Hard Timeouts
# Context: A background worker is making an HTTP request to a flaky third-party API that might hang indefinitely. We forcefully kill it if it runs over 5 minutes.
queue.enqueue(
task_id: 'api-fetch-1',
task_type: 'fetch_data',
data: { endpoint: '/sync' },
max_execution_seconds: 300
)
# 6. Webhook Callbacks
# Context: A developer is using AWS Lambda or Vercel Serverless functions and wants SnerdMQ to trigger the function via an HTTP POST request rather than running a local worker.
queue.enqueue(
task_id: 'serverless-job',
task_type: 'resize_image',
data: { img: 'cat.jpg' },
webhook_url: 'https://api.example.com/webhook/snerdmq'
)
# 7. The Dead Letter Queue (DLQ)
# Context: A task has failed its maximum number of retries (e.g., the SendGrid API is down for hours). The developer needs to catch this to alert the team on Slack.
queue.register_max_retry_handler('send_email') do |data|
puts "Task permanently failed! Alerting Slack with data: #{data}"
end
Architecture Best Practices¶
When building production applications with SnerdMQ, it is recommended to initialize the queue as a Singleton, isolate your domain workers into separate files/functions, use Dead Letter Queues (DLQ) for failed tasks via RegisterMaxRetryHandler, and ensure manual graceful shutdown. The embedded Dashboard UI can also be easily served from the same instance.
require 'snerdmq'
queue = SnerdQueue.new(storage_path: "./.snerdata")
def init_email_workers(queue)
queue.register_handler('send_email') do |data|
puts "Sending email to #{data['email']}..."
end
queue.register_max_retry_handler('send_email') do |data|
puts "Email to #{data['email']} failed permanently. Dead letter processing..."
end
end
def init_image_workers(queue)
queue.register_handler('process_image') do |data|
puts "Processing image #{data['imageId']}..."
end
end
init_email_workers(queue)
init_image_workers(queue)
queue.start_dashboard(8080)
# Trap signals for graceful shutdown
trap('INT') { queue.shutdown; exit }
trap('TERM') { queue.shutdown; exit }
queue.start_listening