A Node.js queue library focused on easy, yet flexible task execution.
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Ivy is node.js queue library focused on easy, yet flexible task execution.


Installation is done via NPM, by running npm install ivy

Version 2 quick example (called TODO)

var ivy = require('ivy');

var factorial = function factorial(number, callback) {
    callback(null, 42);

var finished  = function resolved(result) {
    console.log('result is', result);

// task must be explicitly registered for now
// we'd like to change that in the future

// Also, task must be both available and registered on both client
// and producer

// ...and name must be unique globally. "package.module.submodule.function"
// pattern is highly encouraged.
ivy.registerTask(factorial, finished, {
   'name':   'testpackage.factorial',
   'queue':  'testpackage' //,
//    'route':  'testpackage.route1',
//    'priority': 0,
//    retry:    true,
//    maxRetries: 10

if (process.env.NODE_ENV==='producer') {

      type: 'ironmq',
      auth: {
          token:      process.env.IRONMQ_TOKEN      || 'dummy',
          project_id: process.env.IRONMQ_PROJECT_ID || 'testpackage'
      //, queue: 'testpackage' // optional, inferred from task

  // optional, only if callback is registered
      'type': 'redis',
      'url':  'redis://name:password@hostname:port'

  // execute task
  ivy.delayedCall(factorial, 5, function(err, result) {
    console.log("Factorial result is", result);

elseif (process.env.NODE_ENV==='worker') {
        'type': 'redis',
        'url':  'redis://name:password@hostname:port'
        queue: 'testpackage',

        type: 'ironmq',
        auth: {
            token:      process.env.IRONMQ_TOKEN   || 'dummy',
            project_id: process.env.IRONMQ_PROJECT || 'testpackage'

        // optional
        messages: {
            'testpackage.factorial': {
                reserveTime: 60*60



Problem solved by Ivy

Ivy touches the following workflow:

  • Function execution is scheduled from application ("producer") in similar way as executing function directly
  • Call is serialized and transferred through queue to worker. Producer subscribes to notifier for completion
    • Worker job is considered essential. It should be thus delivered through robust, HA queue, such as AMQP, RabbitMQ, SQS, IronMQ or similar.
    • Arguments are send as a stringifyed JSON. There is no attempt to magically recover original object; called functions should thus rely only on attribute access, not method calls
  • Worker executes function on shared code base, with arguments fetched from queue task
    • If execution errors, task stays in MQ or is returned there, depending on implementation
    • If it fails permanently, please beware of JSON.stringify(new Error()) idiosyncrasy
  • Producer is notified back about completion
    • Speed over robustness is preferred as this should be about notifying client back, not further work
    • Thus, redis pub/sub is preferred
    • If non-notification work should follow after execution is done, it should be scheduled as another task in MQ


  • Only tasks/functions with async interface supported. Assumptions:

    • callback is last argument provided
    • callback must be present
    • first argument of callback signature is either Error or null
  • Think about context change

    • Last callback is about placing task in queue as opposed to having direct callback
    • However, extracting to named function is needed
    • Multiple callbacks looks strange.
  • Explicit is better then implicit

    • In first version, use explicit task registrations
    • Leave continuation and function "backresolve" to v2
    • We can implicitly decide whether notifications are producer or consumer: consumer when listen is invoked, producer when first delayedCall is executed. Make it explicit in v1, we'll see later.
  • Task registries must be same on both sides

    • "Protocol" specification for backends in other languages
  • Serialization boundaries

    • There are (mostly) no requirements on payload in queues
    • Default "protocol" is JSON, should be separated into serialization module/package
    • Protobufs should be neat choice

Naming and definitions

There are a lot of parts and components in distributed environment. This is how Ivy understands them.

  • Producer: Process that decides some task should not be processed by itself, but instead delegated to another process through queue.
  • Caller: Particular function/code where ivy.delayedCall has been called.
  • Queue: Service/process designed to dispatch messages between processes or services. It ideally processes them in (prioritized) FIFO with one time delivery. Also known as broker.
  • Queue name: Inside Queue services, Message's are organized into separate, well, queues, identified by name. To avoid naming clashes, those are always referred to as Queue names instead of just "queues".
  • Queue backend: Particular piece of software implementing Queue's role, i.e. IronMQ, SQS, RabbitMQ, ...
  • Consumer: Process designed to consume messages from Queue and processing them.
  • Listener: Part of the Consumer that listens to Queue and waits for Messages
  • Message: Structured data format placed in Queue, understood on both ends.
  • Message serialization: Particular serialization format used for placing Message into Queue, i.e. JSON.
  • Message format: Particular structure used for particular Message serialization, i.e. {"task": "taskname", "arguments": []} migth be an example Message format for JSON Message serialization.
  • Task: Function to be invoked on Consumer. May be parametrized by Message's content.
  • Scheduled Task: A way to describe intent of invoking Task at some point.
  • Task status: A state that describes current state of Scheduled Task or Task. May be scheduled (successfully placed in Queue, but not consumed by Consumer yet), running (processing on consumer), errored (some state failed), successfull (processing done on consumer and Notifier successfully notified) and done (successfull + Producer successfully notified).
  • Task result: Data "returned" by Task upon its completion with the intent of informing Producer about it. While the primary purpose might be computation task that produces an output that is stored in database, it is not considered Task result if it's not intended for Producer. Result is an array of arguments given to Task's callback.
  • Task execution: The act of running task on Consumer.
  • Task arguments: Array of arguments for the Task excluding the last one (that must be callback. I.e. for function factorial = (number, cb), the arguments are [number], i.e. [5].
  • Sending task is an act of creating ScheduledTask by serializing original delayedCall call into Message and putting it in Queue.
  • Consuming tasks is an act of retrieving Messages from Queue on Consumer done by Listener.
  • Caller resume: The act of resuming the workflow back on Producer, done by calling callback passed to original delayedCall.
  • Task resolved: Task has been executed and Producer notified -- or there has been an error.
  • Notifier: Service/process designed to inform Producer about Task status and/or Task result. Might be same piece of software/service as Queue.
  • Notification channel: Uniquely-named "queue" used to pass Task results from any Consumer to particular Producer.
  • Notifier backend: Particular piece of software implementing Notifier's role, i.e. IronMQ, Redis, ...

Encryption support for IronMQ

If you can encrypt all messages for better security add encryptionKey as password. We use aes-256-cbc algorithm for encrypt and decrypt messages.

    queue: 'testpackage',

    type: 'ironmq',
    auth: {
        token:      process.env.IRONMQ_TOKEN      || 'dummy',
        project_id: process.env.IRONMQ_PROJECT_ID || 'testpackage'
    encryptionKey:  process.env.MESSAGES_ENCRYPTION_KEY


Install grunt

npm -g install grunt-cli

Run tests


Release new version using grunt-bump

grunt bump
grunt bump:minor
grunt bump:major


npm publish

Using Docker


docker-compose build

Running tests

docker-compose run ivy