A job processing system that just verks! 🧛‍
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README.md

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Verk is a job processing system backed by Redis. It uses the same job definition of Sidekiq/Resque.

The goal is to be able to isolate the execution of a queue of jobs as much as possible.

Every queue has its own supervision tree:

  • A pool of workers;
  • A QueueManager that interacts with Redis to get jobs and enqueue them back to be retried if necessary;
  • A WorkersManager that will interact with the QueueManager and the pool to execute jobs.

Verk will hold one connection to Redis per queue plus one dedicated to the ScheduleManager and one general connection for other use cases like deleting a job from retry set or enqueuing new jobs.

The ScheduleManager fetches jobs from the retry set to be enqueued back to the original queue when it's ready to be retried.

It also has one GenStage producer called Verk.EventProducer.

The image below is an overview of Verk's supervision tree running with a queue named default having 5 workers.

Supervision Tree

Feature set:

  • Retry mechanism with exponential backoff
  • Dynamic addition/removal of queues
  • Reliable job processing (RPOPLPUSH and Lua scripts to the rescue)
  • Error and event tracking

Installation

First, add Verk to your mix.exs dependencies:

def deps do
  [{:verk, "~> 1.0"}]
end

and run $ mix deps.get. Add :verk to your applications list if your Elixir version is 1.3 or lower:

def application do
  [applications: [:verk]]
end

Add Verk.Supervisor to your supervision tree:

defmodule Example.App do
  use Application

  def start(_type, _args) do
    import Supervisor.Spec
    tree = [supervisor(Verk.Supervisor, [])]
    opts = [name: Simple.Sup, strategy: :one_for_one]
    Supervisor.start_link(tree, opts)
  end
end

Finally we need to configure how Verk will process jobs.

Configuration

Example configuration for Verk having 2 queues: default and priority

The queue default will have a maximum of 25 jobs being processed at a time and priority just 10.

config :verk, queues: [default: 25, priority: 10],
              max_retry_count: 10,
              poll_interval: 5000,
              start_job_log_level: :info,
              done_job_log_level: :info,
              fail_job_log_level: :info,
              node_id: "1",
              redis_url: "redis://127.0.0.1:6379"

Verk supports the convention {:system, "ENV_NAME", default} for reading environment configuration at runtime using Confex:

config :verk, queues: [default: 25, priority: 10],
              max_retry_count: 10,
              poll_interval: {:system, :integer, "VERK_POLL_INTERVAL", 5000},
              start_job_log_level: :info,
              done_job_log_level: :info,
              fail_job_log_level: :info,
              node_id: "1",
              redis_url: {:system, "VERK_REDIS_URL", "redis://127.0.0.1:6379"}

Now Verk is ready to start processing jobs! 🎉

Workers

A job is defined by a module and arguments:

defmodule ExampleWorker do
  def perform(arg1, arg2) do
    arg1 + arg2
  end
end

This job can be enqueued using Verk.enqueue/1:

Verk.enqueue(%Verk.Job{queue: :default, class: "ExampleWorker", args: [1,2], max_retry_count: 5})

This job can also be scheduled using Verk.schedule/2:

perform_at = Timex.shift(Timex.now, seconds: 30)
Verk.schedule(%Verk.Job{queue: :default, class: "ExampleWorker", args: [1,2]}, perform_at)

Retry at

A job can define the function retry_at/2 for custom retry time delay:

defmodule ExampleWorker do
  def perform(arg1, arg2) do
    arg1 + arg2
  end

  def retry_at(failed_at, retry_count) do
    failed_at + retry_count
  end
end

In this example, the first retry will be scheduled a second later, the second retry will be scheduled two seconds later, and so on.

If retry_at/2 is not defined the default exponential backoff is used.

Keys in arguments

By default, Verk will decode keys in arguments to binary strings. You can change this behavior for jobs enqueued by Verk with the following configuration:

config :verk, :args_keys, value

The following values are valid:

  • :strings (default) - decodes keys as binary strings
  • :atoms - keys are converted to atoms using String.to_atom/1
  • :atoms! - keys are converted to atoms using String.to_existing_atom/1

Queues

It's possible to dynamically add and remove queues from Verk.

Verk.add_queue(:new, 10) # Adds a queue named `new` with 10 workers
Verk.remove_queue(:new) # Terminate and delete the queue named `new`

Deployment

The way Verk currently works, there are two pitfalls to pay attention to:

  1. Each worker node's node_id MUST be unique. If a node goes online with a node_id, which is already in use by another running node, then the second node will re-enqueue all jobs currently in progress on the first node, which results in jobs executed multiple times.
  2. Take caution around removing nodes. If a node with jobs in progress is killed, those jobs will not be restarted until another node with the same node_id comes online. If another node with the same node_id never comes online, the jobs will be stuck forever. This means you should not use dynamic node_ids such as Docker container ids or Kubernetes Deployment pod names.

On Heroku

Heroku provides an experimental environment variable named after the type and number of the dyno.

config :verk,
  node_id: {:system, "DYNO", "job.1"}

It is possible that two dynos with the same name could overlap for a short time during a dyno restart. As the Heroku documentation says:

[...] $DYNO is not guaranteed to be unique within an app. For example, during a deploy or restart, the same dyno identifier could be used for two running dynos. It will be eventually consistent, however.

This means that you are still at risk of violating the first rule above on node_id uniqueness. A slightly naive way of lowering the risk would be to add a delay in your application before the Verk queue starts.

On Kubernetes

We recommend using a StatefulSet to run your pool of workers. StatefulSets add a label, statefulset.kubernetes.io/pod-name, to all its pods with the value {name}-{n}, where {name} is the name of your StatefulSet and {n} is a number from 0 to spec.replicas - 1. StatefulSets maintain a sticky identity for its pods and guarantee that two identical pods are never up simultaneously. This way it satisfies both of our deployment rules mentioned above.

Define your worker like this:

# StatefulSets require a service, even though we don't use it directly for anything
apiVersion: v1
kind: Service
metadata:
 name: my-worker
 labels:
   app: my-worker
spec:
 clusterIP: None
 selector:
   app: my-worker

---

apiVersion: apps/v1
kind: StatefulSet
metadata:
 name: my-worker
 labels:
   app: my-worker
spec:
 selector:
   matchLabels:
     app: my-worker
 serviceName: my-worker
 # We run two workers in this example
 replicas: 2
 # The workers don't depend on each other, so we can use Parallel pod management
 podManagementPolicy: Parallel
 template:
   metadata:
     labels:
       app: my-worker
   spec:
     # This should probably match up with the setting you used for Verk's :shutdown_timeout
     terminationGracePeriodSeconds: 30
     containers:
       - name: my-worker
         image: my-repo/my-worker
         env:
           - name: VERK_NODE_ID
             valueFrom:
               fieldRef:
                 fieldPath: metadata.labels['statefulset.kubernetes.io/pod-name']

Notice how we use a fieldRef to expose the pod's statefulset.kubernetes.io/pod-name label as the VERK_NODE_ID environment variable. Instruct Verk to use this environment variable as node_id:

config :verk,
 node_id: {:system, "VERK_NODE_ID"}

Be careful when scaling the number of replicas down. Make sure that the pods that will be stopped and never come back do not have any jobs in progress. Scaling up is always safe.

Don't use Deployments for pods that will run Verk. If you hardcode node_id into your config, multiple pods with the same node_idwill be online at the same time, violating the first rule. If you use a non-sticky environment variable, such as HOSTNAME, you'll violate the second rule and cause jobs to get stuck every time you deploy.

If your application serves as e.g. both an API and Verk queue, then it may be wise to run a separate Deployment for your API, which does not run Verk. In that case you can configure your application to check an environment variable, VERK_DISABLED, for whether it should handle any Verk queues:

# In your config.exs
config :verk,
  queues: {:system, {MyApp.Env, :verk_queues, []}, "VERK_DISABLED"}

# In some other file
defmodule MyApp.Env do
  def verk_queues("true"), do: []
  def verk_queues(_), do: [default: 25, priority: 10]
end

Then set VERK_DISABLED=true in your Deployment's spec.

Reliability

Verk's goal is to never have a job that exists only in memory. It uses Redis as the single source of truth to retry and track jobs that were being processed if some crash happened.

Verk will re-enqueue jobs if the application crashed while jobs were running. It will also retry jobs that failed keeping track of the errors that happened.

The jobs that will run on top of Verk should be idempotent as they may run more than once.

Error tracking

One can track when jobs start and finish or fail. This can be useful to build metrics around the jobs. The QueueStats handler does some kind of metrics using these events: https://github.com/edgurgel/verk/blob/master/lib/verk/queue_stats.ex

Verk has an Event Manager that notifies the following events:

  • Verk.Events.JobStarted
  • Verk.Events.JobFinished
  • Verk.Events.JobFailed
  • Verk.Events.QueueRunning
  • Verk.Events.QueuePausing
  • Verk.Events.QueuePaused

One can define an error tracking handler like this:

defmodule TrackingErrorHandler do
  use GenStage

  def start_link() do
    GenStage.start_link(__MODULE__, :ok)
  end

  def init(_) do
    filter = fn event -> event.__struct__ == Verk.Events.JobFailed end
    {:consumer, :state, subscribe_to: [{Verk.EventProducer, selector: filter}]}
  end

  def handle_events(events, _from, state) do
    Enum.each(events, &handle_event/1)
    {:noreply, [], state}
  end

  defp handle_event(%Verk.Events.JobFailed{job: job, failed_at: failed_at, stacktrace: trace}) do
    MyTrackingExceptionSystem.track(stacktrace: trace, name: job.class)
  end
end

Notice the selector to get just the type JobFailed. If no selector is set every event is sent.

Then adding the consumer to your supervision tree:

defmodule Example.App do
  use Application

  def start(_type, _args) do
    import Supervisor.Spec
    tree = [supervisor(Verk.Supervisor, []),
            worker(TrackingErrorHandler, [])]
    opts = [name: Simple.Sup, strategy: :one_for_one]
    Supervisor.start_link(tree, opts)
  end
end

Dashboard ?

Check Verk Web!

Dashboard

Metrics ?

Check Verk Stats

Sponsorship

Initial development sponsored by Carnival.io