Sidekiq Style Guide

This document outlines various guidelines that should be followed when adding or modifying Sidekiq workers.


All workers should include ApplicationWorker instead of Sidekiq::Worker, which adds some convenience methods and automatically sets the queue based on the worker’s name.

Dedicated Queues

All workers should use their own queue, which is automatically set based on the worker class name. For a worker named ProcessSomethingWorker, the queue name would be process_something. If you’re not sure what queue a worker uses, you can find it using SomeWorker.queue. There is almost never a reason to manually override the queue name using sidekiq_options queue: :some_queue.

After adding a new queue, run bin/rake gitlab:sidekiq:all_queues_yml:generate to regenerate app/workers/all_queues.yml or ee/app/workers/all_queues.yml so that it can be picked up by sidekiq-cluster.

Queue Namespaces

While different workers cannot share a queue, they can share a queue namespace.

Defining a queue namespace for a worker makes it possible to start a Sidekiq process that automatically handles jobs for all workers in that namespace, without needing to explicitly list all their queue names. If, for example, all workers that are managed by sidekiq-cron use the cronjob queue namespace, we can spin up a Sidekiq process specifically for these kinds of scheduled jobs. If a new worker using the cronjob namespace is added later on, the Sidekiq process will automatically pick up jobs for that worker too (after having been restarted), without the need to change any configuration.

A queue namespace can be set using the queue_namespace DSL class method:

class SomeScheduledTaskWorker
  include ApplicationWorker

  queue_namespace :cronjob

  # ...

Behind the scenes, this will set SomeScheduledTaskWorker.queue to cronjob:some_scheduled_task. Commonly used namespaces will have their own concern module that can easily be included into the worker class, and that may set other Sidekiq options besides the queue namespace. CronjobQueue, for example, sets the namespace, but also disables retries.

bundle exec sidekiq is namespace-aware, and will automatically listen on all queues in a namespace (technically: all queues prefixed with the namespace name) when a namespace is provided instead of a simple queue name in the --queue (-q) option, or in the :queues: section in config/sidekiq_queues.yml.

Note that adding a worker to an existing namespace should be done with care, as the extra jobs will take resources away from jobs from workers that were already there, if the resources available to the Sidekiq process handling the namespace are not adjusted appropriately.

Idempotent Jobs

It’s known that a job can fail for multiple reasons. For example, network outages or bugs. In order to address this, Sidekiq has a built-in retry mechanism that is used by default by most workers within GitLab.

It’s expected that a job can run again after a failure without major side-effects for the application or users, which is why Sidekiq encourages jobs to be idempotent and transactional.

As a general rule, a worker can be considered idempotent if:

  • It can safely run multiple times with the same arguments.
  • Application side-effects are expected to happen only once (or side-effects of a second run do not have an effect).

A good example of that would be a cache expiration worker.

Note: A job scheduled for an idempotent worker will automatically be deduplicated when an unstarted job with the same arguments is already in the queue.

Ensuring a worker is idempotent

Make sure the worker tests pass using the following shared example:

include_examples 'an idempotent worker' do
  it 'marks the MR as merged' do
    # Using subject inside this block will process the job multiple times

    expect(merge_request.state).to eq('merged')

Use the perform_multiple method directly instead of job.perform (this helper method is automatically included for workers).

Declaring a worker as idempotent

class IdempotentWorker
  include ApplicationWorker

  # Declares a worker is idempotent and can
  # safely run multiple times.

  # ...

It’s encouraged to only have the idempotent! call in the top-most worker class, even if the perform method is defined in another class or module.

Note: If the worker class is not marked as idempotent, a cop will fail. Consider skipping the cop if you’re not confident your job can safely run multiple times.


When a job for an idempotent worker is enqueued while another unstarted job is already in the queue, GitLab drops the second job. The work is skipped because the same work would be done by the job that was scheduled first; by the time the second job executed, the first job would do nothing.

For example, AuthorizedProjectsWorker takes a user ID. When the worker runs, it recalculates a user’s authorizations. GitLab schedules this job each time an action potentially changes a user’s authorizations. If the same user is added to two projects at the same time, the second job can be skipped if the first job hasn’t begun, because when the first job runs, it creates the authorizations for both projects.

GitLab doesn’t skip jobs scheduled in the future, as we assume that the state will have changed by the time the job is scheduled to execute.

More deduplication strategies have been suggested. If you are implementing a worker that could benefit from a different strategy, please comment in the issue.

If the automatic deduplication were to cause issues in certain queues. This can be temporarily disabled by enabling a feature flag named disable_<queue name>_deduplication. For example to disable deduplication for the AuthorizedProjectsWorker, we would enable the feature flag disable_authorized_projects_deduplication.

From ChatOps:

/chatops run feature set disable_authorized_projects_deduplication true

From the rails console:


Job urgency

Jobs can have an urgency attribute set, which can be :high, :low, or :throttled. These have the below targets:

Urgency Queue Scheduling Target Execution Latency Requirement
:high 10 seconds p50 of 1 second, p99 of 10 seconds
:low 1 minute Maximum run time of 5 minutes
:throttled None Maximum run time of 5 minutes

To set a job’s urgency, use the urgency class method:

class HighUrgencyWorker
  include ApplicationWorker

  urgency :high

  # ...

Latency sensitive jobs

If a large number of background jobs get scheduled at once, queueing of jobs may occur while jobs wait for a worker node to be become available. This is normal and gives the system resilience by allowing it to gracefully handle spikes in traffic. Some jobs, however, are more sensitive to latency than others. Examples of these jobs include:

  1. A job which updates a merge request following a push to a branch.
  2. A job which invalidates a cache of known branches for a project after a push to the branch.
  3. A job which recalculates the groups and projects a user can see after a change in permissions.
  4. A job which updates the status of a CI pipeline after a state change to a job in the pipeline.

When these jobs are delayed, the user may perceive the delay as a bug: for example, they may push a branch and then attempt to create a merge request for that branch, but be told in the UI that the branch does not exist. We deem these jobs to be urgency :high.

Extra effort is made to ensure that these jobs are started within a very short period of time after being scheduled. However, in order to ensure throughput, these jobs also have very strict execution duration requirements:

  1. The median job execution time should be less than 1 second.
  2. 99% of jobs should complete within 10 seconds.

If a worker cannot meet these expectations, then it cannot be treated as a urgency :high worker: consider redesigning the worker, or splitting the work between two different workers, one with urgency :high code that executes quickly, and the other with urgency :low, which has no execution latency requirements (but also has lower scheduling targets).

Changing a queue’s urgency

On, we run Sidekiq in several shards, each of which represents a particular type of workload.

When changing a queue’s urgency, or adding a new queue, we need to take into account the expected workload on the new shard. Note that, if we’re changing an existing queue, there is also an effect on the old shard, but that will always be a reduction in work.

To do this, we want to calculate the expected increase in total execution time and RPS (throughput) for the new shard. We can get these values from:

  • The Queue Detail dashboard has values for the queue itself. For a new queue, we can look for queues that have similar patterns or are scheduled in similar circumstances.
  • The Shard Detail dashboard has Total Execution Time and Throughput (RPS). The Shard Utilization panel will show if there is currently any excess capacity for this shard.

We can then calculate the RPS * average runtime (estimated for new jobs) for the queue we’re changing to see what the relative increase in RPS and execution time we expect for the new shard:

new_queue_consumption = queue_rps * queue_duration_avg
shard_consumption = shard_rps * shard_duration_avg

(new_queue_consumption / shard_consumption) * 100

If we expect an increase of less than 5%, then no further action is needed.

Otherwise, please ping @gitlab-org/scalability on the merge request and ask for a review.

Jobs with External Dependencies

Most background jobs in the GitLab application communicate with other GitLab services. For example, PostgreSQL, Redis, Gitaly, and Object Storage. These are considered to be “internal” dependencies for a job.

However, some jobs will be dependent on external services in order to complete successfully. Some examples include:

  1. Jobs which call web-hooks configured by a user.
  2. Jobs which deploy an application to a k8s cluster configured by a user.

These jobs have “external dependencies”. This is important for the operation of the background processing cluster in several ways:

  1. Most external dependencies (such as web-hooks) do not provide SLOs, and therefore we cannot guarantee the execution latencies on these jobs. Since we cannot guarantee execution latency, we cannot ensure throughput and therefore, in high-traffic environments, we need to ensure that jobs with external dependencies are separated from high urgency jobs, to ensure throughput on those queues.
  2. Errors in jobs with external dependencies have higher alerting thresholds as there is a likelihood that the cause of the error is external.
class ExternalDependencyWorker
  include ApplicationWorker

  # Declares that this worker depends on
  # third-party, external services in order
  # to complete successfully

  # ...
Note: Note that a job cannot be both high urgency and have external dependencies.

CPU-bound and Memory-bound Workers

Workers that are constrained by CPU or memory resource limitations should be annotated with the worker_resource_boundary method.

Most workers tend to spend most of their time blocked, wait on network responses from other services such as Redis, PostgreSQL, and Gitaly. Since Sidekiq is a multi-threaded environment, these jobs can be scheduled with high concurrency.

Some workers, however, spend large amounts of time on-CPU running logic in Ruby. Ruby MRI does not support true multi-threading - it relies on the GIL to greatly simplify application development by only allowing one section of Ruby code in a process to run at a time, no matter how many cores the machine hosting the process has. For IO bound workers, this is not a problem, since most of the threads are blocked in underlying libraries (which are outside of the GIL).

If many threads are attempting to run Ruby code simultaneously, this will lead to contention on the GIL which will have the affect of slowing down all processes.

In high-traffic environments, knowing that a worker is CPU-bound allows us to run it on a different fleet with lower concurrency. This ensures optimal performance.

Likewise, if a worker uses large amounts of memory, we can run these on a bespoke low concurrency, high memory fleet.

Note that memory-bound workers create heavy GC workloads, with pauses of 10-50ms. This will have an impact on the latency requirements for the worker. For this reason, memory bound, urgency :high jobs are not permitted and will fail CI. In general, memory bound workers are discouraged, and alternative approaches to processing the work should be considered.

If a worker needs large amounts of both memory and CPU time, it should be marked as memory-bound, due to the above restriction on high urgency memory-bound workers.

Declaring a Job as CPU-bound

This example shows how to declare a job as being CPU-bound.

class CPUIntensiveWorker
  include ApplicationWorker

  # Declares that this worker will perform a lot of
  # calculations on-CPU.
  worker_resource_boundary :cpu

  # ...

Determining whether a worker is CPU-bound

We use the following approach to determine whether a worker is CPU-bound:

  • In the Sidekiq structured JSON logs, aggregate the worker duration and cpu_s fields.
  • duration refers to the total job execution duration, in seconds
  • cpu_s is derived from the Process::CLOCK_THREAD_CPUTIME_ID counter, and is a measure of time spent by the job on-CPU.
  • Divide cpu_s by duration to get the percentage time spend on-CPU.
  • If this ratio exceeds 33%, the worker is considered CPU-bound and should be annotated as such.
  • Note that these values should not be used over small sample sizes, but rather over fairly large aggregates.

Feature category

All Sidekiq workers must define a known feature category.

Job weights

Some jobs have a weight declared. This is only used when running Sidekiq in the default execution mode - using sidekiq-cluster does not account for weights.

As we are moving towards using sidekiq-cluster in Core, newly-added workers do not need to have weights specified. They can simply use the default weight, which is 1.

Worker context

To have some more information about workers in the logs, we add metadata to the jobs in the form of an ApplicationContext. In most cases, when scheduling a job from a request, this context will already be deducted from the request and added to the scheduled job.

When a job runs, the context that was active when it was scheduled will be restored. This causes the context to be propagated to any job scheduled from within the running job.

All this means that in most cases, to add context to jobs, we don’t need to do anything.

There are however some instances when there would be no context present when the job is scheduled, or the context that is present is likely to be incorrect. For these instances, we’ve added Rubocop rules to draw attention and avoid incorrect metadata in our logs.

As with most our cops, there are perfectly valid reasons for disabling them. In this case it could be that the context from the request is correct. Or maybe you’ve specified a context already in a way that isn’t picked up by the cops. In any case, leave a code comment pointing to which context will be used when disabling the cops.

When you do provide objects to the context, make sure that the route for namespaces and projects is pre-loaded. This can be done by using the .with_route scope defined on all Routables.

Cron workers

The context is automatically cleared for workers in the Cronjob queue (include CronjobQueue), even when scheduling them from requests. We do this to avoid incorrect metadata when other jobs are scheduled from the cron worker.

Cron workers themselves run instance wide, so they aren’t scoped to users, namespaces, projects, or other resources that should be added to the context.

However, they often schedule other jobs that do require context.

That is why there needs to be an indication of context somewhere in the worker. This can be done by using one of the following methods somewhere within the worker:

  1. Wrap the code that schedules jobs in the with_context helper:

      def perform
        deletion_cutoff = Gitlab::CurrentSettings
        projects = Project.with_route.with_namespace
        projects.find_each(batch_size: 100).with_index do |project, index|
          delay = index * INTERVAL
          with_context(project: project) do
  2. Use the a batch scheduling method that provides context:

      def schedule_projects_in_batch(projects)
          arguments_proc: -> (project) { },
          context_proc: -> (project) { { project: project } }

    Or, when scheduling with delays:

      diffs.each_batch(of: BATCH_SIZE) do |diffs, index|
          .bulk_perform_in_with_contexts(index *  5.minutes,
                                         arguments_proc: -> (diff) { },
                                         context_proc: -> (diff) { { project: diff.merge_request.target_project } })

Jobs scheduled in bulk

Often, when scheduling jobs in bulk, these jobs should have a separate context rather than the overarching context.

If that is the case, bulk_perform_async can be replaced by the bulk_perform_async_with_context helper, and instead of bulk_perform_in use bulk_perform_in_with_context.


      arguments_proc: -> (project) { },
      context_proc: -> (project) { { project: project } }

Each object from the enumerable in the first argument is yielded into 2 blocks:

  • The arguments_proc which needs to return the list of arguments the job needs to be scheduled with.

  • The context_proc which needs to return a hash with the context information for the job.

Arguments logging

When SIDEKIQ_LOG_ARGUMENTS is enabled, Sidekiq job arguments will be logged.

By default, the only arguments logged are numeric arguments, because arguments of other types could contain sensitive information. To override this, use loggable_arguments inside a worker with the indexes of the arguments to be logged. (Numeric arguments do not need to be specified here.)


class MyWorker
  include ApplicationWorker

  loggable_arguments 1, 3

  # object_id will be logged as it's numeric
  # string_a will be logged due to the loggable_arguments call
  # string_b will be filtered from logs
  # string_c will be logged due to the loggable_arguments call
  def perform(object_id, string_a, string_b, string_c)


Each Sidekiq worker must be tested using RSpec, just like any other class. These tests should be placed in spec/workers.

Sidekiq Compatibility across Updates

Keep in mind that the arguments for a Sidekiq job are stored in a queue while it is scheduled for execution. During a online update, this could lead to several possible situations:

  1. An older version of the application publishes a job, which is executed by an upgraded Sidekiq node.
  2. A job is queued before an upgrade, but executed after an upgrade.
  3. A job is queued by a node running the newer version of the application, but executed on a node running an older version of the application.

Changing the arguments for a worker

Jobs need to be backward and forward compatible between consecutive versions of the application. Adding or removing an argument may cause problems during deployment before all Rails and Sidekiq nodes have the updated code.

Remove an argument

Do not remove arguments from the perform function.. Instead, use the following approach:

  1. Provide a default value (usually nil) and use a comment to mark the argument as deprecated
  2. Stop using the argument in perform_async.
  3. Ignore the value in the worker class, but do not remove it until the next major release.

In the following example, if you want to remove arg2, first set a nil default value, and then update locations where ExampleWorker.perform_async is called.

class ExampleWorker
  def perform(object_id, arg1, arg2 = nil)
    # ...

Add an argument

There are two options for safely adding new arguments to Sidekiq workers:

  1. Set up a multi-step deployment in which the new argument is first added to the worker
  2. Use a parameter hash for additional arguments. This is perhaps the most flexible option.
Multi-step deployment

This approach requires multiple merge requests and for the first merge request to be merged and deployed before additional changes are merged.

  1. In an initial merge request, add the argument to the worker with a default value:

    class ExampleWorker
      def perform(object_id, new_arg = nil)
        # ...
  2. Merge and deploy the worker with the new argument.
  3. In a further merge request, update ExampleWorker.perform_async calls to use the new argument.
Parameter hash

This approach will not require multiple deployments if an existing worker already utilizes a parameter hash.

  1. Use a parameter hash in the worker to allow for future flexibility:

    class ExampleWorker
      def perform(object_id, params = {})
        # ...

Removing workers

Try to avoid removing workers and their queues in minor and patch releases.

During online update instance can have pending jobs and removing the queue can lead to those jobs being stuck forever. If you can’t write migration for those Sidekiq jobs, please consider removing the worker in a major release only.

Renaming queues

For the same reasons that removing workers is dangerous, care should be taken when renaming queues.

When renaming queues, use the sidekiq_queue_migrate helper migration method, as show in this example:

class MigrateTheRenamedSidekiqQueue < ActiveRecord::Migration[5.0]
  include Gitlab::Database::MigrationHelpers

  DOWNTIME = false

  def up
    sidekiq_queue_migrate 'old_queue_name', to: 'new_queue_name'

  def down
    sidekiq_queue_migrate 'new_queue_name', to: 'old_queue_name'