• Job urgency
  • Latency sensitive jobs
  • Changing a queue’s urgency
• Jobs with External Dependencies
• CPU-bound and Memory-bound Workers
• Declaring a Job as CPU-bound
• Determining whether a worker is CPU-bound
• Feature category
• Job data consistency strategies
  • Trading immediacy for reduced primary load
  • Overriding data consistency for a decomposed database
  • feature_flag property
  • Data consistency with idempotent jobs
• Job pause control
• Concurrency limit
• Skip execution of workers in Geo secondary

Sidekiq worker attributes

Worker classes can define certain attributes to control their behavior and add metadata.

Child classes inheriting from other workers also inherit these attributes, so you only have to redefine them if you want to override their values.

Job urgency

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

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

class HighUrgencyWorker
  include ApplicationWorker

  urgency :high

  # ...
end

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 standard and gives the system resilience by allowing it to gracefully handle spikes in traffic. Some jobs, however, are more sensitive to latency than others.

In general, latency-sensitive jobs perform operations that a user could reasonably expect to happen synchronously, rather than asynchronously in a background worker. A common example is a write following an action. 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, 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 GitLab.com, 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. If we’re changing an existing queue, there is also an effect on the old shard, but that always reduces 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 displays 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, 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 are dependent on external services to complete successfully. Some examples include:

1. Jobs which call web-hooks configured by a user.
2. Jobs which deploy an application to a Kubernetes 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
  worker_has_external_dependencies!

  # ...
end

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, waiting 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 leads to contention on the GIL which has the effect 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.

Memory-bound workers create heavy GC workloads, with pauses of 10-50 ms. This has an impact on the latency requirements for the worker. For this reason, memory bound, urgency :high jobs are not permitted and 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

  # ...
end

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.
• 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 data consistency strategies

In GitLab 13.11 and earlier, Sidekiq workers would always send database queries to the primary database node, both for reads and writes. This ensured that data integrity is both guaranteed and immediate, since in a single-node scenario it is impossible to encounter stale reads even for workers that read their own writes. If a worker writes to the primary, but reads from a replica, however, the possibility of reading a stale record is non-zero due to replicas potentially lagging behind the primary.

When the number of jobs that rely on the database increases, ensuring immediate data consistency can put unsustainable load on the primary database server. We therefore added the ability to use Database Load Balancing for Sidekiq workers. By configuring a worker’s data_consistency field, we can then allow the scheduler to target read replicas under several strategies outlined below.

Trading immediacy for reduced primary load

We require Sidekiq workers to make an explicit decision around whether they need to use the primary database node for all reads and writes, or whether reads can be served from replicas. This is enforced by a RuboCop rule, which ensures that the data_consistency field is set.

Before data_consistency was introduced, the default behavior mimicked that of :always. Since jobs are now enqueued along with the current database LSN, the replica (for :sticky or :delayed) is guaranteed to be caught up to that point, or the job will be retried, or use the primary. This means that the data will be consistent at least to the point at which the job was enqueued.

The table below shows the data_consistency attribute and its values, ordered by the degree to which they prefer read replicas and wait for replicas to catch up:

In all cases workers read either from a replica that is fully caught up, or from the primary node, so data consistency is always ensured.

To set a data consistency for a worker, use the data_consistency class method:

class DelayedWorker
  include ApplicationWorker

  data_consistency :delayed

  # ...
end

Overriding data consistency for a decomposed database

GitLab uses multiple decomposed databases. Sidekiq workers usage of the respective databases may be skewed towards a particular database. For example, PipelineProcessWorker has a higher write traffic to the ci database compared to the main database. In the event of edge cases around primary stickiness, having separate data consistency defined for each database allows the worker to more efficiently use read replicas.

If the overrides keyword argument is set, the Gitlab::Database::LoadBalancing::SidekiqServerMiddleware loads the load balancing strategy using the data consistency which most prefers the read replicas. The order of preference in increasing preference is: :always, :sticky, then :delayed.

The overrides only apply if the GitLab instance is using multiple databases or Gitlab::Database.database_mode == Gitlab::Database::MODE_MULTIPLE_DATABASES.

To set a data consistency for a worker, use the data_consistency class method with the overrides keyword argument:

class MultipleDataConsistencyWorker
  include ApplicationWorker

  data_consistency :always, overrides: { ci: :sticky }

  # ...
end

feature_flag property

The feature_flag property allows you to toggle a job’s data_consistency, which permits you to safely toggle load balancing capabilities for a specific job. When feature_flag is disabled, the job defaults to :always, which means that the job always uses the primary database.

The feature_flag property does not allow the use of feature gates based on actors. This means that the feature flag cannot be toggled only for particular projects, groups, or users, but instead, you can safely use percentage of time rollout. Since we check the feature flag on both Sidekiq client and server, rolling out a 10% of the time, likely results in 1% (0.1 [from client]*0.1 [from server]) of effective jobs using replicas.

Example:

class DelayedWorker
  include ApplicationWorker

  data_consistency :delayed, feature_flag: :load_balancing_for_delayed_worker

  # ...
end

Data consistency with idempotent jobs

For idempotent jobs that declare either :sticky or :delayed data consistency, we are preserving the latest WAL location while deduplicating, ensuring that we read from the replica that is fully caught up.

Job pause control

With the pause_control property, you can conditionally pause job processing. If the strategy is active, the job is stored in a separate ZSET and re-enqueued when the strategy becomes inactive. PauseControl::ResumeWorker is a cron worker that checks if any paused jobs must be restarted.

To use pause_control, you can:

• Use one of the strategies defined in lib/gitlab/sidekiq_middleware/pause_control/strategies/.
• Define a custom strategy in lib/gitlab/sidekiq_middleware/pause_control/strategies/ and add the strategy to lib/gitlab/sidekiq_middleware/pause_control.rb.

For example:

module Gitlab
  module SidekiqMiddleware
    module PauseControl
      module Strategies
        class CustomStrategy < Base
          def should_pause?
            ApplicationSetting.current.elasticsearch_pause_indexing?
          end
        end
      end
    end
  end
end

class PausedWorker
  include ApplicationWorker

  pause_control :custom_strategy

  # ...
end

Concurrency limit

With the concurrency_limit property, you can limit the worker’s concurrency. It will put the jobs that are over this limit in a separate LIST and re-enqueued when it falls under the limit. ConcurrencyLimit::ResumeWorker is a cron worker that checks if any throttled jobs should be re-enqueued.

The first job that crosses the defined concurrency limit initiates the throttling process for all other jobs of this class. Until this happens, jobs are scheduled and executed as usual.

When the throttling starts, newly scheduled and executed jobs will be added to the end of the LIST to ensure that the execution order is preserved. As soon as the LIST is empty again, the throttling process ends.

Prometheus metrics are exposed to monitor workers using concurrency limit middleware:

• sidekiq_concurrency_limit_deferred_jobs_total
• sidekiq_concurrency_limit_queue_jobs
• sidekiq_concurrency_limit_queue_jobs_total
• sidekiq_concurrency_limit_max_concurrent_jobs
• sidekiq_concurrency_limit_current_concurrent_jobs_total

You should use a lambda to define the limit. If it returns nil or 0, the limit won’t be applied. Negative numbers pause the execution.

class LimitedWorker
  include ApplicationWorker

  concurrency_limit -> { 60 }

  # ...
end

class LimitedWorker
  include ApplicationWorker

  concurrency_limit -> { ApplicationSetting.current.elasticsearch_concurrent_sidekiq_jobs }

  # ...
end

Skip execution of workers in Geo secondary

On Geo secondary sites, database writes are disabled. You must skip execution of workers that attempt database writes from Geo secondary sites, if those workers get enqueued on Geo secondary sites. Conveniently, most workers do not get enqueued on Geo secondary sites, because most non-GET HTTP requests get proxied to the Geo primary site, and because Geo secondary sites disable most Sidekiq-Cron jobs. Ask a Geo engineer if you are unsure. To skip execution, prepend the ::Geo::SkipSecondary module to the worker class.

class DummyWorker
  include ApplicationWorker
  prepend ::Geo::SkipSecondary

 # ...
end