debian-mirror-gitlab/doc/development/sidekiq/worker_attributes.md

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# 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:

| **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:

```ruby
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 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.

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.
1. A job which invalidates a cache of known branches for a project after a push
   to the branch.
1. A job which recalculates the groups and projects a user can see after a
   change in permissions.
1. 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.
1. 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](https://dashboards.gitlab.net/d/sidekiq-shard-detail/sidekiq-shard-detail),
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 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](https://dashboards.gitlab.net/d/sidekiq-queue-detail/sidekiq-queue-detail)
  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](https://dashboards.gitlab.net/d/sidekiq-shard-detail/sidekiq-shard-detail)
  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:

```ruby
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 are dependent on external services to complete
successfully. Some examples include:

1. Jobs which call web-hooks configured by a user.
1. 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.
1. Errors in jobs with external dependencies have higher alerting thresholds as
   there is a likelihood that the cause of the error is external.

```ruby
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](https://thoughtbot.com/blog/untangling-ruby-threads#the-global-interpreter-lock)
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.

Note that memory-bound workers create heavy GC workloads, with pauses of
10-50ms. 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.

```ruby
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`](https://www.rubydoc.info/stdlib/core/Process:clock_gettime)
  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](../feature_categorization/index.md#sidekiq-workers).

## 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](../../administration/postgresql/database_load_balancing.md).
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.

When setting this field, consider the following trade-off:

- Ensure immediately consistent reads, but increase load on the primary database.
- Prefer read replicas to add relief to the primary, but increase the likelihood of stale reads that have to be retried.

To maintain the same behavior compared to before this field was introduced, set it to `:always`, so
database operations will only target the primary. Reasons for having to do so include workers
that mostly or exclusively perform writes, or workers that read their own writes and who might run
into data consistency issues should a stale record be read back from a replica. **Try to avoid
these scenarios, since `:always` should be considered the exception, not the rule.**

To allow for reads to be served from replicas, we added two additional consistency modes: `:sticky` and `:delayed`.

When you declare either `:sticky` or `:delayed` consistency, workers become eligible for database
load-balancing.

In both cases, if the replica is not up-to-date and the time from scheduling the job was less than the minimum delay interval,
 the jobs sleep up to the minimum delay interval (0.8 seconds). This gives the replication process time to finish.
The difference is in what happens when there is still replication lag after the delay: `sticky` workers
switch over to the primary right away, whereas `delayed` workers fail fast and are retried once.
If they still encounter replication lag, they also switch to the primary instead.
**If your worker never performs any writes, it is strongly advised to apply one of these consistency settings,
since it will never need to rely on the primary database node.**

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

| **Data Consistency**  | **Description**  |
|--------------|-----------------------------|
| `:always`    | The job is required to use the primary database (default). It should be used for workers that primarily perform writes or that have strict requirements around data consistency when reading their own writes. |
| `:sticky`    | The job prefers replicas, but switches to the primary for writes or when encountering replication lag. It should be used for jobs that require to be executed as fast as possible but can sustain a small initial queuing delay.  |
| `:delayed`   | The job prefers replicas, but switches to the primary for writes. When encountering replication lag before the job starts, the job is retried once. If the replica is still not up to date on the next retry, it switches to the primary. It should be used for jobs where delaying execution further typically does not matter, such as cache expiration or web hooks execution. |

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:

```ruby
class DelayedWorker
  include ApplicationWorker

  data_consistency :delayed

  # ...
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 will always use the primary database.

The `feature_flag` property does not allow the use of
[feature gates based on actors](../feature_flags/index.md).
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](../feature_flags/index.md).
Note that since we check the feature flag on both Sidekiq client and server, rolling out a 10% of the time,
will likely results in 1% (`0.1` `[from client]*0.1` `[from server]`) of effective jobs using replicas.

Example:

```ruby
class DelayedWorker
  include ApplicationWorker

  data_consistency :delayed, feature_flag: :load_balancing_for_delayed_worker

  # ...
end
```

### Data consistency with idempotent jobs

For [idempotent jobs](idempotent_jobs.md) that declare either `:sticky` or `:delayed` data consistency, we are
[preserving the latest WAL location](idempotent_jobs.md#preserve-the-latest-wal-location-for-idempotent-jobs) while deduplicating,
ensuring that we read from the replica that is fully caught up.