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Merge Request Performance Guidelines
To ensure a merge request does not negatively impact performance of GitLab every merge request must adhere to the guidelines outlined in this document. There are no exceptions to this rule unless specifically discussed with and agreed upon by merge request endbosses and performance specialists.
To measure the impact of a merge request you can use Sherlock. It's also highly recommended that you read the following guides:
Impact Analysis
Summary: think about the impact your merge request may have on performance and those maintaining a GitLab setup.
Any change submitted can have an impact not only on the application itself but also those maintaining it and those keeping it up and running (e.g. production engineers). As a result you should think carefully about the impact of your merge request on not only the application but also on the people keeping it up and running.
Can the queries used potentially take down any critical services and result in engineers being woken up in the night? Can a malicious user abuse the code to take down a GitLab instance? Will my changes simply make loading a certain page slower? Will execution time grow exponentially given enough load or data in the database?
These are all questions one should ask themselves before submitting a merge request. It may sometimes be difficult to assess the impact, in which case you should ask a performance specialist to review your code. See the "Reviewing" section below for more information.
Performance Review
Summary: ask performance specialists to review your code if you're not sure about the impact.
Sometimes it's hard to assess the impact of a merge request. In this case you should ask one of the merge request (mini) endbosses to review your changes. You can find a list of these endbosses at https://about.gitlab.com/team/. An endboss in turn can request a performance specialist to review the changes.
Query Counts
Summary: a merge request should not increase the number of executed SQL queries unless absolutely necessary.
The number of queries executed by the code modified or added by a merge request must not increase unless absolutely necessary. When building features it's entirely possible you will need some extra queries, but you should try to keep this at a minimum.
As an example, say you introduce a feature that updates a number of database rows with the same value. It may be very tempting (and easy) to write this using the following pseudo code:
objects_to_update.each do |object|
object.some_field = some_value
object.save
end
This will end up running one query for every object to update. This code can easily overload a database given enough rows to update or many instances of this code running in parallel. This particular problem is known as the "N+1 query problem".
In this particular case the workaround is fairly easy:
objects_to_update.update_all(some_field: some_value)
This uses ActiveRecord's update_all method to update all rows in a single
query. This in turn makes it much harder for this code to overload a database.
Executing Queries in Loops
Summary: SQL queries must not be executed in a loop unless absolutely necessary.
Executing SQL queries in a loop can result in many queries being executed depending on the number of iterations in a loop. This may work fine for a development environment with little data, but in a production environment this can quickly spiral out of control.
There are some cases where this may be needed. If this is the case this should be clearly mentioned in the merge request description.
Eager Loading
Summary: always eager load associations when retrieving more than one row.
When retrieving multiple database records for which you need to use any associations you must eager load these associations. For example, if you're retrieving a list of blog posts and you want to display their authors you must eager load the author associations.
In other words, instead of this:
Post.all.each do |post|
puts post.author.name
end
You should use this:
Post.all.includes(:author).each do |post|
puts post.author.name
end
Memory Usage
Summary: merge requests must not increase memory usage unless absolutely necessary.
A merge request must not increase the memory usage of GitLab by more than the absolute bare minimum required by the code. This means that if you have to parse some large document (e.g. an HTML document) it's best to parse it as a stream whenever possible, instead of loading the entire input into memory. Sometimes this isn't possible, in that case this should be stated explicitly in the merge request.
Lazy Rendering of UI Elements
Summary: only render UI elements when they're actually needed.
Certain UI elements may not always be needed. For example, when hovering over a diff line there's a small icon displayed that can be used to create a new comment. Instead of always rendering these kind of elements they should only be rendered when actually needed. This ensures we don't spend time generating Haml/HTML when it's not going to be used.
Instrumenting New Code
Summary: always add instrumentation for new classes, modules, and methods.
Newly added classes, modules, and methods must be instrumented. This ensures we can track the performance of this code over time.
For more information see Instrumentation. This guide describes how to add instrumentation and where to add it.
Use of Caching
Summary: cache data in memory or in Redis when it's needed multiple times in a transaction or has to be kept around for a certain time period.
Sometimes certain bits of data have to be re-used in different places during a transaction. In these cases this data should be cached in memory to remove the need for running complex operations to fetch the data. You should use Redis if data should be cached for a certain time period instead of the duration of the transaction.
For example, say you process multiple snippets of text containiner username
mentions (e.g. Hello @alice and How are you doing @alice?). By caching the
user objects for every username we can remove the need for running the same
query for every mention of @alice.
Caching data per transaction can be done using RequestStore. Caching data in Redis can be done using Rails' caching system.