Performance Guidelines

This document describes various guidelines to follow to ensure good and consistent performance of GitLab.

Workflow

The process of solving performance problems is roughly as follows:

1. Make sure there's an issue open somewhere (for example, on the GitLab CE issue tracker), and create one if there is not. See #15607 for an example.
2. Measure the performance of the code in a production environment such as GitLab.com (see the Tooling section below). Performance should be measured over a period of at least 24 hours.
3. Add your findings based on the measurement period (screenshots of graphs, timings, etc) to the issue mentioned in step 1.
4. Solve the problem.
5. Create a merge request, assign the "Performance" label and follow the performance review process.
6. Once a change has been deployed make sure to again measure for at least 24 hours to see if your changes have any impact on the production environment.
7. Repeat until you're done.

When providing timings make sure to provide:

• The 95th percentile
• The 99th percentile
• The mean

When providing screenshots of graphs, make sure that both the X and Y axes and the legend are clearly visible. If you happen to have access to GitLab.com's own monitoring tools you should also provide a link to any relevant graphs/dashboards.

Tooling

GitLab provides built-in tools to help improve performance and availability:

• Profiling.
• Distributed Tracing
• GitLab Performance Monitoring.
• Request Profiling.
• QueryRecoder for preventing N+1 regressions.
• Chaos endpoints for testing failure scenarios. Intended mainly for testing availability.
• Service measurement for measuring and logging service execution.

GitLab team members can use GitLab.com's performance monitoring systems located at https://dashboards.gitlab.net, this requires you to log in using your @gitlab.com email address. Non-GitLab team-members are advised to set up their own Prometheus and Grafana stack.

Benchmarks

Benchmarks are almost always useless. Benchmarks usually only test small bits of code in isolation and often only measure the best case scenario. On top of that, benchmarks for libraries (such as a Gem) tend to be biased in favour of the library. After all there's little benefit to an author publishing a benchmark that shows they perform worse than their competitors.

Benchmarks are only really useful when you need a rough (emphasis on "rough") understanding of the impact of your changes. For example, if a certain method is slow a benchmark can be used to see if the changes you're making have any impact on the method's performance. However, even when a benchmark shows your changes improve performance there's no guarantee the performance also improves in a production environment.

When writing benchmarks you should almost always use benchmark-ips. Ruby's Benchmark module that comes with the standard library is rarely useful as it runs either a single iteration (when using Benchmark.bm) or two iterations (when using Benchmark.bmbm). Running this few iterations means external factors, such as a video streaming in the background, can very easily skew the benchmark statistics.

Another problem with the Benchmark module is that it displays timings, not iterations. This means that if a piece of code completes in a very short period of time it can be very difficult to compare the timings before and after a certain change. This in turn leads to patterns such as the following:

Benchmark.bmbm(10) do |bench|
  bench.report 'do something' do
    100.times do
      ... work here ...
    end
  end
end

This however leads to the question: how many iterations should we run to get meaningful statistics?

The benchmark-ips Gem basically takes care of all this and much more, and as a result of this should be used instead of the Benchmark module.

In short:

• Don't trust benchmarks you find on the internet.
• Never make claims based on just benchmarks, always measure in production to confirm your findings.
• X being N times faster than Y is meaningless if you don't know what impact it has on your production environment.
• A production environment is the only benchmark that always tells the truth (unless your performance monitoring systems are not set up correctly).
• If you must write a benchmark use the benchmark-ips Gem instead of Ruby's Benchmark module.

Profiling

By collecting snapshots of process state at regular intervals, profiling allows you to see where time is spent in a process. The Stackprof gem is included in GitLab, allowing you to profile which code is running on CPU in detail.

It's important to note that profiling an application alters its performance. Different profiling strategies have different overheads. Stackprof is a sampling profiler. It samples stack traces from running threads at a configurable frequency (e.g. 100hz, that is 100 stacks per second). This type of profiling has quite a low (albeit non-zero) overhead and is generally considered to be safe for production.

Development

A profiler can be a very useful tool during development, even if it does run in an unrepresentative environment. In particular, a method is not necessarily troublesome just because it's executed many times, or takes a long time to execute. Profiles are tools you can use to better understand what is happening in an application - using that information wisely is up to you!

Keeping that in mind, to create a profile, identify (or create) a spec that exercises the troublesome code path, then run it using the bin/rspec-stackprof helper, for example:

$ LIMIT=10 bin/rspec-stackprof spec/policies/project_policy_spec.rb

8/8 |====== 100 ======>| Time: 00:00:18

Finished in 18.19 seconds (files took 4.8 seconds to load)
8 examples, 0 failures

==================================
 Mode: wall(1000)
 Samples: 17033 (5.59% miss rate)
 GC: 1901 (11.16%)
==================================
    TOTAL    (pct)     SAMPLES    (pct)     FRAME
     6000  (35.2%)        2566  (15.1%)     Sprockets::Cache::FileStore#get
     2018  (11.8%)         888   (5.2%)     ActiveRecord::ConnectionAdapters::PostgreSQLAdapter#exec_no_cache
     1338   (7.9%)         640   (3.8%)     ActiveRecord::ConnectionAdapters::PostgreSQL::DatabaseStatements#execute
     3125  (18.3%)         394   (2.3%)     Sprockets::Cache::FileStore#safe_open
      913   (5.4%)         301   (1.8%)     ActiveRecord::ConnectionAdapters::PostgreSQLAdapter#exec_cache
      288   (1.7%)         288   (1.7%)     ActiveRecord::Attribute#initialize
      246   (1.4%)         246   (1.4%)     Sprockets::Cache::FileStore#safe_stat
      295   (1.7%)         193   (1.1%)     block (2 levels) in class_attribute
      187   (1.1%)         187   (1.1%)     block (4 levels) in class_attribute

You can limit the specs that are run by passing any arguments rspec would normally take.

The output is sorted by the Samples column by default. This is the number of samples taken where the method is the one currently being executed. The Total column shows the number of samples taken where the method, or any of the methods it calls, were being executed.

To create a graphical view of the call stack:

stackprof tmp/project_policy_spec.rb.dump --graphviz > project_policy_spec.dot
dot -Tsvg project_policy_spec.dot > project_policy_spec.svg

To load the profile in kcachegrind:

stackprof tmp/project_policy_spec.rb.dump --callgrind > project_policy_spec.callgrind
kcachegrind project_policy_spec.callgrind # Linux
qcachegrind project_policy_spec.callgrind # Mac

For flamegraphs, enable raw collection first. Note that raw collection can generate a very large file, so increase the INTERVAL, or run on a smaller number of specs for smaller file size:

RAW=true bin/rspec-stackprof spec/policies/group_member_policy_spec.rb

You can then generate, and view the resultant flamegraph. It might take a while to generate based on the output file size:

# Generate
stackprof --flamegraph tmp/group_member_policy_spec.rb.dump > group_member_policy_spec.flame

# View
stackprof --flamegraph-viewer=group_member_policy_spec.flame

It may be useful to zoom in on a specific method, for example:

$ stackprof tmp/project_policy_spec.rb.dump --method warm_asset_cache

TestEnv#warm_asset_cache (/Users/lupine/dev/gitlab.com/gitlab-org/gitlab-development-kit/gitlab/spec/support/test_env.rb:164)
  samples:     0 self (0.0%)  /   6288 total (36.9%)
  callers:
    6288  (  100.0%)  block (2 levels) in <top (required)>
  callees (6288 total):
    6288  (  100.0%)  Capybara::RackTest::Driver#visit
  code:
                                  |   164  |   def warm_asset_cache
                                  |   165  |     return if warm_asset_cache?
                                  |   166  |     return unless defined?(Capybara)
                                  |   167  |
 6288   (36.9%)                   |   168  |     Capybara.current_session.driver.visit '/'
                                  |   169  |   end
$ stackprof tmp/project_policy_spec.rb.dump --method BasePolicy#abilities
BasePolicy#abilities (/Users/lupine/dev/gitlab.com/gitlab-org/gitlab-development-kit/gitlab/app/policies/base_policy.rb:79)
  samples:     0 self (0.0%)  /     50 total (0.3%)
  callers:
      25  (   50.0%)  BasePolicy.abilities
      25  (   50.0%)  BasePolicy#collect_rules
  callees (50 total):
      25  (   50.0%)  ProjectPolicy#rules
      25  (   50.0%)  BasePolicy#collect_rules
  code:
                                  |    79  |   def abilities
                                  |    80  |     return RuleSet.empty if @user && @user.blocked?
                                  |    81  |     return anonymous_abilities if @user.nil?
   50    (0.3%)                   |    82  |     collect_rules { rules }
                                  |    83  |   end

Since the profile includes the work done by the test suite as well as the application code, these profiles can be used to investigate slow tests as well. However, for smaller runs (like this example), this means that the cost of setting up the test suite tends to dominate.

Production

Stackprof can also be used to profile production workloads.

In order to enable production profiling for Ruby processes, you can set the STACKPROF_ENABLED environment variable to true.

The following configuration options can be configured:

• STACKPROF_ENABLED: Enables stackprof signal handler on SIGUSR2 signal. Defaults to false.
• STACKPROF_MODE: See sampling modes. Defaults to cpu.
• STACKPROF_INTERVAL: Sampling interval. Unit semantics depend on STACKPROF_MODE. For object mode this is a per-event interval (every nth event is sampled) and defaults to 1000. For other modes such as cpu this is a frequency and defaults to 10000 μs (100hz).
• STACKPROF_FILE_PREFIX: File path prefix where profiles are stored. Defaults to $TMPDIR (often corresponds to /tmp).
• STACKPROF_TIMEOUT_S: Profiling timeout in seconds. Profiling will automatically stop after this time has elapsed. Defaults to 30.
• STACKPROF_RAW: Whether to collect raw samples or only aggregates. Raw samples are needed to generate flamegraphs, but they do have a higher memory and disk overhead. Defaults to true.

Once enabled, profiling can be triggered by sending a SIGUSR2 signal to the Ruby process. The process begins sampling stacks. Profiling can be stopped by sending another SIGUSR2. Alternatively, it stops automatically after the timeout.

Once profiling stops, the profile is written out to disk at $STACKPROF_FILE_PREFIX/stackprof.$PID.$RAND.profile. It can then be inspected further via the stackprof command line tool, as described in the previous section.

Currently supported profiling targets are:

• Puma worker
• Sidekiq

NOTE: The Puma master process is not supported. Neither is Unicorn. Sending SIGUSR2 to either of those triggers restarts. In the case of Puma, take care to only send the signal to Puma workers.

This can be done via pkill -USR2 puma:. The : disambiguates between puma 4.3.3.gitlab.2 ... (the master process) from puma: cluster worker 0: ... (the worker processes), selecting the latter.

For Sidekiq, the signal can be sent to the sidekiq-cluster process via pkill -USR2 bin/sidekiq-cluster, which forwards the signal to all Sidekiq children. Alternatively, you can also select a specific pid of interest.

Production profiles can be especially noisy. It can be helpful to visualize them as a flamegraph. This can be done via:

bundle exec stackprof --stackcollapse /tmp/stackprof.55769.c6c3906452.profile | flamegraph.pl > flamegraph.svg

RSpec profiling

The GitLab development environment also includes the rspec_profiling gem, which is used to collect data on spec execution times. This is useful for analyzing the performance of the test suite itself, or seeing how the performance of a spec may have changed over time.

To activate profiling in your local environment, run the following:

export RSPEC_PROFILING=yes
rake rspec_profiling:install

This creates an SQLite3 database in tmp/rspec_profiling, into which statistics are saved every time you run specs with the RSPEC_PROFILING environment variable set.

Ad-hoc investigation of the collected results can be performed in an interactive shell:

$ rake rspec_profiling:console

irb(main):001:0> results.count
=> 231
irb(main):002:0> results.last.attributes.keys
=> ["id", "commit", "date", "file", "line_number", "description", "time", "status", "exception", "query_count", "query_time", "request_count", "request_time", "created_at", "updated_at"]
irb(main):003:0> results.where(status: "passed").average(:time).to_s
=> "0.211340155844156"

These results can also be placed into a PostgreSQL database by setting the RSPEC_PROFILING_POSTGRES_URL variable. This is used to profile the test suite when running in the CI environment.

We store these results also when running nightly scheduled CI jobs on the default branch on gitlab.com. Statistics of these profiling data are available online. For example, you can find which tests take longest to run or which execute the most queries. This can be handy for optimizing our tests or identifying performance issues in our code.

Memory profiling

We can use two approaches, often in combination, to track down memory issues:

• Leaving the code intact and wrapping a profiler around it.
• Monitor memory usage of the process while disabling/enabling different parts of the code we suspect could be problematic.

Using Memory Profiler

We can use memory_profiler for profiling.

The memory_profiler gem is already present in the GitLab Gemfile, you just need to require it:

require 'sidekiq/testing'

report = MemoryProfiler.report do
  # Code you want to profile
end

output = File.open('/tmp/profile.txt','w')
report.pretty_print(output)

The report breaks down 2 key concepts:

• Retained: long lived memory use and object count retained due to the execution of the code block.
• Allocated: all object allocation and memory allocation during code block.

As a general rule, retained is always smaller than or equal to allocated.

The actual RSS cost is always slightly higher as MRI heaps are not squashed to size and memory fragments.

Rbtrace

One of the reasons of the increased memory footprint could be Ruby memory fragmentation.

To diagnose it, you can visualize Ruby heap as described in this post by Aaron Patterson.

To start, you want to dump the heap of the process you're investigating to a JSON file.

You need to run the command inside the process you're exploring, you may do that with rbtrace. rbtrace is already present in GitLab Gemfile, you just need to require it. It could be achieved running webserver or Sidekiq with the environment variable set to ENABLE_RBTRACE=1.

To get the heap dump:

bundle exec rbtrace -p <PID> -e 'File.open("heap.json", "wb") { |t| ObjectSpace.dump_all(output: t) }'

Having the JSON, you finally could render a picture using the script provided by Aaron or similar:

ruby heapviz.rb heap.json

Fragmented Ruby heap snapshot could look like this:

Memory fragmentation could be reduced by tuning GC parameters as described in this post by Nate Berkopec. This should be considered as a tradeoff, as it may affect overall performance of memory allocation and GC cycles.

Importance of Changes

When working on performance improvements, it's important to always ask yourself the question "How important is it to improve the performance of this piece of code?". Not every piece of code is equally important and it would be a waste to spend a week trying to improve something that only impacts a tiny fraction of our users. For example, spending a week trying to squeeze 10 milliseconds out of a method is a waste of time when you could have spent a week squeezing out 10 seconds elsewhere.

There is no clear set of steps that you can follow to determine if a certain piece of code is worth optimizing. The only two things you can do are:

1. Think about what the code does, how it's used, how many times it's called and how much time is spent in it relative to the total execution time (for example, the total time spent in a web request).
2. Ask others (preferably in the form of an issue).

Some examples of changes that are not really important/worth the effort:

• Replacing double quotes with single quotes.
• Replacing usage of Array with Set when the list of values is very small.
• Replacing library A with library B when both only take up 0.1% of the total execution time.
• Calling freeze on every string (see String Freezing).

Slow Operations & Sidekiq

Slow operations, like merging branches, or operations that are prone to errors (using external APIs) should be performed in a Sidekiq worker instead of directly in a web request as much as possible. This has numerous benefits such as:

1. An error doesn't prevent the request from completing.
2. The process being slow doesn't affect the loading time of a page.
3. In case of a failure you can retry the process (Sidekiq takes care of this automatically).
4. By isolating the code from a web request it should be easier to test and maintain.

It's especially important to use Sidekiq as much as possible when dealing with Git operations as these operations can take quite some time to complete depending on the performance of the underlying storage system.

Git Operations

Care should be taken to not run unnecessary Git operations. For example, retrieving the list of branch names using Repository#branch_names can be done without an explicit check if a repository exists or not. In other words, instead of this:

if repository.exists?
  repository.branch_names.each do |name|
    ...
  end
end

You can just write:

repository.branch_names.each do |name|
  ...
end

Caching

Operations that often return the same result should be cached using Redis, in particular Git operations. When caching data in Redis, make sure the cache is flushed whenever needed. For example, a cache for the list of tags should be flushed whenever a new tag is pushed or a tag is removed.

When adding cache expiration code for repositories, this code should be placed in one of the before/after hooks residing in the Repository class. For example, if a cache should be flushed after importing a repository this code should be added to Repository#after_import. This ensures the cache logic stays within the Repository class instead of leaking into other classes.

When caching data, make sure to also memoize the result in an instance variable. While retrieving data from Redis is much faster than raw Git operations, it still has overhead. By caching the result in an instance variable, repeated calls to the same method don't retrieve data from Redis upon every call. When memoizing cached data in an instance variable, make sure to also reset the instance variable when flushing the cache. An example:

def first_branch
  @first_branch ||= cache.fetch(:first_branch) { branches.first }
end

def expire_first_branch_cache
  cache.expire(:first_branch)
  @first_branch = nil
end

String Freezing

In recent Ruby versions calling freeze on a String leads to it being allocated only once and re-used. For example, on Ruby 2.3 or later this only allocates the "foo" String once:

10.times do
  'foo'.freeze
end

Depending on the size of the String and how frequently it would be allocated (before the .freeze call was added), this may make things faster, but this isn't guaranteed.

Strings are frozen by default in Ruby 3.0. To prepare our codebase for this eventuality, we are adding the following header to all Ruby files:

# frozen_string_literal: true

This may cause test failures in the code that expects to be able to manipulate strings. Instead of using dup, use the unary plus to get an unfrozen string:

test = +"hello"
test += " world"

When adding new Ruby files, please check that you can add the above header, as omitting it may lead to style check failures.

Reading from files and other data sources

Ruby offers several convenience functions that deal with file contents specifically or I/O streams in general. Functions such as IO.read and IO.readlines make it easy to read data into memory, but they can be inefficient when the data grows large. Because these functions read the entire contents of a data source into memory, memory use grows by at least the size of the data source. In the case of readlines, it grows even further, due to extra bookkeeping the Ruby VM has to perform to represent each line.

Consider the following program, which reads a text file that is 750MB on disk:

File.readlines('large_file.txt').each do |line|
  puts line
end

Here is a process memory reading from while the program was running, showing how we indeed kept the entire file in memory (RSS reported in kilobytes):

$ ps -o rss -p <pid>

RSS
783436

And here is an excerpt of what the garbage collector was doing:

pp GC.stat

{
 :heap_live_slots=>2346848,
 :malloc_increase_bytes=>30895288,
 ...
}

We can see that heap_live_slots (the number of reachable objects) jumped to ~2.3M, which is roughly two orders of magnitude more compared to reading the file line by line instead. It was not just the raw memory usage that increased, but also how the garbage collector (GC) responded to this change in anticipation of future memory use. We can see that malloc_increase_bytes jumped to ~30MB, which compares to just ~4kB for a "fresh" Ruby program. This figure specifies how much additional heap space the Ruby GC claims from the operating system next time it runs out of memory. Not only did we occupy more memory, we also changed the behavior of the application to increase memory use at a faster rate.

The IO.read function exhibits similar behavior, with the difference that no extra memory is allocated for each line object.

Recommendations

Instead of reading data sources into memory in full, it is better to read them line by line instead. This is not always an option, for instance when you need to convert a YAML file into a Ruby Hash, but whenever you have data where each row represents some entity that can be processed and then discarded, you can use the following approaches.

First, replace calls to readlines.each with either each or each_line. The each_line and each functions read the data source line by line without keeping already visited lines in memory:

File.new('file').each { |line| puts line }

Alternatively, you can read individual lines explicitly using IO.readline or IO.gets functions:

while line = file.readline
   # process line
end

This might be preferable if there is a condition that allows exiting the loop early, saving not just memory but also unnecessary time spent in CPU and I/O for processing lines you're not interested in.

Anti-Patterns

This is a collection of anti-patterns that should be avoided unless these changes have a measurable, significant, and positive impact on production environments.

Moving Allocations to Constants

Storing an object as a constant so you only allocate it once may improve performance, but this is not guaranteed. Looking up constants has an impact on runtime performance, and as such, using a constant instead of referencing an object directly may even slow code down. For example:

SOME_CONSTANT = 'foo'.freeze

9000.times do
  SOME_CONSTANT
end

The only reason you should be doing this is to prevent somebody from mutating the global String. However, since you can just re-assign constants in Ruby there's nothing stopping somebody from doing this elsewhere in the code:

SOME_CONSTANT = 'bar'

How to seed a database with millions of rows

You might want millions of project rows in your local database, for example, in order to compare relative query performance, or to reproduce a bug. You could do this by hand with SQL commands or using Mass Inserting Rails Models functionality.

Assuming you are working with ActiveRecord models, you might also find these links helpful:

• Insert records in batches
• BulkInsert gem
• ActiveRecord::PgGenerateSeries gem

Examples

You may find some useful examples in this snippet: https://gitlab.com/gitlab-org/gitlab-foss/snippets/33946