# 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 (e.g., on the GitLab CE issue tracker), create one if there isn't. See [#15607][#15607] for an example. 1. Measure the performance of the code in a production environment such as GitLab.com (see the [Tooling](#tooling) section below). Performance should be measured over a period of _at least_ 24 hours. 1. Add your findings based on the measurement period (screenshots of graphs, timings, etc) to the issue mentioned in step 1. 1. Solve the problem. 1. Create a merge request, assign the "Performance" label and assign it to [@yorickpeterse][yorickpeterse] for reviewing. 1. 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. 1. 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](profiling.md). - [Sherlock](profiling.md#sherlock). - [Distributed Tracing](distributed_tracing.md) - [GitLab Performance Monitoring](../administration/monitoring/performance/index.md). - [Request Profiling](../administration/monitoring/performance/request_profiling.md). - [QueryRecoder](query_recorder.md) for preventing `N+1` regressions. - [Chaos endpoints](chaos_endpoints.md) for testing failure scenarios. Intended mainly for testing availability. GitLab employees can use GitLab.com's performance monitoring systems located at , this requires you to log in using your `@gitlab.com` Email address. Non-GitLab employees are advised to set up their own InfluxDB + 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 (e.g., 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](https://github.com/evanphx/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 (e.g. 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: ```ruby 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 will actually have 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](https://github.com/tmm1/stackprof) gem is included in GitLab's development environment, allowing you to investigate the behaviour of suspect code in detail. It's important to note that profiling an application *alters its performance*, and will generally be done *in an unrepresentative environment*. In particular, a method is not necessarily troublesome just because it is 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, e.g.: ```sh $ 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: ```sh 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](https://kcachegrind.github.io/): ```sh stackprof tmp/project_policy_spec.dump --callgrind > project_policy_spec.callgrind kcachegrind project_policy_spec.callgrind # Linux qcachegrind project_policy_spec.callgrind # Mac ``` It may be useful to zoom in on a specific method, e.g.: ```sh $ 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 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 will tend to dominate. It's also possible to modify the application code in-place to output profiles whenever a particular code path is triggered without going through the test suite first. See the [StackProf documentation](https://github.com/tmm1/stackprof/blob/master/README.md) for details. ## RSpec profiling GitLab's development environment also includes the [rspec_profiling](https://github.com/foraker/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: ```sh 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: ```sh $ 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. ## Memory profiling 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](https://tenderlovemaking.com/2017/09/27/visualizing-your-ruby-heap.html). To start, you want to dump the heap of the process you are investigating to a JSON file. You need to run the command inside the process you are 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: ```ruby bundle exec rbtrace -p -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](https://gist.github.com/tenderlove/f28373d56fdd03d8b514af7191611b88) or similar: ```sh ruby heapviz.rb heap.json ``` Fragmented Ruby heap snapshot could look like this: ![Ruby heap fragmentation](img/memory_ruby_heap_fragmentation.png) Memory fragmentation could be reduced by tuning GC parameters as described in [this post by Nate Berkopec](https://www.speedshop.co/2017/12/04/malloc-doubles-ruby-memory.html), which 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 (e.g., the total time spent in a web request). 1. Ask others (preferably in the form of an issue). Some examples of changes that aren't 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](#string-freezing)). ## Slow Operations & Sidekiq Slow operations (e.g. 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 won't prevent the request from completing. 1. The process being slow won't affect the loading time of a page. 1. In case of a failure it's easy to re-try the process (Sidekiq takes care of this automatically). 1. By isolating the code from a web request it will hopefully 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: ```ruby if repository.exists? repository.branch_names.each do |name| ... end end ``` You can just write: ```ruby repository.branch_names.each do |name| ... end ``` ## Caching Operations that will 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 won't end up retrieving 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: ```ruby 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 will only allocate the "foo" String once: ```ruby 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 there's no guarantee it will. Strings will be frozen by default in Ruby 3.0. To prepare our code base for this eventuality, we will be adding the following header to all Ruby files: ```ruby # 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: ```ruby 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. ## Anti-Patterns This is a collection of [anti-patterns][anti-pattern] 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 there's no guarantee this will. 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: ```ruby 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: ```ruby 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, but since you have ActiveRecord models, you might find using these gems more convenient: - [BulkInsert gem](https://github.com/jamis/bulk_insert) - [ActiveRecord::PgGenerateSeries gem](https://github.com/ryu39/active_record-pg_generate_series) ### Examples You may find some useful examples in this snippet: [#15607]: https://gitlab.com/gitlab-org/gitlab-foss/issues/15607 [yorickpeterse]: https://gitlab.com/yorickpeterse [anti-pattern]: https://en.wikipedia.org/wiki/Anti-pattern