--- stage: none group: unassigned info: To determine the technical writer assigned to the Stage/Group associated with this page, see https://about.gitlab.com/handbook/product/ux/technical-writing/#assignments --- # Profiling To make it easier to track down performance problems GitLab comes with a set of profiling tools, some of these are available by default while others need to be explicitly enabled. ## Profiling a URL There is a `Gitlab::Profiler.profile` method, and corresponding `bin/profile-url` script, that enable profiling a GET or POST request to a specific URL, either as an anonymous user (the default) or as a specific user. The first argument to the profiler is either a full URL (including the instance hostname) or an absolute path, including the leading slash. When using the script, command-line documentation is available by passing no arguments. When using the method in an interactive console session, any changes to the application code within that console session is reflected in the profiler output. For example: ```ruby Gitlab::Profiler.profile('/my-user') # Returns a RubyProf::Profile for the regular operation of this request class UsersController; def show; sleep 100; end; end Gitlab::Profiler.profile('/my-user') # Returns a RubyProf::Profile where 100 seconds is spent in UsersController#show ``` For routes that require authorization you must provide a user to `Gitlab::Profiler`. You can do this like so: ```ruby Gitlab::Profiler.profile('/gitlab-org/gitlab-test', user: User.first) ``` Passing a `logger:` keyword argument to `Gitlab::Profiler.profile` sends ActiveRecord and ActionController log output to that logger. Further options are documented with the method source. ```ruby Gitlab::Profiler.profile('/gitlab-org/gitlab-test', user: User.first, logger: Logger.new($stdout)) ``` There is also a RubyProf printer available: `Gitlab::Profiler::TotalTimeFlatPrinter`. This acts like `RubyProf::FlatPrinter`, but its `min_percent` option works on the method's total time, not its self time. (This is because we often spend most of our time in library code, but this comes from calls in our application.) It also offers a `max_percent` option to help filter out outer calls that aren't useful (like `ActionDispatch::Integration::Session#process`). There is a convenience method for using this, `Gitlab::Profiler.print_by_total_time`: ```ruby result = Gitlab::Profiler.profile('/my-user') Gitlab::Profiler.print_by_total_time(result, max_percent: 60, min_percent: 2) # Measure Mode: wall_time # Thread ID: 70005223698240 # Fiber ID: 70004894952580 # Total: 1.768912 # Sort by: total_time # # %self total self wait child calls name # 0.00 1.017 0.000 0.000 1.017 14 *ActionView::Helpers::RenderingHelper#render # 0.00 1.017 0.000 0.000 1.017 14 *ActionView::Renderer#render_partial # 0.00 1.017 0.000 0.000 1.017 14 *ActionView::PartialRenderer#render # 0.00 1.007 0.000 0.000 1.007 14 *ActionView::PartialRenderer#render_partial # 0.00 0.930 0.000 0.000 0.930 14 Hamlit::TemplateHandler#call # 0.00 0.928 0.000 0.000 0.928 14 Temple::Engine#call # 0.02 0.865 0.000 0.000 0.864 638 *Enumerable#inject ``` To print the profile in HTML format, use the following example: ```ruby result = Gitlab::Profiler.profile('/my-user') printer = RubyProf::CallStackPrinter.new(result) printer.print(File.open('/tmp/profile.html', 'w')) ``` ### Stackprof support By default, `Gitlab::Profiler.profile` uses a tracing profiler called [`ruby-prof`](https://ruby-prof.github.io/). However, sampling profilers [run faster and use less memory](https://jvns.ca/blog/2017/12/17/how-do-ruby---python-profilers-work-/), so they might be preferred. You can switch to [Stackprof](https://github.com/tmm1/stackprof) (a sampling profiler) to generate a profile by passing `sampling_mode: true`. Pass in a `profiler_options` hash to configure the output file (`out`) of the sampling data. For example: ```ruby Gitlab::Profiler.profile('/gitlab-org/gitlab-test', user: User.first, sampling_mode: true, profiler_options: { out: 'tmp/profile.dump' }) ``` You can get a summary of where time was spent by running Stackprof against the sampling data. For example: ```shell stackprof tmp/profile.dump ``` Example sampling data: ```plaintext ================================== Mode: wall(1000) Samples: 8745 (6.92% miss rate) GC: 1399 (16.00%) ================================== TOTAL (pct) SAMPLES (pct) FRAME 1022 (11.7%) 1022 (11.7%) Sprockets::PathUtils#stat 957 (10.9%) 957 (10.9%) (marking) 493 (5.6%) 493 (5.6%) Sprockets::PathUtils#entries 576 (6.6%) 471 (5.4%) Mustermann::AST::Translator#decorator_for 439 (5.0%) 439 (5.0%) (sweeping) 630 (7.2%) 241 (2.8%) Sprockets::Cache::FileStore#get 208 (2.4%) 208 (2.4%) ActiveSupport::FileUpdateChecker#watched 206 (2.4%) 206 (2.4%) Digest::Instance#file 544 (6.2%) 176 (2.0%) Sprockets::Cache::FileStore#safe_open 176 (2.0%) 176 (2.0%) ActiveSupport::FileUpdateChecker#max_mtime 268 (3.1%) 147 (1.7%) ActiveRecord::ConnectionAdapters::PostgreSQLAdapter#exec_no_cache 140 (1.6%) 140 (1.6%) ActiveSupport::BacktraceCleaner#add_gem_filter 116 (1.3%) 116 (1.3%) Bootsnap::CompileCache::ISeq.storage_to_output 160 (1.8%) 113 (1.3%) Gem::Version#<=> 109 (1.2%) 109 (1.2%) block in
108 (1.2%) 108 (1.2%) Gem::Version.new 131 (1.5%) 105 (1.2%) Sprockets::EncodingUtils#unmarshaled_deflated 1166 (13.3%) 82 (0.9%) Mustermann::RegexpBased#initialize 82 (0.9%) 78 (0.9%) FileUtils.touch 72 (0.8%) 72 (0.8%) Sprockets::Manifest.compile_match_filter 71 (0.8%) 70 (0.8%) Grape::Router#compile! 91 (1.0%) 65 (0.7%) ActiveRecord::ConnectionAdapters::PostgreSQL::DatabaseStatements#query 93 (1.1%) 64 (0.7%) ActionDispatch::Journey::Path::Pattern::AnchoredRegexp#accept 59 (0.7%) 59 (0.7%) Mustermann::AST::Translator.dispatch_table 62 (0.7%) 59 (0.7%) Rails::BacktraceCleaner#initialize 2492 (28.5%) 49 (0.6%) Sprockets::PathUtils#stat_directory 242 (2.8%) 49 (0.6%) Gitlab::Instrumentation::RedisBase.add_call_details 47 (0.5%) 47 (0.5%) URI::RFC2396_Parser#escape 46 (0.5%) 46 (0.5%) ##__setobj__ 44 (0.5%) 44 (0.5%) Sprockets::Base#normalize_logical_path ``` You can also generate flamegraphs: ```shell stackprof --d3-flamegraph tmp/profile.dump > flamegraph.html ``` See [the Stackprof documentation](https://github.com/tmm1/stackprof) for more details. ## Speedscope flamegraphs You can generate a flamegraph for a particular URL by selecting a flamegraph sampling mode button in the performance bar or by adding the `performance_bar=flamegraph` parameter to the request. ![Speedscope](img/speedscope_v13_12.png) Find more information about the views in the [Speedscope docs](https://github.com/jlfwong/speedscope#views). Find more information about different sampling modes in the [Stackprof docs](https://github.com/tmm1/stackprof#sampling). This is enabled for all users that can access the performance bar. ## Bullet Bullet is a Gem that can be used to track down N+1 query problems. Bullet section is displayed on the [performance-bar](../administration/monitoring/performance/performance_bar.md). ![Bullet](img/bullet_v13_0.png) Because Bullet adds quite a bit of logging noise the logging is disabled by default. To enable the logging, set the environment variable `ENABLE_BULLET` to a non-empty value before starting GitLab. For example: ```shell ENABLE_BULLET=true bundle exec rails s ``` Bullet logs query problems to both the Rails log as well as the browser console. As a follow up to finding `N+1` queries with Bullet, consider writing a [QueryRecoder test](query_recorder.md) to prevent a regression. ## System stats During or after profiling, you may want to get detailed information about the Ruby virtual machine process, such as memory consumption, time spent on CPU, or garbage collector statistics. These are easy to produce individually through various tools, but for convenience, a summary endpoint has been added that exports this data as a JSON payload: ```shell curl localhost:3000/-/metrics/system | jq ``` Example output: ```json { "version": "ruby 2.7.2p137 (2020-10-01 revision a8323b79eb) [x86_64-linux-gnu]", "gc_stat": { "count": 118, "heap_allocated_pages": 11503, "heap_sorted_length": 11503, "heap_allocatable_pages": 0, "heap_available_slots": 4688580, "heap_live_slots": 3451712, "heap_free_slots": 1236868, "heap_final_slots": 0, "heap_marked_slots": 3451450, "heap_eden_pages": 11503, "heap_tomb_pages": 0, "total_allocated_pages": 11503, "total_freed_pages": 0, "total_allocated_objects": 32679478, "total_freed_objects": 29227766, "malloc_increase_bytes": 84760, "malloc_increase_bytes_limit": 32883343, "minor_gc_count": 88, "major_gc_count": 30, "compact_count": 0, "remembered_wb_unprotected_objects": 114228, "remembered_wb_unprotected_objects_limit": 228456, "old_objects": 3185330, "old_objects_limit": 6370660, "oldmalloc_increase_bytes": 21838024, "oldmalloc_increase_bytes_limit": 119181499 }, "memory_rss": 1326501888, "memory_uss": 1048563712, "memory_pss": 1139554304, "time_cputime": 82.885264633, "time_realtime": 1610459445.5579069, "time_monotonic": 24001.23145713, "worker_id": "puma_0" } ``` NOTE: This endpoint is only available for Rails web workers. Sidekiq workers can not be inspected this way. ## Settings that impact performance ### Application settings 1. `development` environment by default works with hot-reloading enabled, this makes Rails to check file changes every request, and create a potential contention lock, as hot reload is single threaded. 1. `development` environment can load code lazily once the request is fired which results in first request to always be slow. To disable those features for profiling/benchmarking set the `RAILS_PROFILE` environment variable to `true` before starting GitLab. For example when using GDK: - create a file [`env.runit`](https://gitlab.com/gitlab-org/gitlab-development-kit/-/blob/main/doc/runit.md#modifying-environment-configuration-for-services) in the root GDK directory - add `export RAILS_PROFILE=true` to your `env.runit` file - restart GDK with `gdk restart` *This environment variable is only applicable for the development mode.* ### GC settings Ruby's garbage collector (GC) can be tuned via a variety of environment variables that will directly impact application performance. The following table lists these variables along with their default values. | Environment variable | Default value | |--|--| | `RUBY_GC_HEAP_INIT_SLOTS` | `10000` | | `RUBY_GC_HEAP_FREE_SLOTS` | `4096` | | `RUBY_GC_HEAP_FREE_SLOTS_MIN_RATIO` | `0.20` | | `RUBY_GC_HEAP_FREE_SLOTS_GOAL_RATIO` | `0.40` | | `RUBY_GC_HEAP_FREE_SLOTS_MAX_RATIO` | `0.65` | | `RUBY_GC_HEAP_GROWTH_FACTOR` | `1.8` | | `RUBY_GC_HEAP_GROWTH_MAX_SLOTS` | `0 (disable)` | | `RUBY_GC_HEAP_OLDOBJECT_LIMIT_FACTOR` | `2.0` | | `RUBY_GC_MALLOC_LIMIT(_MIN)` | `(16 * 1024 * 1024 /* 16MB */)` | | `RUBY_GC_MALLOC_LIMIT_MAX` | `(32 * 1024 * 1024 /* 32MB */)` | | `RUBY_GC_MALLOC_LIMIT_GROWTH_FACTOR` | `1.4` | | `RUBY_GC_OLDMALLOC_LIMIT(_MIN)` | `(16 * 1024 * 1024 /* 16MB */)` | | `RUBY_GC_OLDMALLOC_LIMIT_MAX` | `(128 * 1024 * 1024 /* 128MB */)` | | `RUBY_GC_OLDMALLOC_LIMIT_GROWTH_FACTOR` | `1.2` | ([Source](https://github.com/ruby/ruby/blob/45b29754cfba8435bc4980a87cd0d32c648f8a2e/gc.c#L254-L308)) GitLab may decide to change these settings in order to speed up application performance, lower memory requirements, or both. You can see how each of these settings affect GC performance, memory use and application start-up time for an idle instance of GitLab by running the `scripts/perf/gc/collect_gc_stats.rb` script. It will output GC stats and general timing data to standard out as CSV.