941 lines
39 KiB
Markdown
941 lines
39 KiB
Markdown
---
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stage: none
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group: unassigned
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info: To determine the technical writer assigned to the Stage/Group associated with this page, see https://about.gitlab.com/handbook/engineering/ux/technical-writing/#assignments
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---
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# Performance Guidelines
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This document describes various guidelines to follow to ensure good and
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consistent performance of GitLab. Refer to the [Index](#performance-documentation) section below to navigate to Performance-related pages.
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## Performance Documentation
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- General:
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- [Solving performance issues](#workflow)
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- [Handbook performance page](https://about.gitlab.com/handbook/engineering/performance/)
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- [Merge request performance guidelines](../development/merge_request_performance_guidelines.md)
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- Backend:
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- [Tooling](#tooling)
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- Database:
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- [Query performance guidelines](../development/query_performance.md)
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- [Pagination performance guidelines](../development/database/pagination_performance_guidelines.md)
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- [Keyset pagination performance](../development/database/keyset_pagination.md#performance)
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- [Troubleshooting import/export performance issues](../development/import_export.md#troubleshooting-performance-issues)
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- [Pipelines performance in the `gitlab` project](../development/pipelines.md#performance)
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- Frontend:
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- [Performance guidelines](../development/fe_guide/performance.md)
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- [Performance dashboards and monitoring guidelines](../development/new_fe_guide/development/performance.md)
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- [Browser performance testing guidelines](../ci/testing/browser_performance_testing.md)
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- [`gdk measure` and `gdk measure-workflow`](https://gitlab.com/gitlab-org/gitlab-development-kit/-/blob/main/doc/gdk_commands.md#measure-performance)
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- QA:
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- [Load performance testing](../ci/testing/load_performance_testing.md)
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- [GitLab Performance Tool project](https://gitlab.com/gitlab-org/quality/performance)
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- [Review apps performance metrics](../development/testing_guide/review_apps.md#performance-metrics)
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- Monitoring & Overview:
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- [GitLab performance monitoring](../administration/monitoring/performance/index.md)
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- [Development department performance indicators](https://about.gitlab.com/handbook/engineering/development/performance-indicators/)
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- [Service measurement](../development/service_measurement.md)
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- Self-managed administration and customer-focused:
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- [File system performance benchmarking](../administration/operations/filesystem_benchmarking.md)
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- [Sidekiq performance troubleshooting](../administration/troubleshooting/sidekiq.md)
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## Workflow
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The process of solving performance problems is roughly as follows:
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1. Make sure there's an issue open somewhere (for example, on the GitLab CE issue
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tracker), and create one if there is not. See [#15607](https://gitlab.com/gitlab-org/gitlab-foss/-/issues/15607) for an example.
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1. Measure the performance of the code in a production environment such as
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GitLab.com (see the [Tooling](#tooling) section below). Performance should be
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measured over a period of _at least_ 24 hours.
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1. Add your findings based on the measurement period (screenshots of graphs,
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timings, etc) to the issue mentioned in step 1.
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1. Solve the problem.
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1. Create a merge request, assign the "Performance" label and follow the [performance review process](merge_request_performance_guidelines.md).
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1. Once a change has been deployed make sure to _again_ measure for at least 24
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hours to see if your changes have any impact on the production environment.
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1. Repeat until you're done.
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When providing timings make sure to provide:
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- The 95th percentile
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- The 99th percentile
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- The mean
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When providing screenshots of graphs, make sure that both the X and Y axes and
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the legend are clearly visible. If you happen to have access to GitLab.com's own
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monitoring tools you should also provide a link to any relevant
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graphs/dashboards.
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## Tooling
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GitLab provides built-in tools to help improve performance and availability:
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- [Profiling](profiling.md).
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- [Distributed Tracing](distributed_tracing.md)
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- [GitLab Performance Monitoring](../administration/monitoring/performance/index.md).
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- [QueryRecoder](query_recorder.md) for preventing `N+1` regressions.
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- [Chaos endpoints](chaos_endpoints.md) for testing failure scenarios. Intended mainly for testing availability.
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- [Service measurement](service_measurement.md) for measuring and logging service execution.
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GitLab team members can use [GitLab.com's performance monitoring systems](https://about.gitlab.com/handbook/engineering/monitoring/) located at
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[`dashboards.gitlab.net`](https://dashboards.gitlab.net), this requires you to log in using your
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`@gitlab.com` email address. Non-GitLab team-members are advised to set up their
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own Prometheus and Grafana stack.
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## Benchmarks
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Benchmarks are almost always useless. Benchmarks usually only test small bits of
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code in isolation and often only measure the best case scenario. On top of that,
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benchmarks for libraries (such as a Gem) tend to be biased in favour of the
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library. After all there's little benefit to an author publishing a benchmark
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that shows they perform worse than their competitors.
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Benchmarks are only really useful when you need a rough (emphasis on "rough")
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understanding of the impact of your changes. For example, if a certain method is
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slow a benchmark can be used to see if the changes you're making have any impact
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on the method's performance. However, even when a benchmark shows your changes
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improve performance there's no guarantee the performance also improves in a
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production environment.
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When writing benchmarks you should almost always use
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[benchmark-ips](https://github.com/evanphx/benchmark-ips). Ruby's `Benchmark`
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module that comes with the standard library is rarely useful as it runs either a
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single iteration (when using `Benchmark.bm`) or two iterations (when using
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`Benchmark.bmbm`). Running this few iterations means external factors, such as a
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video streaming in the background, can very easily skew the benchmark
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statistics.
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Another problem with the `Benchmark` module is that it displays timings, not
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iterations. This means that if a piece of code completes in a very short period
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of time it can be very difficult to compare the timings before and after a
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certain change. This in turn leads to patterns such as the following:
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```ruby
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Benchmark.bmbm(10) do |bench|
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bench.report 'do something' do
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100.times do
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... work here ...
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end
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end
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end
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```
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This however leads to the question: how many iterations should we run to get
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meaningful statistics?
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The [`benchmark-ips`](https://github.com/evanphx/benchmark-ips)
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gem takes care of all this and much more. You should therefore use it instead of the `Benchmark`
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module.
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The GitLab Gemfile also contains the [`benchmark-memory`](https://github.com/michaelherold/benchmark-memory)
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gem, which works similarly to the `benchmark` and `benchmark-ips` gems. However, `benchmark-memory`
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instead returns the memory size, objects, and strings allocated and retained during the benchmark.
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In short:
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- Don't trust benchmarks you find on the internet.
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- Never make claims based on just benchmarks, always measure in production to
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confirm your findings.
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- X being N times faster than Y is meaningless if you don't know what impact it
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has on your production environment.
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- A production environment is the _only_ benchmark that always tells the truth
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(unless your performance monitoring systems are not set up correctly).
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- If you must write a benchmark use the benchmark-ips Gem instead of Ruby's
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`Benchmark` module.
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## Profiling with Stackprof
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By collecting snapshots of process state at regular intervals, profiling allows
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you to see where time is spent in a process. The
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[Stackprof](https://github.com/tmm1/stackprof) gem is included in GitLab,
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allowing you to profile which code is running on CPU in detail.
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It's important to note that profiling an application *alters its performance*.
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Different profiling strategies have different overheads. Stackprof is a sampling
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profiler. It samples stack traces from running threads at a configurable
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frequency (for example, 100hz, that is 100 stacks per second). This type of profiling
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has quite a low (albeit non-zero) overhead and is generally considered to be
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safe for production.
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A profiler can be a very useful tool during development, even if it does run *in
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an unrepresentative environment*. In particular, a method is not necessarily
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troublesome just because it's executed many times, or takes a long time to
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execute. Profiles are tools you can use to better understand what is happening
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in an application - using that information wisely is up to you!
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There are multiple ways to create a profile with Stackprof.
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### Wrapping a code block
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To profile a specific code block, you can wrap that block in a `Stackprof.run` call:
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```ruby
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StackProf.run(mode: :wall, out: 'tmp/stackprof-profiling.dump') do
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#...
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end
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```
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This creates a `.dump` file that you can [read](#reading-a-stackprof-profile).
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For all available options, see the [Stackprof documentation](https://github.com/tmm1/stackprof#all-options).
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### Performance bar
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With the [Performance bar](../administration/monitoring/performance/performance_bar.md),
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you have the option to profile a request using Stackprof and immediately output the results to a
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[Speedscope flamegraph](profiling.md#speedscope-flamegraphs).
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### RSpec profiling with Stackprof
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To create a profile from a spec, identify (or create) a spec that
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exercises the troublesome code path, then run it using the `bin/rspec-stackprof`
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helper, for example:
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```shell
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$ LIMIT=10 bin/rspec-stackprof spec/policies/project_policy_spec.rb
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8/8 |====== 100 ======>| Time: 00:00:18
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Finished in 18.19 seconds (files took 4.8 seconds to load)
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8 examples, 0 failures
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==================================
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Mode: wall(1000)
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Samples: 17033 (5.59% miss rate)
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GC: 1901 (11.16%)
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==================================
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TOTAL (pct) SAMPLES (pct) FRAME
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6000 (35.2%) 2566 (15.1%) Sprockets::Cache::FileStore#get
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2018 (11.8%) 888 (5.2%) ActiveRecord::ConnectionAdapters::PostgreSQLAdapter#exec_no_cache
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1338 (7.9%) 640 (3.8%) ActiveRecord::ConnectionAdapters::PostgreSQL::DatabaseStatements#execute
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3125 (18.3%) 394 (2.3%) Sprockets::Cache::FileStore#safe_open
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913 (5.4%) 301 (1.8%) ActiveRecord::ConnectionAdapters::PostgreSQLAdapter#exec_cache
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288 (1.7%) 288 (1.7%) ActiveRecord::Attribute#initialize
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246 (1.4%) 246 (1.4%) Sprockets::Cache::FileStore#safe_stat
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295 (1.7%) 193 (1.1%) block (2 levels) in class_attribute
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187 (1.1%) 187 (1.1%) block (4 levels) in class_attribute
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```
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You can limit the specs that are run by passing any arguments `RSpec` would
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normally take.
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### Using Stackprof in production
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Stackprof can also be used to profile production workloads.
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In order to enable production profiling for Ruby processes, you can set the `STACKPROF_ENABLED` environment variable to `true`.
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The following configuration options can be configured:
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- `STACKPROF_ENABLED`: Enables Stackprof signal handler on SIGUSR2 signal.
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Defaults to `false`.
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- `STACKPROF_MODE`: See [sampling modes](https://github.com/tmm1/stackprof#sampling).
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Defaults to `cpu`.
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- `STACKPROF_INTERVAL`: Sampling interval. Unit semantics depend on `STACKPROF_MODE`.
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For `object` mode this is a per-event interval (every `nth` event is sampled)
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and defaults to `100`.
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For other modes such as `cpu` this is a frequency interval and defaults to `10100` μs (99hz).
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- `STACKPROF_FILE_PREFIX`: File path prefix where profiles are stored. Defaults
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to `$TMPDIR` (often corresponds to `/tmp`).
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- `STACKPROF_TIMEOUT_S`: Profiling timeout in seconds. Profiling will
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automatically stop after this time has elapsed. Defaults to `30`.
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- `STACKPROF_RAW`: Whether to collect raw samples or only aggregates. Raw
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samples are needed to generate flame graphs, but they do have a higher memory
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and disk overhead. Defaults to `true`.
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Once enabled, profiling can be triggered by sending a `SIGUSR2` signal to the
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Ruby process. The process begins sampling stacks. Profiling can be stopped
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by sending another `SIGUSR2`. Alternatively, it stops automatically after
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the timeout.
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Once profiling stops, the profile is written out to disk at
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`$STACKPROF_FILE_PREFIX/stackprof.$PID.$RAND.profile`. It can then be inspected
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further through the `stackprof` command line tool, as described in the
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[Reading a Stackprof profile section](#reading-a-stackprof-profile).
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Currently supported profiling targets are:
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- Puma worker
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- Sidekiq
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NOTE:
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The Puma master process is not supported.
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Sending SIGUSR2 to it triggers restarts. In the case of Puma,
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take care to only send the signal to Puma workers.
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This can be done via `pkill -USR2 puma:`. The `:` distinguishes between `puma
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4.3.3.gitlab.2 ...` (the master process) from `puma: cluster worker 0: ...` (the
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worker processes), selecting the latter.
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For Sidekiq, the signal can be sent to the `sidekiq-cluster` process via `pkill
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-USR2 bin/sidekiq-cluster`, which forwards the signal to all Sidekiq
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children. Alternatively, you can also select a specific PID of interest.
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### Reading a Stackprof profile
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The output is sorted by the `Samples` column by default. This is the number of samples taken where
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the method is the one currently executed. The `Total` column shows the number of samples taken where
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the method (or any of the methods it calls) is executed.
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To create a graphical view of the call stack:
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```shell
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stackprof tmp/project_policy_spec.rb.dump --graphviz > project_policy_spec.dot
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dot -Tsvg project_policy_spec.dot > project_policy_spec.svg
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```
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To load the profile in [KCachegrind](https://kcachegrind.github.io/):
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```shell
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stackprof tmp/project_policy_spec.rb.dump --callgrind > project_policy_spec.callgrind
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kcachegrind project_policy_spec.callgrind # Linux
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qcachegrind project_policy_spec.callgrind # Mac
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```
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You can also generate and view the resultant flame graph. To view a flame graph that
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`bin/rspec-stackprof` creates, you must set the `RAW` environment variable to `true` when running
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`bin/rspec-stackprof`.
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It might take a while to generate based on the output file size:
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```shell
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# Generate
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stackprof --flamegraph tmp/group_member_policy_spec.rb.dump > group_member_policy_spec.flame
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# View
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stackprof --flamegraph-viewer=group_member_policy_spec.flame
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```
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To export the flame graph to an SVG file, use [Brendan Gregg's FlameGraph tool](https://github.com/brendangregg/FlameGraph):
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```shell
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stackprof --stackcollapse /tmp/group_member_policy_spec.rb.dump | flamegraph.pl > flamegraph.svg
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```
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It's also possible to view flame graphs through [speedscope](https://github.com/jlfwong/speedscope).
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You can do this when using the [performance bar](profiling.md#speedscope-flamegraphs)
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and when [profiling code blocks](https://github.com/jlfwong/speedscope/wiki/Importing-from-stackprof-(ruby)).
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This option isn't supported by `bin/rspec-stackprof`.
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You can profile specific methods by using `--method method_name`:
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```shell
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$ stackprof tmp/project_policy_spec.rb.dump --method access_allowed_to
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ProjectPolicy#access_allowed_to? (/Users/royzwambag/work/gitlab-development-kit/gitlab/app/policies/project_policy.rb:793)
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samples: 0 self (0.0%) / 578 total (0.7%)
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callers:
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397 ( 68.7%) block (2 levels) in <class:ProjectPolicy>
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95 ( 16.4%) block in <class:ProjectPolicy>
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86 ( 14.9%) block in <class:ProjectPolicy>
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callees (578 total):
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399 ( 69.0%) ProjectPolicy#team_access_level
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141 ( 24.4%) Project::GeneratedAssociationMethods#project_feature
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30 ( 5.2%) DeclarativePolicy::Base#can?
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8 ( 1.4%) Featurable#access_level
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code:
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| 793 | def access_allowed_to?(feature)
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141 (0.2%) | 794 | return false unless project.project_feature
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| 795 |
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8 (0.0%) | 796 | case project.project_feature.access_level(feature)
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| 797 | when ProjectFeature::DISABLED
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| 798 | false
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| 799 | when ProjectFeature::PRIVATE
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429 (0.5%) | 800 | can?(:read_all_resources) || team_access_level >= ProjectFeature.required_minimum_access_level(feature)
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| 801 | else
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```
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When using Stackprof to profile specs, the profile includes the work done by the test suite and the
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application code. You can therefore use these profiles to investigate slow tests as well. However,
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for smaller runs (like this example), this means that the cost of setting up the test suite tends to
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dominate.
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## RSpec profiling
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The GitLab development environment also includes the
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[`rspec_profiling`](https://github.com/foraker/rspec_profiling) gem, which is used
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to collect data on spec execution times. This is useful for analyzing the
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performance of the test suite itself, or seeing how the performance of a spec
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may have changed over time.
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To activate profiling in your local environment, run the following:
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```shell
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export RSPEC_PROFILING=yes
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rake rspec_profiling:install
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```
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This creates an SQLite3 database in `tmp/rspec_profiling`, into which statistics
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are saved every time you run specs with the `RSPEC_PROFILING` environment
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variable set.
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Ad-hoc investigation of the collected results can be performed in an interactive
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shell:
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```shell
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$ rake rspec_profiling:console
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irb(main):001:0> results.count
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=> 231
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irb(main):002:0> results.last.attributes.keys
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=> ["id", "commit", "date", "file", "line_number", "description", "time", "status", "exception", "query_count", "query_time", "request_count", "request_time", "created_at", "updated_at"]
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irb(main):003:0> results.where(status: "passed").average(:time).to_s
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=> "0.211340155844156"
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```
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These results can also be placed into a PostgreSQL database by setting the
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`RSPEC_PROFILING_POSTGRES_URL` variable. This is used to profile the test suite
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when running in the CI environment.
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We store these results also when running nightly scheduled CI jobs on the
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default branch on `gitlab.com`. Statistics of these profiling data are
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[available online](https://gitlab-org.gitlab.io/rspec_profiling_stats/). For
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example, you can find which tests take longest to run or which execute the most
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queries. This can be handy for optimizing our tests or identifying performance
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issues in our code.
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## Memory optimization
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We can use a set of different techniques, often in combination, to track down memory issues:
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- Leaving the code intact and wrapping a profiler around it.
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- Use memory allocation counters for requests and services.
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- Monitor memory usage of the process while disabling/enabling different parts of the code we suspect could be problematic.
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### Memory allocations
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Ruby shipped with GitLab includes a special patch to allow [tracing memory allocations](https://gitlab.com/gitlab-org/gitlab/-/issues/296530).
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This patch is available by default for
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[Omnibus](https://gitlab.com/gitlab-org/omnibus-gitlab/-/merge_requests/4948),
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[CNG](https://gitlab.com/gitlab-org/build/CNG/-/merge_requests/591),
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[GitLab CI](https://gitlab.com/gitlab-org/gitlab-build-images/-/merge_requests/355),
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[GCK](https://gitlab.com/gitlab-org/gitlab-compose-kit/-/merge_requests/149)
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and can additionally be enabled for [GDK](https://gitlab.com/gitlab-org/gitlab-development-kit/-/blob/main/doc/advanced.md#apply-custom-patches-for-ruby).
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This patch provides the following metrics that make it easier to understand efficiency of memory use for a given code path:
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- `mem_total_bytes`: the number of bytes consumed both due to new objects being allocated into existing object slots
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plus additional memory allocated for large objects (that is, `mem_bytes + slot_size * mem_objects`).
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- `mem_bytes`: the number of bytes allocated by `malloc` for objects that did not fit into an existing object slot.
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- `mem_objects`: the number of objects allocated.
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- `mem_mallocs`: the number of `malloc` calls.
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The number of objects and bytes allocated impact how often GC cycles happen.
|
|
Fewer object allocations result in a significantly more responsive application.
|
|
|
|
It is advised that web server requests do not allocate more than `100k mem_objects`
|
|
and `100M mem_bytes`. You can view the current usage on [GitLab.com](https://log.gprd.gitlab.net/goto/3a9678bb595e3f89a0c7b5c61bcc47b9).
|
|
|
|
#### Checking memory pressure of own code
|
|
|
|
There are two ways of measuring your own code:
|
|
|
|
1. Review `api_json.log`, `development_json.log`, `sidekiq.log` that includes memory allocation counters.
|
|
1. Use `Gitlab::Memory::Instrumentation.with_memory_allocations` for a given code block and log it.
|
|
1. Use [Measuring module](service_measurement.md)
|
|
|
|
```json
|
|
{"time":"2021-02-15T11:20:40.821Z","severity":"INFO","duration_s":0.27412,"db_duration_s":0.05755,"view_duration_s":0.21657,"status":201,"method":"POST","path":"/api/v4/projects/user/1","mem_objects":86705,"mem_bytes":4277179,"mem_mallocs":22693,"correlation_id":"...}
|
|
```
|
|
|
|
#### Different types of allocations
|
|
|
|
The `mem_*` values represent different aspects of how objects and memory are allocated in Ruby:
|
|
|
|
- The following example will create around of `1000` of `mem_objects` since strings
|
|
can be frozen, and while the underlying string object remains the same, we still need to allocate 1000 references to this string:
|
|
|
|
```ruby
|
|
Gitlab::Memory::Instrumentation.with_memory_allocations do
|
|
1_000.times { '0123456789' }
|
|
end
|
|
|
|
=> {:mem_objects=>1001, :mem_bytes=>0, :mem_mallocs=>0}
|
|
```
|
|
|
|
- The following example will create around of `1000` of `mem_objects`, as strings are created dynamically.
|
|
Each of them will not allocate additional memory, as they fit into Ruby slot of 40 bytes:
|
|
|
|
```ruby
|
|
Gitlab::Memory::Instrumentation.with_memory_allocations do
|
|
s = '0'
|
|
1_000.times { s * 23 }
|
|
end
|
|
|
|
=> {:mem_objects=>1002, :mem_bytes=>0, :mem_mallocs=>0}
|
|
```
|
|
|
|
- The following example will create around of `1000` of `mem_objects`, as strings are created dynamically.
|
|
Each of them will allocate additional memory as strings are larger than Ruby slot of 40 bytes:
|
|
|
|
```ruby
|
|
Gitlab::Memory::Instrumentation.with_memory_allocations do
|
|
s = '0'
|
|
1_000.times { s * 24 }
|
|
end
|
|
|
|
=> {:mem_objects=>1002, :mem_bytes=>32000, :mem_mallocs=>1000}
|
|
```
|
|
|
|
- The following example will allocate over 40kB of data, and perform only a single memory allocation.
|
|
The existing object will be reallocated/resized on subsequent iterations:
|
|
|
|
```ruby
|
|
Gitlab::Memory::Instrumentation.with_memory_allocations do
|
|
str = ''
|
|
append = '0123456789012345678901234567890123456789' # 40 bytes
|
|
1_000.times { str.concat(append) }
|
|
end
|
|
=> {:mem_objects=>3, :mem_bytes=>49152, :mem_mallocs=>1}
|
|
```
|
|
|
|
- The following example will create over 1k of objects, perform over 1k of allocations, each time mutating the object.
|
|
This does result in copying a lot of data and perform a lot of memory allocations
|
|
(as represented by `mem_bytes` counter) indicating very inefficient method of appending string:
|
|
|
|
```ruby
|
|
Gitlab::Memory::Instrumentation.with_memory_allocations do
|
|
str = ''
|
|
append = '0123456789012345678901234567890123456789' # 40 bytes
|
|
1_000.times { str += append }
|
|
end
|
|
=> {:mem_objects=>1003, :mem_bytes=>21968752, :mem_mallocs=>1000}
|
|
```
|
|
|
|
### Using Memory Profiler
|
|
|
|
We can use `memory_profiler` for profiling.
|
|
|
|
The [`memory_profiler`](https://github.com/SamSaffron/memory_profiler)
|
|
gem is already present in the GitLab `Gemfile`. It's also available in the [performance bar](../administration/monitoring/performance/performance_bar.md)
|
|
for the current URL.
|
|
|
|
To use the memory profiler directly in your code, use `require` to add it:
|
|
|
|
```ruby
|
|
require 'memory_profiler'
|
|
|
|
report = MemoryProfiler.report do
|
|
# Code you want to profile
|
|
end
|
|
|
|
output = File.open('/tmp/profile.txt','w')
|
|
report.pretty_print(output)
|
|
```
|
|
|
|
The report shows the retained and allocated memory grouped by gem, file, location, and class. The
|
|
memory profiler also performs a string analysis that shows how often a string is allocated and
|
|
retained.
|
|
|
|
#### Retained versus allocated
|
|
|
|
- Retained memory: long-lived memory use and object count retained due to the execution of the code
|
|
block. This has a direct impact on memory and the garbage collector.
|
|
- Allocated memory: all object allocation and memory allocation during the code block. This might
|
|
have minimal impact on memory, but substantial impact on performance. The more objects you
|
|
allocate, the more work is being done and the slower the application is.
|
|
|
|
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](https://tenderlovemaking.com/2017/09/27/visualizing-your-ruby-heap.html).
|
|
|
|
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:
|
|
|
|
```ruby
|
|
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](https://gist.github.com/tenderlove/f28373d56fdd03d8b514af7191611b88) or similar:
|
|
|
|
```shell
|
|
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](https://www.speedshop.co/2017/12/04/malloc-doubles-ruby-memory.html). This should be considered as a tradeoff, as it may affect overall performance of memory allocation and GC cycles.
|
|
|
|
### Derailed Benchmarks
|
|
|
|
`derailed_benchmarks` is a [gem](https://github.com/zombocom/derailed_benchmarks)
|
|
described as "A series of things you can use to benchmark a Rails or Ruby app."
|
|
We include `derailed_benchmarks` in our `Gemfile`.
|
|
|
|
We run `derailed exec perf:mem` in every pipeline with a `test` stage, in a job
|
|
called `memory-on-boot`. ([Read an example job.](https://gitlab.com/gitlab-org/gitlab/-/jobs/2144695684).)
|
|
You may find the results:
|
|
|
|
- On the merge request **Overview** tab, in the merge request reports area, in the
|
|
**Metrics Reports** [dropdown list](../ci/testing/metrics_reports.md).
|
|
- In the `memory-on-boot` artifacts for a full report and a dependency breakdown.
|
|
|
|
`derailed_benchmarks` also provides other methods to investigate memory. To learn more,
|
|
refer to the [gem documentation](https://github.com/zombocom/derailed_benchmarks#running-derailed-exec).
|
|
Most of the methods (`derailed exec perf:*`) attempt to boot your Rails app in a
|
|
`production` environment and run benchmarks against it.
|
|
It is possible both in GDK and GCK:
|
|
|
|
- For GDK, follow the
|
|
[the instructions](https://github.com/zombocom/derailed_benchmarks#running-in-production-locally)
|
|
on the gem page. You must do similar for Redis configurations to avoid errors.
|
|
- GCK includes `production` configuration sections
|
|
[out of the box](https://gitlab.com/gitlab-org/gitlab-compose-kit#running-production-like).
|
|
|
|
## 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).
|
|
1. 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](#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.
|
|
1. The process being slow doesn't affect the loading time of a page.
|
|
1. In case of a failure you can retry the process (Sidekiq takes care of
|
|
this automatically).
|
|
1. 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:
|
|
|
|
```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 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:
|
|
|
|
```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 only allocates 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
|
|
this isn't guaranteed.
|
|
|
|
Freezing strings saves memory, as every allocated string uses at least one `RVALUE_SIZE` bytes (40
|
|
bytes on x64) of memory.
|
|
|
|
You can use the [memory profiler](#using-memory-profiler)
|
|
to see which strings are allocated often and could potentially benefit from a `.freeze`.
|
|
|
|
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:
|
|
|
|
```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.
|
|
|
|
## Banzai pipelines and filters
|
|
|
|
When writing or updating [Banzai filters and pipelines](https://gitlab.com/gitlab-org/gitlab/-/tree/master/lib/banzai),
|
|
it can be difficult to understand what the performance of the filter is, and what effect it might
|
|
have on the overall pipeline performance.
|
|
|
|
To perform benchmarks run:
|
|
|
|
```shell
|
|
bin/rake benchmark:banzai
|
|
```
|
|
|
|
This command generates output like this:
|
|
|
|
```plaintext
|
|
--> Benchmarking Full, Wiki, and Plain pipelines
|
|
Calculating -------------------------------------
|
|
Full pipeline 1.000 i/100ms
|
|
Wiki pipeline 1.000 i/100ms
|
|
Plain pipeline 1.000 i/100ms
|
|
-------------------------------------------------
|
|
Full pipeline 3.357 (±29.8%) i/s - 31.000
|
|
Wiki pipeline 2.893 (±34.6%) i/s - 25.000 in 10.677014s
|
|
Plain pipeline 15.447 (±32.4%) i/s - 119.000
|
|
|
|
Comparison:
|
|
Plain pipeline: 15.4 i/s
|
|
Full pipeline: 3.4 i/s - 4.60x slower
|
|
Wiki pipeline: 2.9 i/s - 5.34x slower
|
|
|
|
.
|
|
--> Benchmarking FullPipeline filters
|
|
Calculating -------------------------------------
|
|
Markdown 24.000 i/100ms
|
|
Plantuml 8.000 i/100ms
|
|
SpacedLink 22.000 i/100ms
|
|
|
|
...
|
|
|
|
TaskList 49.000 i/100ms
|
|
InlineDiff 9.000 i/100ms
|
|
SetDirection 369.000 i/100ms
|
|
-------------------------------------------------
|
|
Markdown 237.796 (±16.4%) i/s - 2.304k
|
|
Plantuml 80.415 (±36.1%) i/s - 520.000
|
|
SpacedLink 168.188 (±10.1%) i/s - 1.672k
|
|
|
|
...
|
|
|
|
TaskList 101.145 (± 6.9%) i/s - 1.029k
|
|
InlineDiff 52.925 (±15.1%) i/s - 522.000
|
|
SetDirection 3.728k (±17.2%) i/s - 34.317k in 10.617882s
|
|
|
|
Comparison:
|
|
Suggestion: 739616.9 i/s
|
|
Kroki: 306449.0 i/s - 2.41x slower
|
|
InlineGrafanaMetrics: 156535.6 i/s - 4.72x slower
|
|
SetDirection: 3728.3 i/s - 198.38x slower
|
|
|
|
...
|
|
|
|
UserReference: 2.1 i/s - 360365.80x slower
|
|
ExternalLink: 1.6 i/s - 470400.67x slower
|
|
ProjectReference: 0.7 i/s - 1128756.09x slower
|
|
|
|
.
|
|
--> Benchmarking PlainMarkdownPipeline filters
|
|
Calculating -------------------------------------
|
|
Markdown 19.000 i/100ms
|
|
-------------------------------------------------
|
|
Markdown 241.476 (±15.3%) i/s - 2.356k
|
|
|
|
```
|
|
|
|
This can give you an idea how various filters perform, and which ones might be performing the slowest.
|
|
|
|
The test data has a lot to do with how well a filter performs. If there is nothing in the test data
|
|
that specifically triggers the filter, it might look like it's running incredibly fast.
|
|
Make sure that you have relevant test data for your filter in the
|
|
[`spec/fixtures/markdown.md.erb`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/spec/fixtures/markdown.md.erb)
|
|
file.
|
|
|
|
## 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:
|
|
|
|
```ruby
|
|
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):
|
|
|
|
```shell
|
|
$ ps -o rss -p <pid>
|
|
|
|
RSS
|
|
783436
|
|
```
|
|
|
|
And here is an excerpt of what the garbage collector was doing:
|
|
|
|
```ruby
|
|
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:
|
|
|
|
```ruby
|
|
File.new('file').each { |line| puts line }
|
|
```
|
|
|
|
Alternatively, you can read individual lines explicitly using `IO.readline` or `IO.gets` functions:
|
|
|
|
```ruby
|
|
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](https://en.wikipedia.org/wiki/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 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:
|
|
|
|
```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 or using [Mass Inserting Rails
|
|
Models](mass_insert.md) functionality.
|
|
|
|
Assuming you are working with ActiveRecord models, you might also find these links helpful:
|
|
|
|
- [Insert records in batches](insert_into_tables_in_batches.md)
|
|
- [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](https://gitlab.com/gitlab-org/gitlab-foss/snippets/33946).
|