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Rails App performance at the limit - Bogdan Gusiev

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Talk at Ruby Meditation #24
November 3, Kyiv
2018

Published in: Technology
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Rails App performance at the limit - Bogdan Gusiev

  1. 1. RAILS APPRAILS APP PERFORMANCEPERFORMANCE AT THEAT THE LIMITLIMIT NOVEMBER 2018NOVEMBER 2018 BOGDAN GUSIEVBOGDAN GUSIEV
  2. 2. BOGDAN G.BOGDAN G. is 10 years in IT 8 years with Ruby and Rails Long Run Rails Contributor Active Speaker
  3. 3. SOME OF MY GEMSSOME OF MY GEMS http://github.com/bogdan datagrid js-routes accepts_values_for furi
  4. 4. THE SCOPE OF THIS PRESENTATIONTHE SCOPE OF THIS PRESENTATION App server optimization Only technologies compatible with rails Relay on rails tools if we can
  5. 5. THE PLANTHE PLAN 1. General optimization 2. When to optimize specifically? 3. Identify slow parts 4. Targeted Optimization Methods
  6. 6. 8 years old startup A lot of code Rails version from 2.0 to 5.1 35_000 RPM 2TB sharded database
  7. 7. IS RAILS SLOW?IS RAILS SLOW? A MINUTE OF SEVERE REALITYA MINUTE OF SEVERE REALITY
  8. 8. HOW SLOW IS ACTION CONTROLLER & ACTION DISPATCH?HOW SLOW IS ACTION CONTROLLER & ACTION DISPATCH? Everyone's favorite hello-world benchmark Source: Framework Reqs/sec % from best ----------------------------------- rack 15839.64 100.00% sinatra 3977.30 25.11% grape 2937.03 18.54% rails-api 1211.33 7.65% bench-micro
  9. 9. HOW SLOW ISHOW SLOW IS ACTIVERECORD?ACTIVERECORD? sql = "select * from users where id = 1" # No ActiveRecord Mysql2::Client~query(sql) # Connection Query AR::Base.connection.execute(sql) # ActiveRecord::Base User.find(1) Benchmark Gist
  10. 10. DEV ENVIRONMENT:DEV ENVIRONMENT: QUERY FROM THE SAME MACHINE AS THE DBQUERY FROM THE SAME MACHINE AS THE DB AWS RDS EC2 INSTANCE:AWS RDS EC2 INSTANCE: QUERY FROM A DIFFERENT MACHINE THAN THE DBQUERY FROM A DIFFERENT MACHINE THAN THE DB No ActiveRecord: 7034.8 i/s Connection query: 6825.3 i/s - same-ish ActiveRecord::Base: 1244.8 i/s - 5.65x slower No ActiveRecord: 3204.2 i/s Connection query: 2762.6 i/s - 1.16x slower ActiveRecord::Base: 781.3 i/s - 4.10x slower
  11. 11. NO ACTIVERECORD IMPACTNO ACTIVERECORD IMPACT Up to 4 times faster Ugliest API No code reuse tools The dev process will be slowed down from 4 times
  12. 12. HOW SLOW IS ACTIONVIEW?HOW SLOW IS ACTIONVIEW? It is really hard to evaluate. Hello-world benchmark will not show anything Main things that impact performance: Advanced layout structure Render partials Helper method calls Using ActiveRecord inside views
  13. 13. GOOD NEWS ABOUTGOOD NEWS ABOUT ACTIONVIEWACTIONVIEW Easy to replace with different technology Client side rendering is available
  14. 14. OPTIMIZATIONOPTIMIZATION ANTI-PATTERNANTI-PATTERN Our App is slow, can we make it fast? Sure!
  15. 15. WHAT ARE YOU GOING TO IMPROVE WITH?WHAT ARE YOU GOING TO IMPROVE WITH? App Server Container Changing RDBMS Ruby interpreter
  16. 16. GENERAL OPTIMIZATIONGENERAL OPTIMIZATION Worms are not made to move fast
  17. 17. EFFECTIVE DATABASE STRUCTUREEFFECTIVE DATABASE STRUCTURE IS THE ONLY ONE GENERAL OPTIMIZATIONIS THE ONLY ONE GENERAL OPTIMIZATION TECHNIQUE WE FOUND USEFULTECHNIQUE WE FOUND USEFUL IN 8 YEARSIN 8 YEARS
  18. 18. GREAT DATABASE SCHEMAGREAT DATABASE SCHEMA Allows all controllers to do their work efficiently Reduces the operations using ruby to minimum Reduces SQL complexity
  19. 19. GOLDEN RULESGOLDEN RULES Optimize data storage for reading not for writing Business Rules define the database schema There is usually only one way that "feels" right Design efficiently for today
  20. 20. CACHE COLUMNSCACHE COLUMNS FOREIGN KEY CACHE EXAMPLEFOREIGN KEY CACHE EXAMPLE Poll.has_many :options Option.belongs_to :poll Option.has_many :votes Vote.belongs_to :option, counter_cache: true Vote.belongs_to :user Poll.has_many :votes Vote.belongs_to :poll, counter_cache: true Vote.validates_uniqueness_of :poll_id, scope: [:user_id]
  21. 21. JOIN AVOIDANCEJOIN AVOIDANCE Site.has_many :campaigns Campaign.has_many :offers Offer.has_many :offer_shares OfferShare.has_many :visitor_offers VisitorOffer.has_many :referrals Referral.scope :by_site, -> (id) { joins(visitor_offer: {offer_share: {offer: :campaign}}). where(campaigns: {site_id: id}) } add_column :referrals, :site_id Referral.before_validation do self.site_id = visitor_offer.offer_share.offer.campaign.site_id end
  22. 22. REAL EXAMPLE OFREAL EXAMPLE OF CACHE COLUMNSCACHE COLUMNS id :integer referred_origin_id :integer visitor_offer_id :integer status :string(20) webhook_status :string(10) track_method :string(20) processed_by :integer created_at :datetime updated_at :datetime processed_at :datetime offer_id :integer site_id :integer campaign_id :integer advocate_visitor_id :integer friend_timing :decimal referred_subtotal :decimal qa_generated :boolean ad_rewarded :boolean
  23. 23. CACHE COLUMNSCACHE COLUMNS BEST PRACTICESBEST PRACTICES Mostly for read-only data Remember what is the source and what is cache Watch the disk space It is worth it!
  24. 24. SPECIFIC OPTIMIZATIONSPECIFIC OPTIMIZATION Applied only to problematic pieces Makes sense for the used functionality Makes sense only when the functionality is stable Can be faster than switch to faster technology in general
  25. 25. HOW BUSINESS VIEWS THE OPTIMIZATION?HOW BUSINESS VIEWS THE OPTIMIZATION? You: Lets Optimize! Business: Business says yes when: Functionality is stable Feature is being used Company is making money with it
  26. 26. HOW TO IDENTIFYHOW TO IDENTIFY THE OPTIMIZATION AREASTHE OPTIMIZATION AREAS
  27. 27. THROUGHPUT BY ACTIONTHROUGHPUT BY ACTION
  28. 28. RESOURCES CONSUMEDRESOURCES CONSUMED
  29. 29. OPTIMIZATIONOPTIMIZATION MEASUREMENTMEASUREMENT Average response time Consumed resources (pseudocode) select controller, action, http_method, sum(duration), average(duration), count(*) from app_server_requests
  30. 30. TOOLSTOOLS HTTP access.log Rails production.log Kibana New Relic Your variant?
  31. 31. SPECIFIC OPTIMIZATIONSPECIFIC OPTIMIZATION PLANPLAN 1. Define Optimization Metric 2. Measure the metric 3. Zoom in to see optimizable parts 4. Choose optimized fragment and strategy
  32. 32. OPTIMIZATION METHODSOPTIMIZATION METHODS
  33. 33. AVOIDING ACTIVERECORDAVOIDING ACTIVERECORD Looks awesome on the first glance Less valuable with effective DB schema More work with more DB queries Unreusable code Caching can be superior
  34. 34. AVOIDING ACTIVERECORD EXAMPLESAVOIDING ACTIVERECORD EXAMPLES class Campaign has_many :tags def tag_names tags.pluck(:name) end end def offers_created_at_range scope = offers.reorder(created_at: :asc).limit(1) scope.pluck(:created_at).first.. scope.reverse_order.pluck(:created_at).first end
  35. 35. BOGDAN ❤ ACTIVERECORDBOGDAN ❤ ACTIVERECORD WHY DON'T ALL PEOPLE LOVE ACTIVERECORD?WHY DON'T ALL PEOPLE LOVE ACTIVERECORD? They don't optimize their schema They don't realize it does the hardest work They don't appreaciate the feature-set/performance trade off ALL MY EXAMPLES ARE CONSIDEREDALL MY EXAMPLES ARE CONSIDERED MICRO OPTIMIZATIONMICRO OPTIMIZATION
  36. 36. CONDITIONAL GETCONDITIONAL GET Client GET /products/1827.json Server Response /products/1827.json Etag: "2018-10-29 16:36" Client GET /products/1827.json If-None-Match: "2018-10-29 16:36" Server STATUS 304 Not Modified OR Response /products/1827.json Etag: "2018-10-29 16:37"
  37. 37. CONDITIONAL GET TOOLSCONDITIONAL GET TOOLS class Api::ProductsController < ApplicationController def show @product = Product.find(params[:id]) if stale?(last_modified: @product.updated_at, etag: @product.cache_key) render json: { product: @product } end end end Rails Conditional GET Guide
  38. 38. CONDITIONAL GETCONDITIONAL GET IS GREAT WHENIS GREAT WHEN A single page is viewed multiple times by the same browser Lightweight actions Actions returning JSON
  39. 39. REWRITING WITH RACKREWRITING WITH RACK 1. The action is already under 500ms 2. Heavily loaded action 3. It is painful 4. Looks better than rewriting on Sinatra
  40. 40. CONDITIONAL GET HELD IN RACKCONDITIONAL GET HELD IN RACK class OffersController < ApplicationController def show @offer = Offer.find(params[:id]) digest = SecureRandom.uuid data = { offer_id: offer.id, cached_at: Time.zone.now, } Rails.cache.write(digest, data, expires_in: CACHE_PERIOD.from_now) response.headers['Cache-Control'] = 'max-age=0, private, must-revalidate' response.headers['ETag'] = %(W/"#{digest}") end end
  41. 41. RACK MIDDLEWARERACK MIDDLEWARE def call(env) if fresh?(env['HTTP_IF_NONE_MATCH']) return [304, {}, ['Not Modified']] end @app.call(env) end def fresh?(etag_header) return unless data = Rails.cache.read(digest) site_id, timestamp = Offer.where(id: data[:offer_id]).pluck(:site_id, :updated_at) SettingsChange.where(site_id: site_id, created_at: lookup_period) !((data[:cached_at]..Time.zone.now).include?(timestamp)) end
  42. 42. INTRODUCE CACHINGINTRODUCE CACHING 1. Find suitable code fragment 2. Measure Cache Hit 3. Use expiration by key 4. Always expire by timeout 5. Expire on deploy
  43. 43. CACHE HITCACHE HIT How many cache entries will be made? What is the average size of cache entry? How many times each entry will be used? What % of requests will use cache? How much memory would it take?
  44. 44. CACHE HIT EXAMPLECACHE HIT EXAMPLE class Campaign has_many :offers def cached_offers_count @offers_count ||= if active? offers.count else # Caching only inactive campaigns # because they can not get additional offers Rails.cache.fetch( ["campaign_offers_count", id], expires_in: 1.month) d offers.count end end end end
  45. 45. EXPIRATION BY KEYEXPIRATION BY KEY Manual expiration example def show response = Rails.cache.fetch(["products", @product.id]) do @product.to_json end render json: response end def update @product.update!(params[:product]) Rails.cache.delete(["products", @product.id]) end
  46. 46. EXPIRATION BY KEY EXAMPLEEXPIRATION BY KEY EXAMPLE class ViewStylesheet def css # Combining many cache keys here # to expire whenever any object is updated cache_key = ['view_setup_css', campaign, self, account] Rails.cache.fetch(cache_key, force: template_changed?, expires_in: 3.days) do Sass.compile(template) end end end Key-based Expiration from DHH
  47. 47. EXPIRATION BEST PRACTICESEXPIRATION BEST PRACTICES config.cache_store = :redis_store, namespace: Deploy.identifier, expires_in: 1.day
  48. 48. RAILS MAGIC ON WORKING WITH CACHERAILS MAGIC ON WORKING WITH CACHE Variables usage inside ActionView is implicit Magic is always slow - cache @projects do - @projects.each do |project| %> = render partial: 'projects/project', project %a{href: project_path(product)}= project.name .star{class: current_user.bookmarked?(project) ? 'enabled' : ''}
  49. 49. OPTIMIZABLE REQUESTSOPTIMIZABLE REQUESTS Lightweight GET method Used data is explicit Return JSON but not HTML Do not use ActionView
  50. 50. WHAT OPTIMIZABLE PAGE SHOULD LOOK LIKE?WHAT OPTIMIZABLE PAGE SHOULD LOOK LIKE?
  51. 51. OPTIMIZATION ❤ CLIENT SIDE RENDERINGOPTIMIZATION ❤ CLIENT SIDE RENDERING Saves server resources Parallel load Makes used data explicit
  52. 52. OPTIMIZATION BYOPTIMIZATION BY CODE STRUCTURE CHANGECODE STRUCTURE CHANGE Trivial Always considered first Significant If it gives a huge performance boost Radical If you re-think the business process
  53. 53. TRIVIAL CODE STRUCTURE CHANGETRIVIAL CODE STRUCTURE CHANGE def render_main_template - view_setup.render_liquid(:main_template, translator, options) + template = rendering_cache do + view_setup.render_liquid(:main_template, translator, options) + end end def rendering_cache # 100 lines of code end
  54. 54. RESULTS OF GOOD OPTIMIZATIONRESULTS OF GOOD OPTIMIZATION Throughput 35_000 RPM Infrastructure Cost $16_000/Month
  55. 55. AWS SETUPAWS SETUP Instance Type c4.2xlarge vCPU 8 ECU 31 Memory 15 GiB Requests 120/second Per CPU 15/second
  56. 56. THE STRATEGYTHE STRATEGY 1. Generic Optimization Have the schema always optimized Add cache columns 2. Ensure specific optimization is needed Functionality is stable The performance is measured 3. Apply specific optimization Use Conditional Get Rewrite with Rack Introduce caching Use direct SQL ETC

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