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Delivering Search for Today's Local, Social, and Mobile Applications


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Search is a crucial component of local, social, and mobile web applications. This session will provide an overview of Amazon CloudSearch – a fully managed search service that scales with data & traffic. We'll show how to design, build, deploy a search solution, and tune it for best results. You will hear from customers that quickly integrated CloudSearch into their applications to deliver powerful search capabilities.

Published in: Technology, Self Improvement
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Delivering Search for Today's Local, Social, and Mobile Applications

  1. 1. Jon HandlerDelivering Search for Todays Local, Social, and MobileApplicationsCloudSearch Solution Architect
  2. 2. Agenda• Search for social and mobile applications• Amazon CloudSearch• Customer stories and panel– Tim Ramsey, CTO/Co-founder for Ziplist / Conde Nast– Pat Binkley, VP Engineering for Zumobi– Ken Chung, CTO for Viddy
  3. 3. Time Spent Online (source:
  4. 4. Social Activities• People are talking!• Word of mouth is the primary factor behind20% to 50% of all purchasing decisions(Jonah Berger, "Contagious")• Search brings relevance to the process ofpulling in friends
  5. 5. The Rise of Mobile Search (Source: BIA / Kelsey)• In 2011, Search was 75% desktop,25% mobile• In 2013, they project 60% / 40%• By 2016, 57% mobile, 43% desktop
  6. 6. In the Mobile Space, Local Is Growing
  7. 7. The Path to Search• Search is central to user experience• Options for building a search experience: BYO, open source, legacy enterprise• Challenges
  8. 8. Amazon CloudSearch: Simple Search Simply• Pay for infrastructure as you need it• Lower total cost of operation• No need to guess at capacity• Increase innovation – low risk, inexpensive, simple experimentation• Does the undifferentiated heavy lifting• Available in 5 regions: go global in minutes
  9. 9. Amazon CloudSearch OverviewDNS / Load Balancing AWS QuerySearch API Console ConfigAPICommandLine InterfaceConsoleDocSvc APICommandLine InterfaceConsoleSEARCH SERVICESearch DocumentsDOCUMENT SERVICEAdd DocumentsUpdate DocumentsDelete DocumentsCreate DomainsConfigure DomainsDelete DomainsCONFIG SERVICESearch Domain
  10. 10. CloudSearch in Context
  11. 11. Automatic ScalingSEARCH INSTANCEIndex Partition nCopy 1SEARCH INSTANCEIndex Partition 2Copy 2SEARCH INSTANCEIndex Partition nCopy 2SEARCH INSTANCEIndex Partition 2Copy nSEARCH INSTANCEDATA Document Quantity and SizeTRAFFICSearchRequestVolume andComplexityIndex Partition nCopy nSEARCH INSTANCEIndex Partition 1Copy 1SEARCH INSTANCEIndex Partition 2Copy 1SEARCH INSTANCEIndex Partition 1Copy 2SEARCH INSTANCEIndex Partition 1Copy n
  12. 12. Pricing• Get started for just $2.40/day; $75/month• AWS Calculator started now – 30 days free
  13. 13. Customer Stories
  14. 14. Searching Recipes and Recipe Boxes on ZipListTim RamseyChief Architect and Co-Founder,Ziplist Inc.
  15. 15. - High-Level Description and Context• Shopping List• Recipes and Recipe Boxo 1.6M Recipes in Global Indexo 25M Recipes in Boxes• 3.5M Users• iPhone and Android Mobile Apps –315K Downloads• Partnerso 300+ White Label Partners( 1,500+ Recipe Partners(• Hardware/Software Platformo Amazon EC2o Amazon RDSo Amazon CloudSearcho Ruby on Rails
  16. 16. ZipList RecipesIndexed from Around the Web• Structured ( orUnstructured (• Recipes collected with and withoutwebsite cooperation• Pushes to API or JIT with the RecipeClipper* Attributes used in recipe box search/filteringAttributes Collected and Indexed• Title*• Description• Photo* (yes/no)• Publisher* (Martha Stewart, SeriousEats, etc.)• Ingredients (text and number*)• Instructions• Publisher Tags*• Course* (main, dessert, breakfast, etc.)• Season (Thanksgiving, summer,birthday, etc.)• Etc.
  17. 17. Searching ZipList Recipes• First try – Ferreto Ability to index on any attribute without DB changeso Zero latency between insert and available in indexo Runtime load and stability• Next try – Sphinxo Incremental indexing load and timeo Indexing DB loado Search daemon load and latency• Current – CloudSearcho SDF allows for synthetic fields (like ferret)o Fast query resultso Indexing latency issues
  18. 18. ZipList PlatformZipList Recipe IndexingWWWRecipesZLREcipesUser ARecipe BoxUser BRecipe BoxRecipesCloudSearchSDF DocumentsSave RecipeSave RecipeRecipe BoxCloudsearchSDF DocumentsRecipes Clipped orPushed
  19. 19. Recipe Box Indexing Latency• Desire to unify the UX for Global Recipe Search and Recipe Box Searcho Filteringo Facet Countso Pagination• Scenarioo User finds a recipe on and adds it to their boxo They then go immediately to to view their Recipe Box and filter byEpicurious publishero The time it takes for the recipe to appear is LTOT = LSDF + LCSIo Even 10s is unacceptable
  20. 20. ZipList PlatformHandling Indexing LatencyZLRecipesRecipeBoxRecipe BoxCloudsearchSDF DocumentsSave RecipeQueryModuleRBCacheSearchQuerySearchResults
  21. 21. Summary and Conclusions• With the latency issues resolved, CloudSearch works very well for oursearching needs• Searching, filtering, and faceting provided by CloudSearch fit in well with ourapplication• We have a consistent interface / capabilities between Global Recipe Searchand Recipe Box Search• Positions ZipList well for moving to NoSQL solutions
  22. 22. ZumobiPat Binkley, VP Engineering
  23. 23. Zumobi is the leading mobile media company that partners with top media brands to publish premium applicationsand provide integrated native advertising experiences on smartphones and connected devices.The Zumobi Brand Integration (ZBi) native advertising platform offers an SDK to drive a widearray of features and formats enabling seamlessly integrated brand experiences on mobile.Zumobi’s Long History of Mobile Innovation
  24. 24. CloudSearch Example: THE WEEK App• Why? Parity with Desktop experience– Most news apps are not yet integrated with searchcapabilities, but desktop is. Mobile is quickly catching upwith desktop functionalities• Problem? Volume of data on device– Zumobi apps store content locally for performance andoffline experience– Too much data to store on phone to provide meaningfulsearch results• Solution? AWS CloudSearch– Store documents in the cloud and query
  25. 25. New Capabilities• Keyword Searching• Relevant Results Display• Article Viewing• Social sharing– Facebook & Twitter• Save– Save your favorite articles to review later
  26. 26. ArchitectureZumobiPlatform(Rails App)CloudSearchDOCUMENTSERVICERSSOptional: >2K itemsDocumentRequestResponseCloudSearchSEARCHSERVICEStep 1: Search for Item/List ReturnedStep 2: Fetch optional > 2K itemsApp ContentSQLite/S3CDN** Implementation Time:- 5 days
  27. 27. Impact• Feature parity with website• Enhanced App Performance– Increased Usage• Amplified User Engagement• More Pageviews• New Users• Data Collection– Find out what your audience is searching for in order to provide more relevantcontentImpact
  28. 28. Zumobi App NetworkFuture Search Integrations…
  29. 29. Learn More – Check Out••
  30. 30. ViddyKen Chung, CTO
  31. 31. ViddyViddy is a simple way to capture,create, and share short, 30-secondsocial videos with friends.
  32. 32. Viddy – Search Use Cases• Users want to discover content around them• Users want to find their friends and celebrities• Users want to discover content with certain #hashtags and keywords• Users want to discover more relevant content by language, country, and morecontext
  33. 33. Viddy – Unique Problem? No• Problems not unique to Viddy• With rise of social, mobile, and geo-aware mobile apps, search is basicallyeveryone’s challenge• Why re-invent the wheel?• Let the platform handle the headaches and let us focus on innovations!
  34. 34. Viddy – B.C. aka “Before CloudSearch”• Custom managed Lucene + Indexers + Search API Servers• A full dedicated backend development effort for search product (out of twobackend engineers)• Lack of tooling for optimizing queries and improving search relevancy bynon-engineers• Lack of monitoring and reporting around search
  35. 35. … and then 300K users to 40 million users in less than 6 months
  36. 36. Viddy – Hello, CloudSearch!• Migrated entirely from Lucene to CloudSearch in 3 days• Simple CloudSearch REST API for Indexing and Querying• 2–5 hrs/wk of maintenance effort for search product• Interns improving search result through CloudSearch console• Better monitoring and report
  37. 37. Viddy – Aftermath• Operation cost: $5–6K/mo to $1600/mo• More development resources for innovation• 5x increase in search usage• Faster incremental index: avg. delay from 15 mins to 30 secs
  38. 38. Customer Panel
  39. 39. Thank You!• Learn more at• Please fill out your evaluation forms