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Skeuomorphs, Databases, and Mobile Performance

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Skeuomorphs, Databases, and Mobile Performance

  1. 1. Skeuomorphs,Databases,and Mobile PerformanceArchitecting for performance with devices & APIsgroups.google.com/group/api-craftSam Ramji @sramjiApigee
  3. 3. A brief history of architecture
  4. 4. Banister FletcherA History of Architecture
  5. 5. “ Greek columns and their entablatures were at first entirely of timber, with terra-cotta decorations in the upper trabeation, but were converted into stone quite early in the [Hellenic] period, about 600 BC. The translation was quite direct, timber forms being imitated in stonework with remarkable exactness. For this reason, Greek architecture sometimes has been called a ‘carpentry in marble’… Banister Fletcher A History of Architecture
  6. 6. Skeuomorphs and metaphors
  7. 7. SkeuomorphA skeuomorph is a design feature found on animitation, pastiche or homage that was necessaryonly to the original. Often used for the sake offamiliarity, they are details that have moved fromfunction to form. Tom Petty hipstercheerleaders.com
  8. 8. MetaphorIn cognitive linguistics, conceptual metaphor, orcognitive metaphor, refers to the understandingof one idea, or conceptual domain, in terms ofanother, for example, understanding quantity interms of directionality (e.g. "prices are rising"). Wikipedia.org Conceptual Metaphors
  9. 9. “ The concepts that govern our thought are not just matters of the intellect. They also govern our everyday functioning, down to the most mundane details. Our concepts structure what we perceive, how we get around in the world, and how we relate to other people. Our conceptual system thus plays a central role in defining our everyday realities. If we are right in suggesting that our conceptual system is largely metaphorical, then the way we thinks what we experience, and what we do every day is very much a matter of metaphor. George Lakoff and Mark Johnson Metaphors We Live By
  10. 10. DATABASES
  11. 11. A brief history of ^ architecture
  12. 12. Connected Devices Smartphone N-tier Web App Personal DCOM CORBA Computer WebsiteMinicomputer Client/Server Mainframe Integrated Distributed Computing Architectures
  13. 13. Domain-specific Data APIs PrivateCloud DBs Caching DBs Data API Data Warehousing Mainframe RDBMS Flat file Shared Silos Data Architectures
  14. 14. We’ve come back to client-server computing
  15. 15. From the perspective of the mobile client,the Internet is a database.
  16. 16. Is that a skeuomorph or a metaphor?
  17. 17. If the Internet is a database,what have we learned from prior eras aboutarchitecting for performance?
  19. 19. The classic client-server problem returns
  20. 20. If the database is slow, the app is slow.
  21. 21. Research shows that people will put up withabout 1.5 seconds between interactions.
  22. 22. More than 3 seconds on averageand they’ll stop using the app.
  23. 23. This is a problem.
  24. 24. Let’s dig into our client-server historyto break it down.
  25. 25. Make the application smarterUse the network intelligentlyOptimize the database aggressively
  27. 27. application What makes the app feel fast to the user?
  28. 28. application Time to first render Time to first interaction Time between interactions
  29. 29. application Three mutually reinforcing techniques: Code profiling for performance optimization Threading/concurrency for user interactions Client-side caching for everything else
  30. 30. application Use the profiler to see where you’re slow Write faster code where you see big gains Run long operations in parallel Keep local copies of everything you need
  31. 31. application Concurrency
  32. 32. application Anticipation
  33. 33. application Caching
  34. 34. application What should you be caching locally? Security credentials or tokens Last user session data MRU (Most recently used) MFU (Most frequently used) LFC (Least frequently changed) API write operations Graceful fallbacks for failed API calls
  35. 35. application Issues do remain Can’t hit local cache on first use of app Receiving the right shape of data
  37. 37. network The radio network is a high-latency, limited-resource environment.
  38. 38. network Speed and battery usage are both important dimensions of mobile performance
  39. 39. network Intermittent usage of the radio for pingbacks keep-alives analytics screen rotations will slow you down and burn battery.
  40. 40. network A better approach: Bundling, piggybacking, and pipelining
  41. 41. network Connection Tail setup time 2 sec n sec 15 sec Data Idle transfer Battery cost of a series of small API requests 90 sec of radio use and battery burn Bundling a set of API requests 19 sec
  42. 42. network Intermittent analytics and keep-alives 90 sec of radio use and battery burn Piggybacking on a set of user API requests 19 sec
  43. 43. network API calls in series 200 ms 200 ms 200 ms 200 ms 200 ms 1000 ms API pipelining
  44. 44. Bundling loosely-related requests togetherPiggybacking secondary intermittent trafficPipelining requests to maximize throughput
  46. 46. database What were our old database optimization tricks that we can apply to Internet data?
  47. 47. database Stored Procedures Queueing Denormalization Result Sets
  48. 48. database What is a Stored Procedure in this world? Server-side code that executes complex operations Ones that should happen right next to the data Where you need high compute and low latency Could be written in node.js, ruby, java, python, c#
  49. 49. database Where does a Stored Procedure run in this world?
  50. 50. database Where does a Stored Procedure run in this world? Probably in a cloud
  51. 51. database Once you’ve built this architectural layer you gain a lot of control
  52. 52. database You can deal with queueing, denormalization, and manage result sets properly.
  53. 53. database Queueing enables you to break the request/response pair into separate pieces You may even be able to tell the client when to call you back for the result Making your requests to this queueing layer also lets you serve from a cloud-side cache if you have one
  54. 54. database Denormalization refers to writing multiple indexes in order to optimize query performance Where your app relies on your own data, don’t make it wait for slow queries Remember, in the cloud, storage is cheap and easy to obtain – write data as often as needed to improve query speeds.
  55. 55. database Managing result sets to save bandwidth and response time means limiting cursor size by default This can be complementary to the caches you keep around, since a massive API result is cheap to manage in the cloud and can be trickled back to the app in bite-size chunks.
  56. 56. database Managing result sets to save processor time for the client is an option as well. What would happen if you could focus on app-shaped data?
  57. 57. XML in Javascriptvar parseXml;if (typeof window.DOMParser != "undefined"){ parseXml = function(xmlStr) { return (new window.DOMParser()).parseFromString(xmlStr,"text/xml"); };}else if (typeof window.ActiveXObject != "undefined" && new window.ActiveXObject("Microsoft.XMLDOM")){ parseXml = function(xmlStr) { var xmlDoc = new window.ActiveXObject("Microsoft.XMLDOM"); xmlDoc.async = "false"; xmlDoc.loadXML(xmlStr); return xmlDoc; };}else { throw new Error("No XML parser found"); }var xml = parseXml("<result>true</result><count>1</count>");alert(xml.documentElement.nodeName);
  58. 58. JSON in Javascriptvar json = {"result":true,"count":1}, obj = JSON.parse(json);alert(obj.count);
  59. 59. INCLOSING
  60. 60. There are a few things we canborrow from the pastto help us right now
  61. 61. Make the application smarterUse the network intelligentlyOptimize the database aggressively
  62. 62. What did you decide about the statement“The internet is a database”?
  63. 63. Carpentry in marble?or cognitive tool?
  64. 64. Skeuomorph?or metaphor?
  65. 65. groups.google.com/group/api-craft
  66. 66. THANK YOUQuestions and ideas to:@sramjigroups.google.com/group/api-craft