O slideshow foi denunciado.
Seu SlideShare está sendo baixado. ×

Introduction to Google's Cloud Technologies


Confira estes a seguir

1 de 74 Anúncio

Mais Conteúdo rRelacionado


Semelhante a Introduction to Google's Cloud Technologies (20)

Mais de Chris Schalk (20)


Mais recentes (20)

Introduction to Google's Cloud Technologies

  1. Introduction to Google’s Cloud Technologies Chris  Schalk   OpenCF  Summit   Google  Developer  Advocate   Monday  Feb  21st,  2010  
  2. Google Cloud Technologies at a Glance ExisIng   Google  App  Engine   Google  App  Engine  for  Business  (new)   New!   Google     Google  BigQuery   Predic0on  API   Google  Storage  
  3. Agenda •  Part I - Intro to App Engine •  App Engine Details •  Development Tools •  App Engine for Business •  Part II – Google’s new cloud technologies •  Google Storage •  Prediction API •  BigQuery •  Part III - Dabbling with CFML on App Engine
  4. Part I – Intro to App Engine Topics covered •  App Engine a PaaS •  App Engine usage/customers •  App Engine Technical Details
  5. Google App Engine Build your own applications in Google's cloud
  6. Cloud Computing as Gartner Sees It SaaS   PaaS   IaaS   Source:  Gartner  AADI  Summit  Dec  2009   6  
  7. Why Google App Engine? • Easy to build • Easy to maintain • Easy to scale 7
  8. By the Numbers 150,000+  ac0ve   apps  on  a  weekly   basis   8 8
  9. By the Numbers 100,000+   developers  use   every  month   9 9
  10. By the Numbers 1B+   daily  pageviews   10 10
  11. Some App Engine Partners 11  
  12. App Engine Details 12
  13. Cloud Development in a Box •   Downloadable SDK •  Application runtimes •  Java, Python •  Local development tools •  Eclipse plugin, AppEngine Launcher •  Specialized application services •  Cloud based dashboard •  Ready to scale •  Built in fault tolerance, load balancing 13
  14. Specialized Services Memcache   Datastore   URL  Fetch   Mail   XMPP   Task  Queue   Images   Blobstore   User  Service   14  
  15. Language Runtimes Duke,  the  Java  mascot   Copyright  ©  Sun  Microsystems  Inc.,  all  rights  reserved.   15  
  16. Ensuring Portability 16  
  17. Extended Language support through JVM •  Java •  Scala •  Ruby (JRuby) •  Groovy (Gaelyk) •  PHP (Quercus) •  JavaScript (Rhino) Duke,  the  Java  mascot   Copyright  ©  Sun  Microsystems  Inc.,  all  rights  reserved.   •  Python (Jython) •  CFML (Open BlueDragon) 17  
  18. Always free to get started •  43M requests/day •  6.5 CPU hrs/day •  1 GB outgoing bandwidth •  1 GB blob storage •  650K URL Fetch calls/day •  2,000 recipients emailed •  1 GB/day bandwidth •  100,000 tasks api calls •  257K XMPP messages/minute hWp://code.google.com/appengine/docs/quotas.html   18
  19. Manage your expansion – Easy Billing 19
  20. Application Platform Management 20  
  21. App Engine Dashboard 21  
  22. Development Tools for App Engine 22  
  23. Google Plugin for Eclipse 23  
  24. SDK Console 24  
  25. Two+ years in review Apr 2008! Python launch May 2008! Memcache, Images API Jul 2008! Logs export Aug 2008! Batch write/delete Oct 2008! HTTPS support Dec 2008! Status dashboard, quota details Feb 2009! Billing, larger files Apr 2009! Java launch, DB import, cron support, SDC May 2009! Key-only queries Jun 2009! Task queues Aug 2009! Kindless queries Sep 2009! XMPP Oct 2009! Incoming email Dec 2009! Blobstore Feb 2010! Datastore cursors, Appstats Mar 2010! Read policies, IPv6 May 2010! App Engine for Business Jun 2010! Task queue increases, Python pre-compilation… Jul 2010! Mapper API Aug 2010! Multi-tenancy, hi perf img serving, custom err pages Oct 2010! Instances Console, Delete Kind/App Data 25  
  26. App Engine 1.4 Release New Features 1.  Channel  API      Allows  for  Server  Push  (Comet)  to  browser      -­‐  hWp://code.google.com/appengine/docs/java/channel/   2.  Always  On   3.  Warm  Up  Requests   –   Enabled  by  default  for  Java  apps   –   Can  turn  off  in  appengine-­‐web.xml  via:  <warmup-­‐requests-­‐ enabled>false</warmup-­‐requests-­‐enabled>  
  27. App Engine 1.4 Release New Features 4.  Hard  Limit  Updates   –   No  more  30  second  limit  for  background  work  -­‐>  up  to  10  minutes   –   Response  size  limits  for  URLFetch  have  been  raised  from  1MB  to  32MB   –   Memcache  batch  get/put  can  now  also  do  up  to  32MB  requests   –   Image  API  requests  and  response  size  limits  have  been  raised  from  1MB  to  32MB   –   Mail  API  outgoing  aWachments  have  been  increased  from  1MB  to  10MB  
  28. Introducing App Engine for Business App Engine for Business Same scalable cloud platform, but designed for the Enterprise 29
  29. Google App Engine for Business Details •  Enterprise application management –  Centralized domain console (preview available) •  Enterprise reliability and support Google App Engine –  99.9% Service Level Agreement for Business –  Direct support •  Hosted SQL –  Relational SQL database in the cloud (preview available) •  SSL on your domain •  Extremely Secure by default –  Integrated Single Sign On (SSO) •  Pricing that makes sense –  Apps cost $8 per user, up to $1000 max per month 30  
  30. App Engine for Business Roadmap Enterprise Administration Preview (signups available) Console Direct Support Preview (signups available) Hosted SQL Preview (signups available) Service Level Agreement Preview (Draft published) Custom Domain SSL Limited Release Q1 2011 32  
  31. App Engine Resources Get started with App Engine •  http://code.google.com/appengine Read up on App Engine for Business and become a trusted tester •  http://code.google.com/appengine/business
  32. App Engine Demos •  App Engine •  Getting started •  App Engine for Business •  Domain Console •  Guestbook on SQL on GAE4B
  33. Part II - Google’s new Cloud Technologies Topics covered •  Google Storage for Developers •  Prediction API (machine learning) •  BigQuery
  34. Google Storage for Developers Store your data in Google's cloud
  35. What Is Google Storage? •  Store  your  data  in  Google's  cloud   o  any  format,  any  amount,  any  Ime   •  You  control  access  to  your  data   o  private,  shared,  or  public   •   Access  via  Google  APIs  or  3rd  party  tools/libraries  
  36. Sample Use Cases Static content hosting e.g. static html, images, music, video Backup and recovery e.g. personal data, business records Sharing e.g. share data with your customers Data storage for applications e.g. used as storage backend for Android, AppEngine, Cloud based apps Storage for Computation e.g. BigQuery, Prediction API
  37. Google Storage Benefits High  Performance  and  Scalability                 Backed  by  Google  infrastructure     Strong  Security  and  Privacy                  Control  access  to  your  data   Easy  to  Use   Get  started  fast  with  Google  &  3rd  party  tools  
  38. Google Storage Technical Details •  RESTful API    o  Verbs: GET, PUT, POST, HEAD, DELETE    o  Resources: identified by URI   o  Compatible with S3    •  Buckets    o  Flat containers    •  Objects    o  Any type   o  Size: 100 GB / object   •  Access Control for Google Accounts    o  For individuals and groups   •  Two Ways to Authenticate Requests    o  Sign request using access keys    o  Web browser login#
  39. Performance and Scalability •  Objects of any type and 100 GB / Object •  Unlimited numbers of objects, 1000s of buckets •  All data replicated to multiple US data centers •  Utilizes Google's worldwide network for data delivery •  Only you can use bucket names with your domain names •  Read-your-writes data consistency •  Range Get
  40. Some  Early  Google  Storage  Adopters  
  41. Google Storage - Availability •  Preview in US currently o  100GB free storage and network from Google per account o  Sign up for waitlist at http://code.google.com/apis/ storage/ •  Note: Non US preview available on case-by-case basis •  http://bit.ly/dKm770 (for Storage, BigQuery, Prediction)
  42. Demo •  Tools: o  GS Manager o  GSUtil •  Upload / Download
  43. Google Prediction API Google's prediction engine in the cloud
  44. Introducing the Google Prediction API •  Google's sophisticated machine learning technology •  Available as an on-demand RESTful HTTP web service
  45. How  does  it  work?   "english"   The  quick  brown  fox  jumped  over  the  lazy   The Prediction API dog.   finds relevant features in the "english"   To  err  is  human,  but  to  really  foul  things  up   sample data during you  need  a  computer.   training. "spanish"   No  hay  mal  que  por  bien  no  venga.   "spanish"   La  tercera  es  la  vencida.   The  PredicIon  API   ?   To  be  or  not  to  be,  that  is  the  quesIon.   later  searches  for   those  features   ?   La  fe  mueve  montañas.   during  predicIon.  
  46. A  virtually  endless  number  of  applicaIons...   Customer TransacIon   Species   Message   DiagnosIcs   Sentiment Risk   IdenIficaIon   RouIng   Churn   Legal  Docket   Suspicious   Work  Roster   Inappropriate   PredicIon   ClassificaIon   AcIvity   Assignment   Content   Recommend   PoliIcal   Uplin   Email   Career   Products   Bias   MarkeIng   Filtering   Counselling   ...  and  many  more  ...  
  47. Using the Prediction API A  simple  three  step  process...   Upload  your  training  data  to   1.  Upload   Google  Storage     Build  a  model  from  your  data   2.  Train   3.  Predict   Make  new  predicIons  
  48. Step  1:  Upload   Upload  your  training  data  to  Google  Storage   •  Training data: outputs and input features •  Data format: comma separated value format (CSV) "english","To err is human, but to really ..."   "spanish","No hay mal que por bien no venga."   ... Upload  to  Google  Storage   gsutil cp ${data} gs://yourbucket/${data}
  49. Step  2:  Train   Create  a  new  model  by  training  on  data   To train a model: POST prediction/v1.1/training?data=mybucket%2Fmydata Training runs asynchronously. To see if it has finished: GET prediction/v1.1/training/mybucket%2Fmydata {"data":{ "data":"mybucket/mydata", "modelinfo":"estimated accuracy: 0.xx"}}}
  50. Step  3:  Predict   Apply  the  trained  model  to  make  predicIons  on  new  data   POST prediction/v1.1/query/mybucket%2Fmydata/predict { "data":{ "input": { "text" : [ "J'aime X! C'est le meilleur" ]}}}
  51. Step  3:  Predict   Apply  the  trained  model  to  make  predicIons  on  new  data   POST prediction/v1.1/query/mybucket%2Fmydata/predict { "data":{ "input": { "text" : [ "J'aime X! C'est le meilleur" ]}}} { data : { "kind" : "prediction#output", "outputLabel":"French", "outputMulti" :[ {"label":"French", "score": x.xx} {"label":"English", "score": x.xx} {"label":"Spanish", "score": x.xx}]}}
  52. Step  3:  Predict   Apply  the  trained  model  to  make  predicIons  on  new  data   An  example  using  Python   import httplib header = {"Content-Type" : "application/json"} #...put new data in JSON format in params variable conn = httplib.HTTPConnection("www.googleapis.com")conn.request("POST", "/prediction/v1.1/query/mybucket%2Fmydata/predict”, params, header) print conn.getresponse()
  53. Prediction API Capabilities Data •  Input Features: numeric or unstructured text •  Output: up to hundreds of discrete categories Training •  Many machine learning techniques •  Automatically selected •  Performed asynchronously Access from many platforms: •  Web app from Google App Engine •  Apps Script (e.g. from Google Spreadsheet) •  Desktop app
  54. Prediction API v1.1 - features •  Updated Syntax •  Multi-category prediction o  Tag entry with multiple labels •  Continuous Output o  Finer grained prediction rankings based on multiple labels •  Mixed Inputs o  Both numeric and text inputs are now supported Can combine continuous output with mixed inputs
  55. Prediction API Demos •  Creating training data – recipes.csv •  Simple REST access •  Training the prediction engine •  Start predicting! •  A Java Web example
  56. Google BigQuery Interactive analysis of large datasets in Google's cloud
  57. Introducing Google BigQuery •  Google's large data adhoc analysis technology o  Analyze massive amounts of data in seconds •  Simple SQL-like query language •  Flexible access o  REST APIs, JSON-RPC, Google Apps Script
  58. Why  BigQuery?     Working  with  large  data  is  a  challenge  
  59. Many  Use  Cases  ...   InteracIve  Tools   Trends   Spam DetecIon   Web  Dashboards   Network   OpImizaIon  
  60. Key  CapabiliIes  of  BigQuery   •  Scalable: Billions of rows •  Fast: Response in seconds •  Simple: Queries in SQL •  Web Service o  REST o  JSON-RPC o  Google App Scripts
  61. Using BigQuery Another  simple  three  step  process...   Upload  your  raw  data  to   1.  Upload   Google  Storage     Import  raw  data  into  BigQuery  table   2.  Import   3.  Query   Perform  SQL  queries  on  table  
  62. Writing Queries Compact subset of SQL o  SELECT ... FROM ... WHERE ... GROUP BY ... ORDER BY ... LIMIT ...; Common functions o  Math, String, Time, ... Statistical approximations o  TOP o  COUNT DISTINCT
  63. BigQuery via REST GET /bigquery/v1/tables/{table name} GET /bigquery/v1/query?q={query} Sample JSON Reply: { "results": { "fields": { [ {"id":"COUNT(*)","type":"uint64"}, ... ] }, "rows": [ {"f":[{"v":"2949"}, ...]}, {"f":[{"v":"5387"}, ...]}, ... ] } } Also supports JSON-RPC
  64. Security and Privacy Standard Google Authentication •  Client Login •  OAuth •  AuthSub HTTPS support •  protects your credentials •  protects your data Relies on Google Storage to manage access
  65. Large Data Analysis Example Wikimedia  Revision  History   Wikimedia  Revision  history  data  from:  hWp://download.wikimedia.org/enwiki/latest/enwiki-­‐ latest-­‐pages-­‐meta-­‐history.xml.7z  
  66. Using  BigQuery  Shell   Python DB API 2.0 + B. Clapper's sqlcmd http://www.clapper.org/software/python/sqlcmd/
  67. BigQuery from a Spreadsheet
  68. BigQuery from a Spreadsheet
  69. Further  info  available  at:   •  Google Storage for Developers o  http://code.google.com/apis/storage •  Prediction API o  http://code.google.com/apis/predict •  BigQuery o  http://code.google.com/apis/bigquery
  70. Recap •  Google App Engine o  Google’s PaaS cloud development platform •  Google App Engine for Business o  New enterprise version of App Engine •  Google Storage o  New high speed data storage on Google Cloud •  Prediction API o  New machine learning technology able to predict outcomes based on sample data •  BigQuery o  New service for Interactive analysis of very large data sets using SQL
  71. Part III – Dabbling with CFML on App Engine •  How? –  Answer: Open BlueDragon •  OpenBD is a Java CFML runtime engine •  Has Google App Engine port •  Easy installation –  Just copy an example War directory to new GAE Java Web App –  Make sure to merge the jar files from the new WEBINF/lib with the existing WEBINF/lib
  72. Demo – App Engine’s Guestbook application on OpenBD •  http://guestbookcfm.appspot.com/
  73. Q&A
  74. Thank You! Chris Schalk Google Developer Advocate http://twitter.com/cschalk