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Introduction to Google's Cloud Technologies

This is a presentation given by Google Developer Advocate, Chris Schalk, on Google's cloud technologies and how to build and run CFML apps on them.

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Introduction to Google’s Cloud
                   Technologies




Chris	
  Schalk	
                     OpenCF	
  Summit	
  
Google	
  Developer	
  Advocate	
     Monday	
  Feb	
  21st,	
  2010	
  
Google Cloud Technologies at a Glance


ExisIng	
                                    Google	
  App	
  Engine	
  

                                             Google	
  App	
  Engine	
  for	
  Business	
  (new)	
  




 New!	
                                                      Google	
  	
  
              Google	
  BigQuery	
  
                                                          Predic0on	
  API	
  



                                       Google	
  Storage	
  
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
Part I – Intro to App Engine

Topics covered

•  App Engine a PaaS
•  App Engine usage/customers
•  App Engine Technical Details
Google App Engine
Build your own applications in Google's cloud
Cloud Computing as Gartner Sees It



       SaaS	
  

       PaaS	
  


       IaaS	
  

                  Source:	
  Gartner	
  AADI	
  Summit	
  Dec	
  2009	
  



                                                                            6	
  

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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  
  • 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  
  • 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
  • 22. Development Tools for App Engine 22  
  • 23. Google Plugin for Eclipse 23  
  • 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