Quick Intro to Google Cloud Technologies


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This is the "Lightning Presentation" given at DreamForce 2011 on Google's Cloud Technologies. It covers, App Engine, Google Storage and BigQuery. #df11

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Quick Intro to Google Cloud Technologies

  1. 1. A Quick Introduction to Googles CloudTechnologiesChris SchalkDeveloper Advocate@cschalk
  2. 2. Agenda● Introduction● Introduction to Googles Cloud Technologies● App Engine Review● Googles new Cloud Technologies ○ Google Storage ○ Prediction API ○ BigQuery● Summary Q&A
  3. 3. Googles Cloud Technologies Google App Engine Google Google BigQuery Prediction API Google Storage
  4. 4. Google App EngineA quick look at App Engine...
  5. 5. 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
  6. 6. Quick App Engine Demo!
  7. 7. Specialized ServicesMemcache Datastore URL FetchMail XMPP Task QueueImages Blobstore User Service But, is that it?
  8. 8. No!Now App Engine has access to all thenew Google Cloud Services...
  9. 9. Googles new Cloud Technologies
  10. 10. New Google Cloud Technologies ● Google Storage ○ Store your data in Googles cloud ● Prediction API ○ Googles machine learning tech in an API ● BigQuery ○ Hi-speed data analysis on massive scale ● ...
  11. 11. Google Storage for Developers Store your data in Googles cloud
  12. 12. What Is Google Storage? ● Store your data in Googles cloud ○ any format, any amount, any time ● You control access to your data ○ private, shared, or public ● Access via Google APIs or 3rd party tools/libraries
  13. 13. 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
  14. 14. 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
  15. 15. Google Storage Technical Details ● RESTful API ○ Verbs: GET, PUT, POST, HEAD, DELETE ○ Resources: identified by URI ○ Compatible with S3 ● Buckets ○ Flat containers ● Objects ○ Any type ○ Size: 100 GB / object ● Access Control for Google Accounts ○ For individuals and groups ● Two Ways to Authenticate Requests ○ Sign request using access keys ○ Web browser login
  16. 16. Demo● Tools: ○ GSUtil ○ GS Manager● Upload / Download
  17. 17. Google Storage usage within Google Google Google BigQuery Prediction API Haiti Relief Imagery USPTO data Partner Reporting Partner Reporting
  18. 18. Some Early Google Storage Adopters
  19. 19. Google Storage - Pricing ○ Free trial quota until Dec 31, 2011 ■ For first project ■ 5 GB of storage ■ 25 GB download/upload data ■ 20 GB to Americas/EMEA, 5GB APAC ■ 25K GET, HEAD requests ■ 2,5K PUT, POST, LIST* requests ○ Production Storage ■ $0.17/GB/Month (Location US, EU) ■ Upload - $0.10/GB ■ Download ■ $0.15/GB Americas / EMEA ■ $0.30/GB APAC ■ Requests ■ PUT, POST, LIST - $0.01 / 1000 Requests ■ GET, HEAD - $0.01 / 10,000 Requests ■ Up to 99.9% SLA
  20. 20. Google Storage Summary ● Store any kind of data using Googles cloud infrastructure ● Easy to Use APIs ● Many available tools and libraries ○ gsutil, GS Manager ○ 3rd party: ■ Boto, CloudBerry, CyberDuck, JetS3t, and more
  21. 21. Google Prediction APIGoogles prediction engine in the cloud
  22. 22. Google Prediction API as a simple example Predicts outcomes based on learned patterns
  23. 23. How does it work? "english" The quick brown fox jumped over theThe Prediction API lazy dog.finds relevant "english" To err is human, but to really foul thingsfeatures in the up you need a computer.sample data during "spanish" No hay mal que por bien no venga.training. "spanish" La tercera es la vencida.The Prediction APIlater searches for ? To be or not to be, that is the question.those features ? La fe mueve montañas.during prediction.
  24. 24. A virtually endless number of applications... Customer Transaction Species Message Diagnostics Sentiment Risk Identification Routing Churn Legal Docket Suspicious Work Roster InappropriatePrediction Classification Activity Assignment ContentRecommend Political Uplift Email Career Products Bias Marketing Filtering Counselling ... and many more ...
  25. 25. Using the Prediction APIA 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 predictions
  26. 26. 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}
  27. 27. Step 2: TrainCreate a new model by training on dataTo train a model:POST prediction/v1.3/training{"id":"mybucket/mydata"}Training runs asynchronously. To see if it has finished:GET prediction/v1.3/training/mybucket%2Fmydata{"kind": "prediction#training",...,"training status": "DONE"}
  28. 28. Step 3: Predict Apply the trained model to make predictions on new dataPOSTprediction/v1.3/training/mybucket%2Fmydata/predict{ "data":{ "input": { "text" : [ "Jaime X! Cest le meilleur" ]}}}
  29. 29. Step 3: Predict Apply the trained model to make predictions on new dataPOST prediction/v1.3/training/bucket%2Fdata/predict{ "data":{ "input": { "text" : [ "Jaime X! Cest le meilleur" ]}}}{ data : { "kind" : "prediction#output", "outputLabel":"French", "outputMulti" :[ {"label":"French", "score": x.xx} {"label":"English", "score": x.xx} {"label":"Spanish", "score": x.xx}]}}
  30. 30. Step 3: Predict Apply the trained model to make predictions on new dataimport httplibheader = {"Content-Type" : "application/json"}#...put new data in JSONformat in params variableconn = httplib.HTTPConnection("www.googleapis.com")conn.request("POST", "/prediction/v1.3/query/bucket%2Fdata/predict", params, header)printconn.getresponse()
  31. 31. Google Prediction API Demo! ● Command line Demos ○ Training a model ○ Checking training status ○ Making predictions ● A complete Web application using the JavaScript API for Prediction
  32. 32. Prediction API CapabilitiesData ● Input Features: numeric or unstructured text ● Output: up to hundreds of discrete categoriesTraining ● Many machine learning techniques ● Automatically selected ● Performed asynchronouslyAccess from many platforms: ● Web app from Google App Engine ● Apps Script (e.g. from Google Spreadsheet) ● Desktop app
  33. 33. Prediction API - key features ● Multi-category prediction ○ Tag entry with multiple labels ● Continuous Output ○ Finer grained prediction rankings based on multiple labels ● Mixed Inputs ○ Both numeric and text inputs are now supportedCan combine continuous output with mixed inputs
  34. 34. Google BigQueryInteractive analysis of large datasets in Googles cloud
  35. 35. Introducing Google BigQuery ● Googles large data adhoc analysis technology ○ Analyze massive amounts of data in seconds ● Simple SQL-like query language ● Flexible access ○ REST APIs, JSON-RPC, Google Apps Script
  36. 36. Why BigQuery?Working with large data is a challenge
  37. 37. Many Use Cases ... Interactive Trends Spam Tools Detection Web Network Dashboards Optimization
  38. 38. Key Capabilities of BigQuery ● Scalable: Billions of rows ● Fast: Response in seconds ● Simple: Queries in SQL ● Web Service ○ REST ○ JSON-RPC ○ Google App Scripts
  39. 39. Using BigQueryAnother simple three step process... Upload your raw data to 1. Upload Google Storage Import raw data into 2. Import BigQuery table 3. Query Perform SQL queries on table
  40. 40. Writing QueriesCompact subset of SQL ○ SELECT ... FROM ... WHERE ... GROUP BY ... ORDER BY ... LIMIT ...;Common functions ○ Math, String, Time, ...Statistical approximations ○ TOP ○ COUNT DISTINCT
  41. 41. BigQuery via RESTGET /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
  42. 42. Security and PrivacyStandard Google Authentication ● Client Login ● OAuth ● AuthSubHTTPS support ● protects your credentials ● protects your dataRelies on Google Storage to manage access
  43. 43. Large Data Analysis ExampleWikimedia Revision HistoryWikimedia Revision history data from: http://download.wikimedia.org/enwiki/latest/enwiki-latest-pages-meta-history.xml.7z
  44. 44. Using BigQuery ShellPython DB API 2.0 + B. Clappers sqlcmdhttp://www.clapper.org/software/python/sqlcmd/
  45. 45. BigQuery from a Spreadsheet
  46. 46. BigQuery from a Spreadsheet
  47. 47. Recap ● Google App Engine ○ Application development platform for the cloud ● Google Storage ○ High speed cloud data storage on Googles infrastructure ● Prediction API ○ Googles machine learning technology able to predict outcomes based on sample data ● BigQuery ○ Interactive analysis of very large data sets ○ Simple SQL query language access
  48. 48. Further info available at:● Google App Engine ○ http://code.google.com/appengine● Google Storage for Developers ○ http://code.google.com/apis/storage● Prediction API ○ http://code.google.com/apis/predict● BigQuery ○ http://code.google.com/apis/bigquery
  49. 49. Thank you!Questions? ● @cschalk