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Building Integrated Applications on Google's Cloud Technologies

This is the presentation "Building Integrated Applications on Google's Cloud Technologies" that was given at GDD 2011 #gdd11 in Sao Paulo and Buenos Aires by Google Developer Advocate Chris Schalk @cschalk.

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Chris Schalk
Google Developer Advocate


Creación de aplicaciones
integradas en las tecnologías de
nube de Google
(Building Integrated Applications on
Google's Cloud Technologies)
Agenda

● Introduction

● Introduction to Google's Cloud Technologies

● App Engine Recap

● Google's new Cloud Technologies
   ○ Google Storage
   ○ Prediction API
   ○ BigQuery

● Summary Q&A
Google's Cloud Technologies




                Google App Engine



           Google                 Google
          BigQuery             Prediction API



                     Google
                     Storage
Google App Engine




An App Engine recap...
Cloud Development in a Box
● Downloadable SDK

● Application runtimes
   ○ Java, Python, (Go)

● Local development tools
    ○ Eclipse plugin
    ○ App Engine Launcher

● Specialized application services

● Cloud based dashboard

● Ready to scale
   ○ Built in fault tolerance, load
     balancing
Quick GAE Demo!




Building, testing and deploying a new cloud app

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Building Integrated Applications on Google's Cloud Technologies

  • 1. Chris Schalk Google Developer Advocate Creación de aplicaciones integradas en las tecnologías de nube de Google (Building Integrated Applications on Google's Cloud Technologies)
  • 2. Agenda ● Introduction ● Introduction to Google's Cloud Technologies ● App Engine Recap ● Google's new Cloud Technologies ○ Google Storage ○ Prediction API ○ BigQuery ● Summary Q&A
  • 3. Google's Cloud Technologies Google App Engine Google Google BigQuery Prediction API Google Storage
  • 4. Google App Engine An App Engine recap...
  • 5. Cloud Development in a Box ● Downloadable SDK ● Application runtimes ○ Java, Python, (Go) ● Local development tools ○ Eclipse plugin ○ App Engine Launcher ● Specialized application services ● Cloud based dashboard ● Ready to scale ○ Built in fault tolerance, load balancing
  • 6. Quick GAE Demo! Building, testing and deploying a new cloud app
  • 7. Specialized Services Memcache Datastore URL Fetch Mail XMPP Task Queue Images Blobstore User Service But, is that it?
  • 8. No! App Engine now has access to new specialized cloud services...
  • 9. Google's new Cloud Technologies
  • 10. New Google Cloud Technologies ● Google Storage ○ Store your data in Google's cloud ● Prediction API ○ Google's machine learning tech in an API ● BigQuery ○ Hi-speed data analysis on massive scale
  • 11. Google Storage for Developers Store your data in Google's cloud
  • 12. What Is Google Storage? ● Store your data in Google's 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. 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. 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. 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. Security and Privacy Features ● Key-based authentication ● Authenticated downloads from a web browser ● Sharing with individuals ● Group sharing via Google Groups ● Access control for buckets and objects ● Set Read/Write/List permissions
  • 17. Demo ● Tools: ○ GSUtil ○ GS Manager ● Upload / Download
  • 18. Google Storage usage within Google Google Google BigQuery Prediction API Haiti Relief Imagery USPTO data Partner Reporting Partner Reporting
  • 19. Some Early Google Storage Adopters
  • 20. 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 ■ 99.9% uptime SLA
  • 21. Google Storage Summary ● Store any kind of data using Google's cloud infrastructure ● Easy to Use APIs ● Many available tools and libraries ○ gsutil, GS Manager ○ 3rd party: ■ Boto, CloudBerry, CyberDuck, JetS3t, and more
  • 22. Google Prediction API Google's prediction engine in the cloud
  • 23. Google Prediction API as a simple example Predicts outcomes based on 'learned' patterns
  • 24. How does it work? "english" The quick brown fox jumped over the The Prediction API lazy dog. finds relevant "english" To err is human, but to really foul things features 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 API later searches for ? To be or not to be, that is the question. those features ? La fe mueve montañas. during prediction.
  • 25. A virtually endless number of applications... Customer Transaction Species Message Diagnostics Sentiment Risk Identification Routing Churn Legal Docket Suspicious Work Roster Inappropriate Prediction Classification Activity Assignment Content Recommend Political Uplift Email Career Products Bias Marketing Filtering Counselling ... and many more ...
  • 26. 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 predictions
  • 27. 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}
  • 28. Step 2: Train Create a new model by training on data To 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"}
  • 29. Step 3: Predict Apply the trained model to make predictions on new data POST prediction/v1.3/training/mybucket%2Fmydata/predict { "data":{ "input": { "text" : [ "J'aime X! C'est le meilleur" ]}}}
  • 30. Step 3: Predict Apply the trained model to make predictions on new data POST prediction/v1.3/training/bucket%2Fdata/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}]}}
  • 31. Step 3: Predict Apply the trained model to make predictions on new data 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/v1.3/query/bucket%2Fdata/predict", params, header)print conn.getresponse()
  • 32. Prediction API - Pricing ○ Free Quota ■ Free trial quota for first 6 months (per project) ■ 100 predictions/day ■ 5 MB trained/day ■ 100 Streaming updates ■ Lifetime cap: 20,000 predictions ○ Paid Usage ■ 99.9% availability SLA ■ Base fee: $10 monthly fee per project ■ Prediction: ■ 10,000 predictions/month: $0.00 (free) ■ $0.50/1,000 predictions (beyond initial 10k) ■ Training ■ $0.002/MB bulk trained (dataset max size: 250MB) ■ 0-10k streaming updates: $0.00 (free) ■ $0.05/1,000 updates (beyond initial 10k)
  • 33. Demos! ● Command line Demos ○ Training a model ○ Checking training status ○ Making predictions ● A complete Web application using the JavaScript API for Prediction
  • 34. 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
  • 35. 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 supported Can combine continuous output with mixed inputs
  • 36. Google BigQuery Interactive analysis of large datasets in Google's cloud
  • 37. Introducing Google BigQuery ● Google's 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
  • 38. Many Use Cases ... Interactive Trends Spam Tools Detection Web Network Dashboards Optimization
  • 39. Key Capabilities of BigQuery ● Scalable: Billions of rows ● Fast: Response in seconds ● Simple: Queries in SQL ● Web Service ○ REST ○ JSON-RPC ○ Google App Scripts
  • 40. Using BigQuery Another 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
  • 41. Writing Queries Compact subset of SQL ○ SELECT ... FROM ... WHERE ... GROUP BY ... ORDER BY ... LIMIT ...; Common functions ○ Math, String, Time, ... Statistical approximations ○ TOP ○ COUNT DISTINCT
  • 42. 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
  • 43. BigQuery Security and Privacy Standard Google Authentication ● Client Login ● AuthSub ● OAuth HTTPS support ● protects your credentials ● protects your data Relies on Google Storage to manage access
  • 44. Large Data Analysis Example Wikimedia Revision History Wikimedia Revision history data from: http://download.wikimedia. org/enwiki/latest/enwiki-latest-pages-meta-history.xml.7z
  • 45. BigQuery from a Spreadsheet
  • 46. BigQuery from a Spreadsheet
  • 47. Recap ● Google App Engine ○ Application development platform for the cloud ● Google Storage ○ High speed cloud data storage on Google's infrastructure ● Prediction API ○ Google's 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. 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. Muchas Gracias! Questions? Contact: @cschalk