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Introduction to Google’s Cloud Platform Technologies
Chris Schalk Google Developer Advocate
Cloudstock Monday Dec 6th, 2010
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What is cloud compu/ng?
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Just Kidding ;‐)
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Google Cloud Platform Technologies at Glance
Google BigQuery Google Predic/on API
Google Storage
Google App Engine
Google App Engine for Business (new)
ExisFng
New!
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• 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
Agenda
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Part I – Intro to App Engine
Topics covered
• App Engine a PaaS • App Engine usage/customers • App Engine Technical Details
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Google App Engine Build your own applications in Google's cloud
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IaaS
PaaS
SaaS
Source: Gartner AADI Summit Dec 2009
Cloud Computing as Gartner Sees It
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• Easy to build
• Easy to maintain
• Easy to scale
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Why Google App Engine?
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500M+ daily Pageviews
250,000+ Developers 100,000+ Apps
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By the Numbers
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Some App Engine Partners
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App Engine Details
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Cloud Development in a Box
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• 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
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Specialized Services
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Blobstore Images
Mail XMPP Task Queue
Memcache Datastore URL Fetch
User Service
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Language Runtimes
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Duke, the Java mascot Copyright © Sun Microsystems Inc., all rights reserved.
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Ensuring Portability
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Extended Language support through JVM
• Java • Scala • JRuby (Ruby) • Groovy • Quercus (PHP) • Rhino (JavaScript) • Jython (Python)
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Duke, the Java mascot Copyright © Sun Microsystems Inc., all rights reserved.
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Always free to get started
• ~5M pageviews/month • 6.5 CPU hrs/day • 1 GB storage • 650K URL Fetch calls/day • 2,000 recipients emailed • 1 GB/day bandwidth • 100,000 tasks enqueued • 650K XMPP messages/day
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Application Platform Management
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App Engine Dashboard
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App Engine Health History
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Development Tools for App Engine
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Google App Engine Launcher
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SDK Console
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Google Plugin for Eclipse
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Two+ years in review
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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
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Introducing App Engine for Business
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Same scalable cloud platform, but designed for the Enterprise
App Engine for Business
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Google App Engine for Business Details
• Enterprise application management – Centralized domain console (preview available)
• Enterprise reliability and support – 99.9% Service Level Agreement – 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
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Google App Engine for Business
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Enterprise App Development with Google
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Build your own
Google App Engine for Business
Buy from others
Google Apps Marketplace
Enterprise Firewall
Enterprise Data AuthenFcaFon Enterprise Services User Management
Buy from Google
Google Apps for Business
Enterprise Application Platform
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App Engine for Business Roadmap
Enterprise Administration Console Preview (signups available)
Direct Support Preview (signups available)
Hosted SQL Preview (signups available)
Service Level Agreement Available Q4 2010 (Draft published)
Enterprise billing Available Q4 2010
Custom Domain SSL Limited Release EOY 2010
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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
• bit.ly/gae4btt <- sign up!
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Part II - Google’s new Cloud Technologies
Topics covered • Google Storage for Developers • Prediction API (machine learning) • BigQuery
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Google Storage for Developers Store your data in Google's cloud
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What Is Google Storage?
• Store your data in Google's cloud o any format, any amount, any Fme
• You control access to your data o private, shared, or public
• Access via Google APIs or 3rd party tools/libraries
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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
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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
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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
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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
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Demo
• Tools: o GS Manager o GSUtil
• Upload / Download
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Google Storage usage within Google
HaiF Relief Imagery USPTO data
Partner ReporFng
Google BigQuery Google Predic/on API
Partner ReporFng
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Some Early Google Storage Adopters
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Google Storage - Pricing
o Storage $0.17/GB/Month
o Network Upload - $0.10/GB Download
$0.15/GB Americas / EMEA $0.30/GB APAC
o Requests PUT, POST, LIST - $0.01 / 1000 Requests GET, HEAD - $0.01 / 10000 Requests
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Google Storage - Availability
• Limited 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
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Google Prediction API Google's prediction engine in the cloud
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Introducing the Google Prediction API
• Google's sophisticated machine learning technology • Available as an on-demand RESTful HTTP web service
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How does it work?
"english" The quick brown fox jumped over the lazy dog.
"english" To err is human, but to really foul things up you need a computer.
"spanish" No hay mal que por bien no venga.
"spanish" La tercera es la vencida.
? To be or not to be, that is the quesFon.
? La fe mueve montañas.
The Prediction API finds relevant features in the sample data during training.
The PredicFon API later searches for those features during predicFon.
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A virtually endless number of applicaFons...
Customer Sentiment
TransacFon Risk
Species IdenFficaFon
Message RouFng
Legal Docket ClassificaFon
Suspicious AcFvity
Work Roster Assignment
Recommend Products
PoliFcal Bias
Uplij MarkeFng
Email Filtering
DiagnosFcs
Inappropriate Content
Career Counselling
Churn PredicFon
... and many more ...
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A PredicFon API Example
AutomaFcally categorize and respond to emails by language
• Customer: ACME Corp, a multinational organization • Goal: Respond to customer emails in their language • Data: Many emails, tagged with their languages
• Outcome: Predict language and respond accordingly
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Using the Prediction API
1. Upload
2. Train
Upload your training data to Google Storage
Build a model from your data
Make new predicFons 3. Predict
A simple three step process...
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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}
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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"}}}
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Step 3: Predict Apply the trained model to make predicFons on new data
POST prediction/v1.1/query/mybucket%2Fmydata/predict { "data":{ "input": { "text" : [ "J'aime X! C'est le meilleur" ]}}}
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Step 3: Predict Apply the trained model to make predicFons 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}]}}
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Step 3: Predict Apply the trained model to make predicFons 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/v1.1/query/mybucket%2Fmydata/predict”, params, header)
print conn.getresponse()
An example using Python
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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
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Prediction API v1.1 - new 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
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Google BigQuery Interactive analysis of large datasets in Google's cloud
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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
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Why BigQuery?
Working with large data is a challenge
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Many Use Cases ...
Spam Trends
DetecFon
Web Dashboards Network OpFmizaFon
InteracFve Tools
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Key CapabiliFes 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
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Using BigQuery
1. Upload
2. Import
Upload your raw data to Google Storage
Import raw data into BigQuery table
Perform SQL queries on table 3. Query
Another simple three step process...
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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
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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
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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
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Large Data Analysis Example
Wikimedia Revision history data from: hmp://download.wikimedia.org/enwiki/latest/enwiki‐latest‐pages‐meta‐history.xml.7z
Wikimedia Revision History
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Using BigQuery Shell
Python DB API 2.0 + B. Clapper's sqlcmd http://www.clapper.org/software/python/sqlcmd/
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BigQuery from a Spreadsheet
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BigQuery from a Spreadsheet
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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
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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
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Q&A
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Thank You!
Chris Schalk Google Developer Advocate
http://twitter.com/cschalk