Introduction to Google's Cloud Technologies

Introduction to Google’s Cloud TechnologiesChris Schalk OpenCF Summit Google Developer Advocate Monday Feb 21st, 2010

Google Cloud Technologies at a GlanceExisIng 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 EngineTopics covered•  App Engine a PaaS•  App Engine usage/customers•  App Engine Technical Details

Google App EngineBuild your own applications in Googles cloud

Cloud Computing as Gartner Sees It SaaS PaaS IaaS Source: Gartner AADI Summit Dec 2009 6

Why Google App Engine? • Easy to build • Easy to maintain • Easy to scale 7

By the Numbers 150,000+ ac0ve apps on a weekly basis 8 8

By the Numbers 100,000+ developers use every month 9 9

By the Numbers1B+ daily pageviews 10 10

Some App Engine Partners 11

App Engine Details 12

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

Specialized Services Memcache Datastore URL Fetch Mail XMPP Task Queue Images Blobstore User Service 14

Ensuring Portability16

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

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

Manage your expansion – Easy Billing 19

Application Platform Management20

App Engine Dashboard21

Development Tools for App Engine22

Google Plugin for Eclipse 23

SDK Console24

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 Data25

App Engine 1.4 Release New Features1. 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>

App Engine 1.4 Release New Features4. 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

Introducing App Engine for Business App Engine for BusinessSame scalable cloud platform, but designed for the Enterprise 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 month30

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 201132

App Engine ResourcesGet started with App Engine•  http://code.google.com/appengineRead up on App Engine for Business and become a trusted tester•  http://code.google.com/appengine/business

App Engine Demos•  App Engine •  Getting started•  App Engine for Business •  Domain Console •  Guestbook on SQL on GAE4B

Part II - Google’s new Cloud TechnologiesTopics covered•  Google Storage for Developers•  Prediction API (machine learning)•  BigQuery

Google Storage for Developers Store your data in Googles cloud

What Is Google Storage?•  Store your data in Googles 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

Sample Use CasesStatic content hostinge.g. static html, images, music, videoBackup and recoverye.g. personal data, business recordsSharinge.g. share data with your customersData storage for applicationse.g. used as storage backend for Android, AppEngine, Cloud based appsStorage for Computatione.g. BigQuery, Prediction API

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

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#

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 Googles worldwide network for data delivery•  Only you can use bucket names with your domain names•  Read-your-writes data consistency•  Range Get

Some Early Google Storage Adopters

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)

Demo•  Tools: o  GS Manager o  GSUtil•  Upload / Download

Google Prediction APIGoogles prediction engine in the cloud

Introducing the Google Prediction API•  Googles sophisticated machine learning technology•  Available as an on-demand RESTful HTTP web service

How does it work? "english" The quick brown fox jumped over the lazy The Prediction API dog. finds relevantfeatures 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.

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 ...

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 predicIons

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}

Step 2: Train Create a new model by training on data To train a model:POST prediction/v1.1/training?data=mybucket%2FmydataTraining runs asynchronously. To see if it has finished:GET prediction/v1.1/training/mybucket%2Fmydata{"data":{ "data":"mybucket/mydata", "modelinfo":"estimated accuracy: 0.xx"}}}

Step 3: Predict Apply the trained model to make predicIons on new data POST prediction/v1.1/query/mybucket%2Fmydata/predict{ "data":{ "input": { "text" : [ "Jaime X! Cest le meilleur" ]}}}

Step 3: Predict Apply the trained model to make predicIons on new data POST prediction/v1.1/query/mybucket%2Fmydata/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}]}}

Step 3: Predict Apply the trained model to make predicIons on new data An example using Python import httplibheader = {"Content-Type" : "application/json"}#...put new data in JSON format in params variableconn = httplib.HTTPConnection("www.googleapis.com")conn.request("POST", "/prediction/v1.1/query/mybucket%2Fmydata/predict”, params, header)print conn.getresponse()

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

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 supportedCan combine continuous output with mixed inputs

Prediction API Demos•  Creating training data – recipes.csv•  Simple REST access •  Training the prediction engine •  Start predicting!•  A Java Web example

Google BigQueryInteractive analysis of large datasets in Googles cloud

Introducing Google BigQuery•  Googles 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

Why BigQuery? Working with large data is a challenge

Many Use Cases ... InteracIve Tools Trends Spam DetecIon Web Dashboards Network OpImizaIon

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

Using BigQueryAnother 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

Writing QueriesCompact 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

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

Security and PrivacyStandard Google Authentication•  Client Login•  OAuth•  AuthSubHTTPS support•  protects your credentials•  protects your dataRelies on Google Storage to manage access

Large Data Analysis ExampleWikimedia Revision History Wikimedia Revision history data from: hWp://download.wikimedia.org/enwiki/latest/enwiki-‐latest-‐pages-‐meta-‐history.xml.7z

Using BigQuery Shell Python DB API 2.0 + B. Clappers sqlcmdhttp://www.clapper.org/software/python/sqlcmd/

BigQuery from a Spreadsheet

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

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

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

Demo – App Engine’s Guestbook application on OpenBD •  http://guestbookcfm.appspot.com/

Thank You!Chris SchalkGoogle DeveloperAdvocatehttp://twitter.com/cschalk

http://www.mongodb.org	3
http://us-w1.rockmelt.com	2
http://www.istikbal.gr	2
http://www.onlydoo.com	2
http://paper.li	2
http://www.gnav.ma	1
http://preacherspen.org	1
http://www.demysex.org.mx	1
http://twitter.com	1

Introduction to Google's Cloud Technologies

by Chris Schalk on Feb 23, 2011

Accessibility

Categories

Tags

Upload Details

Usage Rights

9 Embeds 15

Statistics

Introduction to Google’s Cloud Technologies — Presentation Transcript