GDD Brazil 2010 - Google Storage, Bigquery and Prediction APIs

Google Storage, Bigquery and Prediction APIs
Patrick Chanezon, Developer Advocate, Cloud
@chanezon, chanezon@google.com
Sao Paulo, October 29th 2010
Developer DayGoogle 2010
Friday, October 29, 2010

Mobile Agenda for GDD
http://bit.ly/mgddbr

Agenda
• Google Storage for Developers
• Prediction API
• BigQuery

What is
cloud
computing?
Infrastructure…
Platform…
Software…
… as a Service

What is
cloud
computing?
Place
Postage
Here
IaaS
PaaS
SaaS
Infrastructure…
Platform…
Software…
…asaService

Google Storage
Prediction API
BigQuery
1. Google Apps
2. Third party Apps:
Google Apps Marketplace
3. ________
5
Google App Engine
IaaS
PaaS
SaaS
Google's Cloud Offerings

Google Storage
Prediction API
BigQuery
Your Apps
1. Google Apps
2. Third party Apps:
Google Apps Marketplace
3. ________
5
Google App Engine
IaaS
PaaS
SaaS
Google's Cloud Offerings

Google Storage for Developers
Store your data in Google's cloud

What Is Google Storage?
• Store your data in Google's cloud
o any format, any amount, any time
• You control access to your data
o private, shared, or public
• Access via Google APIs or 3rd party tools/libraries

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, App Engine,
Cloud based apps
Storage for Computation
e.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, i.e. no bucket hierarchy

• 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
• Leveraging Google's worldwide network for data delivery
• Only you can use bucket names with your domain names
• “Read-your-writes” data consistency
• Range Get

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

Tools
Google Storage Manager
gsutil

Google Storage usage within Google
Haiti Relief Imagery USPTO data
Partner Reporting
Google
BigQuery
Google
Prediction API
Partner Reporting

Some Early Google Storage Adopters

Google Storage - Pricing
o Storage
$0.17/GB/Month
o Network
Upload - $0.10/GB
Download
$0.30/GB APAC
$0.15/GB Americas / EMEA
o Requests
PUT, POST, LIST - $0.01 / 1,000 Requests
GET, HEAD - $0.01 / 10,000 Requests

Google Storage - Availability
• Limited preview in US* currently
o 100GB free storage and network per account
o Sign up for wait list at
o http://code.google.com/apis/storage/
* Non-US preview available on case-by-case basis

Google Storage Summary
• Store any kind of data using Google's cloud infrastructure
• Easy to Use APIs
• Many available tools and libraries
o gsutil, Google Storage Manager
o 3rd party:
Boto, CloudBerry, CyberDuck, JetS3t, …

Google Prediction API
Google's prediction engine in the cloud

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

"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
question.
? La fe mueve montañas.
2. PREDICT
The Prediction API
later searches for
those features
during prediction.
How does it work?
1. TRAIN
The Prediction API
finds relevant
features in the
sample data during
training.

Customer
Sentiment
Transaction
Risk
Species
Identification
Message
Routing
Legal Docket
Classification
Suspicious
Activity
Work Roster
Assignment
Recommend
Products
Political
Bias
Uplift
Marketing
Email
Filtering
Diagnostics
Inappropriate
Content
Career
Counseling
Churn
Prediction
... and many more ...
A virtually endless number of applications...

Automatically 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
A Prediction API Example

Using the Prediction API
1. Upload
2. Train
Upload your training data to
Google Storage
Build a model from your data
Make new predictions3. Predict
A simple three step process...

Upload your training data to Google Storage
• Training data: outputs and input features
• Data format: comma separated value format (CSV), result in first column
"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 1: Upload

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"}}}
Step 2: Train

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

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}]}}
Step 3: Predict

import httplib
# put new data in JSON format
params = { ... }
header = {"Content-Type" : "application/json"}
conn =
httplib.HTTPConnection("www.googleapis.com")conn.reques
t("POST",
"/prediction/v1.1/query/mybucket%2Fmydata/predict",
params, header)
print conn.getresponse()
Step 3: Predict

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
Prediction API Capabilities

• 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
Prediction API v1.1 - new features

Google BigQuery
Interactive analysis of large datasets in Google's cloud

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

Working with large data is a challenge
Why BigQuery?

Spam
Trends
Detection
Web
Dashboards
Network
Optimization
Interactive
Tools
Many Use Cases ...

• Scalable: Billions of rows
• Fast: Response in seconds
• Simple: Queries in SQL
• Web Service
oREST
oJSON-RPC
oGoogle App Scripts
Key Capabilities of 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...
Using BigQuery

Compact subset of SQL
o SELECT ... FROM ...
WHERE ...
GROUP BY ... ORDER BY ...
LIMIT ...;
Common functions
o Math, String, Time, ...
Additional statistical approximations
o TOP
o COUNT DISTINCT
Writing Queries

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
BigQuery via REST

Standard Google Authentication
• Client Login
• OAuth
• AuthSub
HTTPS support
• protects your credentials
• protects your data
Relies on Google Storage to manage access
Security and Privacy

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

Python DB API 2.0 + B. Clapper's sqlcmd
http://www.clapper.org/software/python/sqlcmd/
Using BigQuery Shell

BigQuery from a Spreadsheet

Input Data: http://delic.io.us/chanezon
–6000 urls, 14000 tags in 6 years
Analyze my delicious tags
–use delicious API to get all tagged urls
–cleanup data, resize (100Mb limit)
–PUT data in Google storage
–Define table
–analyze
Predict how I would tag a technology article
–input is tag,url,text
–send new url and text
–get predicted tag
Prediction API and BigQuery Demo: Tagger

Nick Johnson’s blog
–http://blog.notdot.net/2010/06/Trying-out-the-
new-Prediction-API
–42,753 submissions, for a week
–63% accuracy, to categorize new submissions
Guessing Subreddits with Prediction API

• Google Storage
o High speed data storage on Google Cloud
• Prediction API
o Google's machine learning technology able to
predict outcomes based on sample data
• BigQuery
o Interactive analysis of very large data sets
o Simple SQL query language access
Recap

• Google Storage for Developers
o http://code.google.com/apis/storage
• Prediction API
o http://code.google.com/apis/prediction
• BigQuery
o http://code.google.com/apis/bigquery
More information

GDD Brazil 2010 - Google Storage, Bigquery and Prediction APIs

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