This document provides an overview of Google App Engine and its support for Java applications. It discusses key features like automatic scaling, usage-based billing, and built-in services. It explains that App Engine runs web apps on Google's infrastructure without requiring installation or maintenance of servers. The document demonstrates the App Engine architecture and development tools. It also summarizes the Datastore service and how it differs from relational databases while providing scalable storage and queries.

1. Google App Engine
Now Serving Java
Chris Schalk
June 29, 2009

2. Goals
• Help you understand...
– what App Engine is.
– what App Engine is not.
– where App Engine preserves programming models.
– where App Engine changes programming models.
• Demonstrate that App Engine really is fast and free to get
started.
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3. Overview
• Google App Engine
• Java on App Engine
• The App Engine Datastore
• Demo
• Questions
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4. What Is Google App Engine?
• A cloud-computing platform
• Run your web apps on Google’s infrastructure
• We provide the container and services (PaaS)
– Hardware, connectivity
– Operating system
– JVM
– Servlet container
– Software services
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5. Key Features
• No need to install or maintain your own stack
• We scale for you
• Use Google’s scalable services via standard APIs
• Charge only for actual usage
– Always free to get started
• Built-in application management console
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6. App Engine Architecture
Incoming Requests
App Engine App Engine App Engine
Front End Front End Front End
Load
Balancer
AppServer AppServer AppServer
Other Google
AppServer Infrastructure
API Layer - Bigtable
- Google Accounts
- Memcache
App App App
- Image manipulation
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7. When To Use Google App Engine
• Targeting web applications
– Serve HTTP requests, limited to 30 seconds
– No long-running background processes
– No server push
• Sandboxed environment
– No threads
– Read-only file system
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8. Java Support
• Servlets
• Software services
• Sandboxing
• DevAppServer
• Deployment
• Tooling
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9. Demo!
Java App Engine Basics

10. Servlet API
• Full Servlet 2.5 Container
– HTTP Session
– JSP
• Uses Jetty and Jasper
– Powered by Google’s HTTP stack
– No Jetty-specific features
– Subject to change
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11. Software Services
Authentication Servlet API Google Accounts
Datastore JPA, JDO Bigtable
Caching javax.cache memcacheg
E-mail javax.mail Gmail gateway
URLFetch URLConnection Caching HTTP proxy
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12. Sandboxing
• What do we do?
– Restrict JVM permissions
– WhiteList classes
• Why is it necessary?
– Clustering - JVMs come and go
– Protect applications from one another
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13. Sandboxing Restrictions
Threads
Async API (coming soon)
Direct network connections
URLConnection
Direct file system writes
Memory, memcache, datastore
Images API
Java2D
Software rendering
Native code
Pure Java libraries
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14. Flexible Sandboxing
• JVM Permissions often too coarse
• Reflection
– Access private fields, call private methods
• Class Loading
– Custom Class Loaders
– Dynamic bytecode
• Alternate JVM languages
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15. DevAppServer
• Emulates the production environment
• Customized Jetty server
• Local implementation of services
– LRU memcache
– Disk-backed datastore
– HttpClient-backed URLFetch
• Some sandbox restrictions difficult to emulate
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16. Deployment
• Your app lives at
– <app_id>.appspot.com, or
– Custom domain with Google Apps for your Domain
• Command line and IDE tools
• Application
• Datastore Indexes
• Cron Jobs
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17. Quotas and Billing
CPU 6.5 hours/day $0.10/hour
Bandwidth In 1GByte/day $0.10/GByte
Bandwidth Out 1GByte/day $0.12/GByte
Stored Data 1 GB $0.005/GB-day
2000/day to users
Emails sent $0.0001/email
5000/day to admins
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18. Tooling
• SDK Tools API
– Command-line tools, Ant, and IDE plugins
• Provides
– Deployment
– DevAppServer
– WhiteList for compile-time checks
– XML validation
• Google Eclipse Plugin
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19. The Datastore Is...
• Distributed
• Scalable
• Transactional
• Natively Partitioned
• Hierarchical
• Schema-less
• Based on Bigtable
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20. The Datastore Is Not...
• A relational database
• A SQL engine
• Just Bigtable
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21. Simplifying Storage
• Simplify development of apps
• Simplify management of apps
• App Engine services build on Google’s strengths
• Scale always matters
– Request volume
– Data volume
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22. Datastore Storage Model
• Basic unit of storage is an Entity consisting of
– Kind (table)
– Key (pk)
– Entity Group (top level ancestor)
• Has locking implications
– 0..N typed Properties (columns)
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23. Interesting Datastore Modeling Features
• Ancestor
• Multi-value properties
• Variable properties
• Heterogenous property types
Kind Person Kind Person
Entity Group /Person:Ethel
Entity Group /Person:Ethel
Key /Person:Ethel
Age Int64: 30
Key /Person:Ethel/Person:Jane
Hobbies String: Tennis
Age Double: 3.5
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Pets Key:/Turtle:Sam Key:/Dog:Ernie

24. Datastore Transactions
• Transactions apply to a single Entity Group
– Global transactions are feasible
• get(), put(), delete() are transactional
• Queries are not transactional (yet)
/Person:Ethel
Transaction /Person:Ethel/Person:Jane
/Person:Max
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25. Datastore Queries
• Every query must be supported by an index
– Built-in or user-defined
• Filters
– Equality, inequality, intersection, ancestor
– Union, IN not supported (yet)
– Joins not supported (unlikely, but never say never)
• Sorting
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26. Standards-based Persistence
• JDO or JPA (your choice)
– Established apis and existing tooling
– Easier porting
– Mappable (mostly) to the datastore
– Soft schemas
• DataNucleus App Engine plugin
• Why not a JDBC driver instead?
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27. Transparent Entity Group Management
• Entity Group layout is important
– Write throughput
– Atomicity of updates
• Ownership implies co-location within Entity Group
Kind
Pet
Entity Group
/Person:Ethel
Key
/Person:Ethel/Pet:Sam
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28. Demo!
Building Apps with Java App Engine

29. Coming Soon
• Task queues
• Full text search
• Incoming email
• XMPP
• Large file storage and retrieval
• Datastore export tools
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30. Q&A