Session presented at the 2nd IndicThreads.com Conference on Cloud Computing held in Pune, India on 3-4 June 2011. http://CloudComputing.IndicThreads.com Abstract: There are many persistence frameworks on Google App Engine. Google has its low-level datastore API, then there are standards based frameworks like JPA and there are more frameworks like Objectify-Appengine specifically for Google App Engine. Managing persistence is still a challenge. With so many options, we face the dilemma of choosing right framework for the application we are building. Objectify-Appengine extends the convenience of NoSql by providing a Hibernate-style mapping layer between your application and GAE datastore. In this session I will cover handling persistence on Google App Engine with Objectify-Appengine. At one hand Google datastore API is low level and at the other there are standard based frameworks like JPA/JDO. Objectify-Appengine tries to hit the sweet spot between the two. Takeaways for the Audience Audience will see the capabilities of Objectify-Appengine and how can they leverage it to manage persistence on Google App Engine. The best practises to be followed when using Objectify, and demo of an application built on Objectify-Appengine. Speaker:Meetu Maltiar is a Senior Consultant at Inphina. In his six year experience he has worked as Software developer, Knowledge Manager and as a Mentor. He is passionate about technology and has experience in product development as well as services. He has experience on Google App Engine and has migrated existing applications to cloud and he has also worked on Green-field projects. He likes to invest his time in exploring upcoming languages like Scala and technology frameworks like GridGain, Esper and Hadoop and extensively blogs about them.

1. Easy ORM-ness with Objectify-Appengine
Meetu Maltiar
Inphina Technologies
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2. Overall Presentation Goal
Google Datastore Basics
Options available for managing persistence
Objectify-Appengine
Demo of an application using Objectify
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3. Enough About Me
Senior Software Engineer at Inphina
Technologies that interest me:
Cloud Computing
Scala
Hadoop
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4. Datastore Basics
Entities
Operations
Keys
Transactions
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5. Entities
An Entity is an object’s worth of data in the
datastore
In datastore an Entity is like HashMap-like object
of type Entity
Datastore is conceptually a HashMap of keys to
entities, and an Entity is conceptually a
HashMap of name/value pairs
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6. Operations
Get() an entity as a whole from datastore
Put() an entity as whole in datastore
Delete() an entity from datastore
Query() for entities matching criteria you define
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7. Keys
In the datastore, entities are identified by the id
(or name) and a kind, which corresponds to the
type of Object you are storing.
So, to get Employee #111 from datastore, we
need to call something like get_from_datastore
(“Employee”, 111)
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8. Keys Continued
There is actually a third parameter required to
identify an entity and it is called parent
Parent places the child in the same entity group
as the parent
Parent (which can be null for the un-parented,
root entity) is also required to uniquely
identify an Entity.
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9. Keys Continued
So, to get Employee #111 from datastore we
need to call something equivalent to:
get_from_datastore (“Employee”, 111, null)
or,
get_from_datastore (“Employee”, 111, the_parent).
Instead of passing three parameters datastore
wraps it in a single Object called Key.
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10. Transactions
Data gets stored in gigantic form of thousands of
machines
In order to perform an atomic transaction
datastoreTo give us more that over where our data is same
requires control entities lie on
servers. stored, the datastore has a concept of an entity
group
To give us more control over where our data is
stored, the datastore has a concept of an entity
group
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11. Transactions Continued
Within a Transaction we can access data from a
single entity group
Choose entity groups carefully
Why not have everything in a single entity
group?
Google restricts number of requests per second
per entity group
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12. Executing Transactions
When we execute get(), put(), delete() or query()
in transaction
We must operate it on single entity group
All operations will either fail or succeed
completely
If another process modifies the data before
commit datastore operation will fail
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13. Tools
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14. Persistence Options
JPA/JDO
Google Datastore
Persistence Frameworks on GAE
Objectify-Appengine
Twig
Simple Datastore
Slim3
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15. Google Datastore Challenges
Supports just four operations
It persists GAE-Specific entity classes rather
than POJO’s
Datastore Keys are untyped
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16. JPA/JDO Challenges
Extra Cruft
Fetch Groups
Detaching
Owned/Unowned relationships
Leaky Abstraction
Keys
Entity Groups
Indexes
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17. Developers Dilemma
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18. Objectify
It lets you persist, retrieve, delete and
query typed objects
All native datastore features are supported
It provides type safe key and query classes
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19. Objectify Design Considerations
Minimally impacts cold start time. It is light
weight
No external dependencies apart from GAE
SDK
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20. Working With Datastore
Entity ent = new Entity(“car”);
ent.setProperty(“color”, “red”);
ent.setProperty(“doors”, 2);
service.put(ent);
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21. Objectify ORMNess Objects!
Employee emp = new Employee();
emp.setFirstName(“John”);
emp.setLastName(“Smith”);
service.put(emp);
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22. An Objectify Entity
public class Employee {
@Id Long id;
private String firstName;
private String lastName;
}
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23. get() Operation
Objectify ofy = Objectifyservice.begin();
Employee emp = ofy.get(Employee.class, 123);
Map<Long, Employee> employees =
ofy.get(Employee.class, 123, 124, 125);
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24. put() Operation
Objectify ofy = Objectifyservice.begin();
Employee emp = new Employee(“John”, “adams”);
ofy.put(emp);
System.out.println(“Id Generated is ” + emp.getId());
List<Employee> employees = createEmployees();
ofy.put(employees);
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25. delete() Operation
Objectify ofy = Objectifyservice.begin();
ofy.delete(Employee.class, 123);
Employee emp = // get Employee from some where
ofy.delete(emp);
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26. query() Operation
Objectify ofy = Objectifyservice.begin();
List<Employee> employees =
ofy.query(Employee.class).filter(“firstName =”, “John”)
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27. Demo
get()
put()
delete()
query()
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28. Objectify Best Practices
Use a DAO to register entities
Automatic Scanning
not advised adds to initialization time
will require dependency jars apart from GAE
will require changes in web.xml
GAE spins down the instance when not in use. When it
comes up the request is slow because of added
initialization time. This is called cold start.
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29. Objectify Best Practices …
Use Batch Gets Instead of Queries
Use Indexes sparingly
By default every field of object will be indexed. It comes with
heavy computational price.
Use @Unindexed on entity level and @Indexed at fields required
for query
Avoid @Parent
In JPA “owned” entity relationship provides referential integrity
checking and cascading deletes and saves. Not so here.
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30. Happy Developer
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31. Conclusion
JDO/JPA learning curve is steep due to App Engine’s non-
relational nature and unique concepts such as entity groups,
owned and un-owned relationships.
Google Datastore is low level. It makes no pretense of being
relational but also does not allow working with objects. It just
stores the objects of type
com.google.appengine.api.datastore.Entity
Objectify is light weight and it preserves the simplicity and
transparency of low level API and does all work converting to
and from POJOS to Entity Objects.
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32. mmaltiar@inphina.com
www.inphina.com
http://thoughts.inphina.com
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33. References
Objectify-Appengine
http://code.google.com/p/objectify-appengine/
Google IO 2011 Session on highly productive gwt
rapid development with app-engine objectify-requestfactory
and gwt-platform
Twitter mining with Objectify-Appengine
http://www.ibm.com/developerworks/java/library/j-javadev2-14
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