Uri Cohen

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So far Uri Cohen has created 24 blog entries.

XAP 9.0 – Geared for Real-Time BigData Stream Processing

By |2020-10-05T10:16:59+00:00May 10, 2012|

XAP 9.0 is out the doorway, and I thought it would be a good opportunity to share some of the things we’ve been up to lately. Traditionally, one of XAP’s primary use cases was large scale event processing, more recently referred to as big data...

XAP 8.0.1 is Out!

By |2020-10-06T09:34:41+00:00April 7, 2011|

We’ve just released XAP 8.0.1, with a lot of goodies included. 8.0.1 is the first feature and service pack on top of XAP 8.0.0. It includes many enhancements and a few exciting new features. Here’s a short recap: Improved Web UI Dashboard with...

Data Grid Querying, Revisited

By |2020-10-06T09:41:18+00:00March 8, 2011|

There has been a great deal of talk lately about the new EHCache cache querying capabilities and the advantages of real-time analytics through in-memory cache querying. I find that rather odd since extensive querying and processing capabilities have b...

Data Grid Querying, Revisited

By |2020-10-06T09:43:01+00:00March 8, 2011|

There has been a great deal of talk lately about the new EHCache cache querying capabilities and the advantages of real-time analytics through in-memory cache querying. I find that rather odd since extensive querying and processing capabilities have been around for years with in memory data grids like GigaSpaces XAP, Oracle Coherence, Gemstone GemFire and more recently Hazelcast and GridGain. So I don’t really understand the big fuss around EHCache finally supporting it….

But that’s actually a great opportunity to revisit some of the work we’ve done in our recent 8.0 release in the context of querying. There are two main features we’ve introduced in 8.0 that take data grid querying to the next level.

Complex Object Querying

The first one is complex object graph querying. Simply put, you can now index and any type of property, at any nesting level, of your data grid objects: primitive types, user defined types, or collections/arrays of either.
Think of an Author object, which has a collection of books, each of them containing a collection of user reviews.
Here are just two examples of what you can achieve with XAP 8.0:
• List all the Authors that wrote at list one sci-fi book:




Author[] authors = gigaSpace.readMultiple(new SQLQuery<author>(Author.class,
"books[*].genre = ‘Sci-Fi’ ORDER BY name",Integer.MAX_VALUE);


• List all the Authors who have at least one book on which your friend on Facebook commented:




Author[] authors = gigaSpace.readMultiple(new SQLQuery<author>(Author.class,
"books[*].comments[*].username= ?", “myFacebookFriend”, Integer.MAX_VALUE);


This goes hand in hand with our new Document support, so you can apply the above to schema free documents as well as just plain Java or .Net objects.


GigaSpaces JPA


The second feature is our all new JPA support. We believe this to be a major step towards ease of use and standardization of distributed data store access.


There have been a lot of discussions in the developer community about how the “classic” querying and interaction models (such as SQL/JDBC and JPA) can be mapped to the world of distributed data stores (which in memory data grids are a part of).


When it comes down to querying, each distributed data store defines its own querying language and semantics. Some are completely proprietary (e.g. EHCache’s new query DSL, or MongoDB’s query syntax), some are modeled after standard SQL to some extent or the other (e.g. GigaSpaces’ native querying support, Coherence’s recent QL, or GridGain’s query language).
All of these seem be solid query implementations (naturally you can guess which one I like best :) ).


But when developing to each of those products, you need to learn their interaction model, data access semantics (such as transaction management) and of course querying language.


The first take at bridging the two worlds, and IMO pretty successful one considering the inherent difficulties in bridging the two models (see this presentation I’m giving at QCon London in two days), was actually done by Google almighty with the JPA support in AppEngine.
Although not 100% standard (actually not even close), it gives you as a developer a very easy and familiar way to implement scalable data access on top of their BigTable data store, and a very clear migration path for putting your apps on AppEngine, should you choose to do so. It also makes your code a lot more portable since JPA, even a partial version of it, is still way more standard and portable than any proprietary API.


GigaSpaces JPA – Design Principles


When we initially thought of implementing a JPA interface to our data grid, we had the following goals in mind:



  • Make our user’s life easier by providing a standard way for the to interact with the data grid that will allow them to easily get up to speed with new applications, and port existing JEE applications more smoothly and quickly.
    For that, we created a thin layer on top of the excellent OpenJPA framework so that configuring a GigaSpaces EntityManagerFactory is a breeze if you’re already into JPA in general and OpenJPA in particular. We also managed to support a nice set of features from the JPA specification (actually more extensive than Google’s AppEngine JPA) so that most of the stuff users actually do with JPA is covered. Naturally, this is just our first take at this huge project, and we’ll add more and more capabilities as we move forward.
  • Enable our users to leverage the power and scalability of the data grid to scale their data access, even when they’re using JPA (which was originally designed for a centralized data model). This means exposing content based routing and data grid indexing to their JPA domain model, and abstracting away cluster wide transactions and queries.
  • Protect our users from making wrong implementation decisions when modeling and accessing their distributed data. In Google AppEngine’s terminology, this means supporting Owned Relationship only such that object graphs are not scattered across many nodes and are always limited to a single node.
  • Allow our users to use our powerful native APIs for functions that are not covered by the JPA specification, e.g. event handling, distributed task execution, and more. So it actually means that you can use the JPA API, while still operate on the same data model and entities via our native POJO based API. This is a very powerful concept that covers not only JPA but all of our data access APIs, we call Same Data, Any API and it means that you can operate on the same data using a variety of APIs – besides our native POJO API and JPA. For example, you can read a JPA entity as a document (in case you want to treat it in a more loosely flexible manner, or use our C++ client side API from native clients to read this data.

See for Yourself


We have just published an comprehensive tutorial tha t explains the principles of the our JPA implementation using the good old Spring PetClinic sample app (we’ve added our own flavor to it :) ). It explains the data model considerations and shows how take advantage of data grid features such as space based remoting to optimize the data access of the application and use our


TV Made Right :)

By |2020-10-06T10:35:21+00:00December 7, 2010|

We've just created a new channel on YouTube called GigaSpaces TV. It will feature interviews and tech talks on various aspects of the product, and you also get to see me unshaved in the first episode ;). In it I give a short overview of the new APIs we...

To Scale or Not to Scale – the Recording

By |2020-10-06T10:36:09+00:00December 7, 2010|

The recording of my session at Devoxx 2010 is now available online at parleys.comAt this point you need a subscription to view it in full (which I highly recommend anyway since there's a lot of valuable content there), but it will be made fully public ...

Yes, Sql!

By |2020-10-06T11:03:38+00:00November 5, 2010|

See below the slide deck from my session today at QCon SF titled "Yes, Sql!" (hopefully you liked it if you attended :) ). It focuses on how the classic querying models like plain SQL and JPA map to distributed data stores. It first reviews the current...

Our Citrix Integration Demo

By |2020-10-06T11:14:12+00:00October 19, 2010|

This week we’re attending the Interop conference in New York to present our integrated solution with Citrix Netscaler and XenServer. The solution enables GigaSpaces XAP applications to utilize the Netscaler load balancer AND XenServer infrastructure ...

Join Us for the New XAP Security Framework Webinar

By |2020-10-06T12:08:09+00:00October 27, 2009|

You’re welcome to join our XAP security webinar on Wednesday, October 28. In this webinar I’ll will explain the motivation behind our revamped security framework and overview its capabilities. The session will also include a live demo in which Iâ...

GigaSpaces XAP 7.0.1 Is Out!

By |2020-10-07T06:56:10+00:00September 15, 2009|

I’m happy to announce that today we’ve released our latest version, XAP 7.0.1. I would like to share with you a few details about it. Version 7.0.1 is a service pack release on top of version 7.0.0 and as such is backwards compatible with it. In ad...

Join Us for the 7.0 Overview Webinar

By |2020-10-07T07:21:36+00:00July 25, 2009|

You’re all welcome to join our 7.0 introduction webinar next Tuesday (July 28). In this technical update and live demo, I will go over the new and noteworthy in the 7.0 release. Joining me in this webinar will be Shay Banon, our System Architect (w...

UI & Manageability Improvements in XAP 7.0

By |2020-10-07T08:55:03+00:00May 10, 2009|

As GigaSpaces XAP 7.0 release is getting closer, it's time to overview some of the important new features and enhancements of this version. In this post I will focus on the manageability aspects of this release. I'll review the general concepts behind ...