Are you curious about where Java architecture might go in the next decade ? Do you need to skill yourself up for application development in Clouds ?
Having canvassed opinion, we've decided to put together a meeting for architects, project leaders and developers to discuss how to develop large scale, high speed, fully transactional applications in the Cloud. The evening will include a review of market trends, key players, products and architectures for cloud / grid commercial applications. And we'll also be announcing CloudTran, a new product to bring the Cloud into the mainstream as a platform for Java developers.
We're delighted that we'll be joined by Dan Stone (http://blog.scapps.co.uk/) who will give us a run down of the leading products in this area based on his forthcoming book on the subject. And Jim Liddle, UK Operations Director, GigaSpaces who will talk about the game-changing features of GigaSpaces XAP.
There'll be time on the day for an open session as we're keen to get a dialogue going, so please come armed with your questions, comments, views, war stories. The evening will be held on Thurs Feb 11th from 5pm at The Masons Arms, London W1S.
If you'd like to come along, please sign up at http://www.eventbrite.com/event/533997200
Monday 25 January 2010
Tuesday 5 January 2010
What does CloudTran add over GigaSpaces XAP ?
CloudTran's goal is to make it as easy as possible for Java developers to write mission-critical applications that are scalable and fast. CloudTran is layered on top of GigaSpaces XAP. As both products serve Java developers and provide transactions, we are often asked what CloudTran adds over GigaSpaces. Here's the bite-sized answer.
1 Coordinating data grid and storage services. GigaSpaces transactions coordinate operations in one space or between spaces, and it has a mirror service that does asynchronous write-behind to a database. However, for multi-database or multi-space scenarios it does not preserve atomicity or consistency between the spaces and the data storage services.
CloudTran provides rock-solid ACID transactionality that coordinates operations between the data grid and storage services, without limiting the speed by disk writes. This means that developers using rich domain models spread across different data stores can use transactions without worrying about whether the particular deployment configuration can handle it.
2 Object-Space-Relational Mapping. Java developers are used to working with an object view, where relations between objects are held as object references. But in a scalable, cloud-based system, objects are distributed across different machines and object references aren't possible. Instead, the relation must use an identifier - like a foreign key - that can be used to simulate an object reference by doing a lookup and fetch under the covers. This means that there need to be different types of objects: one used by Java developers in their app, having object references; and another stored in the space with foreign keys in place of the object references.
As if that wasn't enough, backing up Java space objects to a SQL database requires the usual object-relational mapping. The code has to load objects from the database into memory, as well as saving updates from memory to the persistent store.
In other words, there are three different views of information that need format definitions and mapping operations between them. CloudTran generates code to make sure this is all done correctly: JDBC is supported out of the box; other types of persistent stores can be mapped via plug-ins.
3 Automatic Configuration and Deployment. GigaSpaces XAP is an application server-level product. This means it has more features than just a caching product, but it needs to be configured and deployed. As Stefan Norberg says in his post, Why it sucks being an Oracle customer, the down side is the developer has to do "configuration, deployments and all of that". This requires a lot of investment in learning config and deployment concepts and increases cost and risk from getting it wrong.
CloudTran provides modelling and code generation to help developers get over this hump. Modelling is via an Eclipse plugin which uses terms that developers can readily understand - entities, data sources, services and queue receivers/topic subscribers. Then the code generation makes it easy to convert the model into a production system - just add business logic.
Developers can also model multiple deployments, tied to the application model. The default deployment is to the Eclipse UI, but Windows, Linux and Amazon EC2 are supported. We have found it especially useful to be able to model deployments for different purposes (such as Dev, Test, UAT and Live) strictly driven by the application model - it avoids the finger trouble of reworking the configuration by hand when the application changes.
1 Coordinating data grid and storage services. GigaSpaces transactions coordinate operations in one space or between spaces, and it has a mirror service that does asynchronous write-behind to a database. However, for multi-database or multi-space scenarios it does not preserve atomicity or consistency between the spaces and the data storage services.
CloudTran provides rock-solid ACID transactionality that coordinates operations between the data grid and storage services, without limiting the speed by disk writes. This means that developers using rich domain models spread across different data stores can use transactions without worrying about whether the particular deployment configuration can handle it.
2 Object-Space-Relational Mapping. Java developers are used to working with an object view, where relations between objects are held as object references. But in a scalable, cloud-based system, objects are distributed across different machines and object references aren't possible. Instead, the relation must use an identifier - like a foreign key - that can be used to simulate an object reference by doing a lookup and fetch under the covers. This means that there need to be different types of objects: one used by Java developers in their app, having object references; and another stored in the space with foreign keys in place of the object references.
As if that wasn't enough, backing up Java space objects to a SQL database requires the usual object-relational mapping. The code has to load objects from the database into memory, as well as saving updates from memory to the persistent store.
In other words, there are three different views of information that need format definitions and mapping operations between them. CloudTran generates code to make sure this is all done correctly: JDBC is supported out of the box; other types of persistent stores can be mapped via plug-ins.
3 Automatic Configuration and Deployment. GigaSpaces XAP is an application server-level product. This means it has more features than just a caching product, but it needs to be configured and deployed. As Stefan Norberg says in his post, Why it sucks being an Oracle customer, the down side is the developer has to do "configuration, deployments and all of that". This requires a lot of investment in learning config and deployment concepts and increases cost and risk from getting it wrong.
CloudTran provides modelling and code generation to help developers get over this hump. Modelling is via an Eclipse plugin which uses terms that developers can readily understand - entities, data sources, services and queue receivers/topic subscribers. Then the code generation makes it easy to convert the model into a production system - just add business logic.
Developers can also model multiple deployments, tied to the application model. The default deployment is to the Eclipse UI, but Windows, Linux and Amazon EC2 are supported. We have found it especially useful to be able to model deployments for different purposes (such as Dev, Test, UAT and Live) strictly driven by the application model - it avoids the finger trouble of reworking the configuration by hand when the application changes.
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