Here is some specific details on the potential technology synergies ....
Synergy with SpringSource
What makes the integration very synergistic and complementary are the areas of focus for the two companies : GemStone/GemFire has primarily focused on getting the infrastructure for clustering and managing data in the middle tier to be extremely reliable and offer tremendous flexibility; SpringSource focus has been to offer a ubiquitous programming model (through the Spring framework) that is simple to adopt and quite open where a myriad of products and technologies can be seamlessly integrated and a light weight runtime environment through the enterprise class TC server and a management environment enabled by HypericHQ. In a sense, the GemFire integration brings first class data management and clustering to the run time - a key component enabling "extreme scale" application deployment.
GemStone, as well as the merged entity is totally committed to support heterogeneous access to the GemFire data fabric with continued support for Java, C++ and .NET. In fact, we are exploring opportunities to integrate with Spring.NET and continue on our commitments to simplify the programming models for non-Java environments.
GemStone management and the SpringSource/VMWare management remains committed to make sure we deliver on the roadmap and future extensions to the platform that have already been discussed with customers. In fact, we will go well beyond our current commitments by leveraging the Spring framework and integration with a multitude of Spring modules to provide a much simpler configuration and development model for our customers.
How might we leverage the Spring framework?
The spring framework is all about choice for enterprise Java applications. We now enable more choice by offering a clustered data management solution that can be used in multiple ways:
1) As a transparent L2 Cache: Spring application using Hibernate will be able to plugin a L2 Cache that can intercept traffic to/from a backend database for scaling and performance reasons. Note that GemFire already supports Hibernate based L2 Cache plugins but now this effort will take increased focus. On the .NET side of things, we will accelerate support for Spring.NET applications using nHibernate.
2) AOP cache: We will be able to offer sophisticated AOP caching interceptors that can transparently use a highly scalable cache.
3) Parallel Data aware method invocations: Spring bean service invocations could transparently make use of the GemFire data-aware parallel function execution capabilities where behavior execution can happen in parallel, operate on localized data sets and go through a "aggregation" phase to produce a final result.
4) Session state management: We already provide an abstraction layer for session state management on top of GemFire today. The intent would be to include this capability as an integral part of the platform. The highlights of this add-on include the ability to handle very large object graphs efficiently, dynamic partitioning and load balancing, visibility of session state across multiple clusters (WAN), HA and the ability to maintain a "edge" cache that maximizes memory utilization through a heap LRU based eviction algorithm.
We are exploring options to enable quick integration of the data fabric into existing Spring applications through enhancements to the Eclipse plugins - for instance, the configuration of the cache regions and deployment topologies will be simplified. We will also investigate how we can leverage Spring Roo (next generation rapid application development tool) where a developer will be able to build a Java application with integrated caching in minutes.
Integration with Spring Modules
Unlike some of the other distributed caching options available in the market, GemFire natively supports memory based, reliable event notifications. Applications can subscribe to data in the fabric through "continuous queries" and can receive in-order, reliable notifications when the data of interest changes. Spring Integration extends the Spring framework into the messaging domain and provides a higher level of abstraction so business components are further isolated from the infrastructure and developers are relieved of complex integration responsibilities. We are exploring techniques to leverage this simple and intuitive API so applications are abstracted away from dealing with GemFire specific notification APIs.
Spring Batch enables extremely high-volume and high performance batch jobs though optimization and partitioning techniques. Simple as well as complex, high-volume batch jobs can leverage the spring framework in a highly scalable manner to process significant volumes of information. Integration with the GemFire function service will now mean developers can develop Spring batch applications but behind the scenes leverage the parallelism and "data aware" routing capabilities built into GemFire.
There are many integration possibilities all of which will make the job of the developer integrating with a data fabric/grid significantly simpler.
GemFire in the cloud
Our offering will expand the VMWare strategy to deliver 'Platform as a Service' solutions over time. The already powerful arsenal includes VMWare VSphere as the cloud operating system, the Spring framework as the enterprise application development model and SpringSource TCServer as a lightweight application platform. Now, GemStone extends the capabilities with GemStone's GemFire as the scalable, elastic data platform.
While a good case is being offered by VMWare on the current VMWare products positioned for the cloud, I will focus on the rationale for GemFire as a data platform for the cloud.
One of the big advantages with applications deployed in a cloud environment is to tap into additional capacity 'just in time' based on demand changes. The underlying platform needs to detect and respond by exhibiting elastic characteristics. The additional capacity could be provisioned through virtual or physical machines spread across subnets or perhaps even across data centers. This means application behavior as well the associate state (data) needs to horizontal scale through migration. The behavior migration is well taken care of by modern day clustered application servers, but it is lot more challenging with data. The traditional relational database is rooted in a design where the focus is optimizing disk IO and maintaining ACID properties. The frame of reference was quite different - data was centralized and extensive locking/latching techniques used to preserve the ACID transaction properties. The design did not account for data to be managed across a large cluster of heterogeneous machines. Several clustered databases today offer a shared nothing architecture at the database engine layer but fall short at the storage layer (where it is shared everything). For elasticity, we believe the design has to change along the following two important dimensions:
1) distribution orientation - efficient distribution methods to move data around a large network efficiently without loss of consistency
2) memory orientation - when demand spikes it is lot faster and easier to move data between memory segments across machines. GemFire manages data by horizontally partitioning the data across any number of machines, and initiates automatic data rebalancing when additional capacity is added to its cluster. Unlike common database architectures, GemFire primarily manages data in memory and leaves the persistence to disk as a choice for the developer. Besides being more efficient to simply manage ephemeral state such as session state only in memory, regulations in some environments may mandate that nothing be stored on disk.
We also know that applications deployed on the cloud will be subject to higher levels of SLAs - continuous availability and very predictable response times. Data stored in GemFire is typically always synchronously copied to one or more machines often also redundantly stored on disk and even asynchronously copied across data centers in case the entire data center goes down. Again, by allowing the data to be primarily managed in memory, the cost of maintaining data redundantly is considerably reduced. GemFire's built in mechanism to carry out continuous instrumentation and potential integration with cloud provisioning environments (vCloud API) permits apriori detection of changing load patterns to initiate data rebalancing without any operator intervention.
Highly parallelizable, computationally intensive applications such as large scale web applications, or analytic applications (such as risk analysis in finance) are ideal candidates for cloud deployments. A lot of these computational intensive tasks tend to be data intensive also. Spring enabled services can now be parallelized through the use of GemFire's data-aware parallel function service where the underlying application behavior can now be parallelized and executed on the nodes carrying the data sets required by each parallel activity. This data localization will dramatically increase the throughput and reduce costs with little out-of-process data access. Instead of developing applications using the custom GemFire APIs, application development could rely on the open spring community driven modules such as Spring Batch simplifying the development task.
We also believe the combination of SpringSource/VMWare infrastructure and GemFire will enable customers to implement "cloud bursting" strategies where on-premises stateful services can much more easily expand to a in-house private or even a public cloud.
Vision of a first class middle tier data management platform
GemFire started off as a pure main memory distributed cache where transactional updates were immediately persisted to the database of record. In recent years, with the addition of capabilities such as reliable asynchronous writes to the database, data partitioning with dynamic rebalancing and wide area network data management the product now is often used as the high performance store with data written to the database either in batches, at the end of the business day or a batch run. The database is being relegated as the archival store for integration purposes.
We are now adding support for parallel shared nothing disk storage layer in the product. Unlike other cluster database management system designs where the disks are shared across many distributed processes, each member of the cluster can persist in parallel to their local disks. Transactions do not result in disk flushes but rather allow the IO scheduler to time the block writes to disk dramatically increased disk write throughput. Any data loss risks due to sudden failures at a hardware level are mitigated by having multiple nodes write in parallel to disk. In fact, it is assumed that hardware will fail, especially in large clusters and data centers and software needs to take into account such failures. The system is designed to recover in parallel from disk and to guarantee data consistency when data copies on disk don't agree with each other.
It is our strong belief that with the proliferation of highly distributed middle tier architectures, a data tier that is colocated with the application tier, is primarily clustered and is used to manage ephemeral data will substantially change the architectures for all enterprise applications that demand scaling and predictability.
It is with this belief that we built the new SQLFabric product - same underpinnings as GemFire but uses SQL as the interface. We took this step in spite of the recent push by the "no sql" database alternatives for the cloud. Our decades of database implementation and research experience does not implicate SQL as a query language when it comes to the desired characteristics of elasticity and performance. This is simply a matter of design choices made by SQL database vendors based on a frame of reference that is no longer valid. In other words, there is nothing wrong with SQL as a language but rather it is the design of disk oriented, centralized databases that is sub-optimal. The premise behind SQLFabric is to capitalize on the power of SQL as an expressive, flexible, very well understood query language, but alter the design underpinnings in common databases for scalability and high performance. By providing ubiquitous interfaces like JDBC and ADO.NET the product becomes significantly easier to adopt and integrates well into existing eco-systems.
Technical rants on distributed computing, high performance data management, etc. You are warned! A lot will be shameless promotion for VMWare products