New-age Transactional Systems - Not Your Grandpa's OLTP
John Hugg discusses high volume transaction processing applications with high and low frequency profiles, and how VoltDB can be used for that purpose.
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GigaSpaces 5.2: Adds support for Spring, .NET, local-views
Posted by Floyd Marinescu on Dec 28, 2006
On local views and SQL-based continuous queries:
Client applications, such as order books in financial firms and Microsoft Excel spreadsheets, often display only part of the data that is held in a data-grid. For performance reasons it would be much more efficient if the relevant part of the data would be kept locally near the client. A local-view is a local space that holds this information and is continuously updated to represent the current state of that view. To determine the specific part of the cluster that will be retrieved by a specific client we use SQL Query. The SQL Query is continuously matched against updates made on the data grid and is used to filter only the updates relevant to the local-view, improving bandwidth utilization.GigaSpaces now provides Java and .NET interoperability solution for typical .NET rich client applications talking to Java backends as well as a data distribution framework for pure .NET environments. Nati mentioned that GigaSpaces is being used in pure .NET companies now. A pure .NET client will be available in Q1 2007 and currently their customers are using a .NET clients built as thin wrappers over their Java API's using JNI. Nati explained how the .NET interoperability works:
The key of our approach to .Net support is a PONO (“Plain Old .Net Object”) abstraction, the .Net equivalent of POJOs in Java. It enables users to define, store and retrieve their C# classes, while using attributes/annotations to set indexes, replication modes, primary keys, etc. We expose the .Net abstraction with a native interface to our space-based API...To enable this level of interoperability, we introduced a serialization model referred to as PBS (Portable Binary Serialization). Among other things, it provides significant performance improvements. As a result, in a recent benchmark we conducted for a leading financial firm, our .Net and Java performance were almost identical.GigaSpaces's has extended Spring's programming model to enable accessing data and expose/access services in the grids via custom implementations of JMSTemplate, JDBCTemplate, a JavaSpaces/GigaSpaces Template. "We also implement the declarative cache interface, enabling implicit updates of POJOs in the IMDG using aspects."
Asked about how companies typically use GigaSpaces, Nati explained two scenarios:
In Memory Data Grid (distributed caching) - Customers uses our In-Memory-Data-Grid (IMDG) with a partitioned and replicated topology to remove the data bottleneck. The bottleneck is removed by bringing the data into memory, and if possible, into the application address space. With this approach, we’ve achieved linear scaling of up to 2 terabytes (and have not tested further than that yet). We’re seeing a very strong trend, in which customers are accepting our approach and moving all data into memory, so the capacity limits are continuously stretched (successfully). As an example, we are currently being evaluated for storing 5 terabytes of data in memory. In a majority of cases, customers use the IMDG in conjunction with existing databases, and use our new Mirror Service extensively to enable asynchronous, yet reliable, integration with external databases.Nati has been evangelizing space-based architectures quite a bit, it's main differentiating attribtutes being:
Space-Based Architecture - In this scenario, customers use our IMDG, MessagingGrid, and Processing Grid -- as well as our SLA-driven container -- to scale out their high-performance, stateful applications. Increasingly, we’re seeing customers move towards this scenario, even if they were initially only looking into pure caching. They often realize that what they actually need is an end-to-end solution for scaling out their entire application.
- Data maintained in reliable memory [and often sychronized to the DB asychronously]
- Condense messaging and data layers into ONE
- Business logic becomes loosely-coupled services that interact and share data through the Space
- Co-locate data, messaging, business logic
Data Grid
by
Cameron Purdy
Posted
And then there were three...
by
Billy Newport
Posted
One grid to bind them all
by
Jesse Kuhnert
Posted
Re: One grid to bind them all
by
Geva Perry
Posted
Re: One grid to bind them all
by
Geva Perry
Posted
Re: One grid to bind them all
by
Cameron Purdy
Posted
Distributed architectures
by
Cyril Gambis
Posted
DataGrid vs GigaSpaces Space Based Architecture
by
Nati Shalom
Posted
Implementation vs Architecture
by
Cameron Purdy
Posted
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Congratulations on the release. These are certainly capabilities that any vendor would be expected to have today, and it's great to see our message being validated by other vendors.
* Data maintained in reliable memory [and often sychronized to the DB asychronously]
* Condense messaging and data layers into ONE
* Business logic becomes loosely-coupled services that interact and share data through the Space
* Co-locate data, messaging, business logic
That is pretty close to the definition of a data grid. The implementation certainly is different (with Gigaspaces choosing to mount theirs on top of JINI and Javaspaces), but it's apparent that the goals are the same.
Peace,
Cameron Purdy
Tangosol Coherence: Data Grid -
Yep
Looks like we're the three vendors here. WebSphere XD ObjectGrid does the same thing more or less. The upcoming release (V6.1) has many of these features and some that are not mentioned here with the exception that like Coherence, it's not JINI based. -
Of course the article fails to point out that gigaspaces has probably had / bean working on their grid for longer than anybody.
I've even used in some products about 5 or so years ago. Good stuff. -
Thanks, Jesse. In addition, the posts above may be misleading so I would clarify by saying that GigaSpaces goes well beyond being a simple Data Grid (although our product is an excellent implementation of one). It is a comprehensive solution for building high-performance, scalable applications (transactional or not, data-intensive and/or compute-intensive). While Data Grid (or distributed caching) products try to solve performance and scalability issues only by addressing the data bottleneck, Space-Based Architecture, as it is implemented with GigaSpaces, goes for a holistic approach:
-- A SINGLE platform with a common cluster model and architecture for the entire application stack: data, messaging, processing coordination, etc., and co-location of these tiers on a single machine.
-- Scaling-out across multiple machines (in some cases, in the thousands)
-- Dynamic Management: The capability to define your C++, C# or Java business logic as plain objects and dynamically control their behavior in terms of deployment (including re-location), scaling policies, fault-detection and recovery, and dependencies on other distributed services. This is done through configuration, not programmatically.
We achieve all this by relying on the "tuple space" model, and specifically Jini/JavaSpaces.
Our customers understand that this approach is a fundamentally better one than trying to cobble together a solution based on multiple products, such as caching with JEE application servers. That's why we get quotes such as this:
"We now see GigaSpaces' Space-Based Architecture as a key element in our ongoing strategy", said Julian Browne, Virgin Mobile's Head of Architecture, "The decision to move away from a traditional application server approach to the far more elegant and maintainable Spaces model was an easy one", Browne explained... link to full article
To learn more about our approach, I recommend this white paper, and our company blog.
Happy New Year,
Geva Perry
GigaSpaces
gevaperry.typepad.com -
Clearly, distributed architectures are the future of application development. It's a very good point that when there will be a massive interest on these kind of product (which will happen for sure), there will be reliable and elegant solutions.
Congratulations and keep the good work, everybody! -
I noticed that the links in my post above don't work for some reason, so here they are again:
Virgin Mobile story: www.gigaspaces.com/News/news2006_12_18.htm
Space-Based Architecture white paper: www.gigaspaces.com/os_papers.html#a1
GigaSpaces blog: www.gigaspacesblog.com
GigaSpaces web site: www.gigaspaces.com.
Thanks,
Geva -
-- A SINGLE platform with a common cluster model and architecture for the entire application stack: data, messaging, processing coordination, etc., and co-location of these tiers on a single machine.
-- Scaling-out across multiple machines (in some cases, in the thousands)
-- Dynamic Management: The capability to define your C++, C# or Java business logic as plain objects and dynamically control their behavior in terms of deployment (including re-location), scaling policies, fault-detection and recovery, and dependencies on other distributed services. This is done through configuration, not programmatically.
Yes, that is a data grid.
Peace,
Cameron Purdy
Tangosol Coherence: Data Grid -
For those that are confused with the definition of DataGrid i've posted a new blog entry that outlines the difference between DataGrid and Space Based Architecture. It include real-life example and table the summarizes the differences between the two.
I welcome any thoughts comments on that topic.
HTH
Nati S
Write Once Scale Anywhere
www.gigaspacesblog.com -
For those that are confused with the definition of DataGrid i've posted a new blog entry that outlines the difference between DataGrid and Space Based Architecture. It include real-life example and table the summarizes the differences between the two.
An architecture is just that -- an architecture.
A grid, on the other hand, is an actual managed, provisionable, deployment platform. Our customers (which now number in the _thousands_ of real life examples ;-) deploy many different types of architectures, including "space based architectures", onto their Coherence data grids.
I would posit that your understanding of data grids is no different from your understanding of distributed caching. In the case of Tangosol Coherence, we provide an extremely successful implementation of both, and I'd be happy to illustrate how they work -- and especially how they work extremely well together.
Peace,
Cameron Purdy
Tangosol Coherence: The Java Data Grid
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