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Tag Archives: patterns

How We Improved APM’s Monitoring Capacity by 500% with Gemfire, RabbitMQ, vPostgres, and a Few Scalability Design Patterns

Earlier this week, we announced the general availability of a major upgrade to vFabric Application Performance Manager (APM). This release started one year ago, after we released the first version of the product to market. When we started work on this release, we knew we would need to invest heavily in scalability. APM is designed to help simplify monitoring and management for highly dynamic, large web applications living in the cloud. To succeed, we needed to make sure our product could scale gracefully with our customers. So, we set out with a challenging goal to increase the capacity of APM by a factor of 5.

Transforming a complex product such as APM into a more scalable architecture is not an easy task, let alone doing so in a single release. For this reason we’ve started by modifying the architecture in steps, starting with local improvements inside our virtual appliance, (available in the APM 5.0 release) and moving towards a horizontal scale solution in future releases. Continue reading

3 Key Stages to Evolve from Legacy Databases to a Modern Cloud Data Grid

How do you plan a roadmap for moving from a legacy data architecture to a cloud-enabled data grid? In this article, we will offer a pragmatic, three-stage approach. At SpringOne-2012, the “Effective design patterns with NewSQL” session (see presentation embedded below) generated a lot of interest. (Thank you to everyone who joined us!) Jags Ramnarayan and I discussed problems with legacy RDBMS systems, NewSQL driving principles, SQLFire architecture, application design patterns as well as data consistency and reliability.

We went deep into vFabric SQLFire which is a pragmatic solution that addresses these data challenges:

  • How do I architect my data tier for very high concurrent workloads?
  • How do I achieve predictability both for data access response time and availability?
  • How do I distribute data efficiently and real time to multiple data centers (and to external clouds)? 
  • How do I process these large quantities of data in an efficient manner to allow for better real-time decision-making? 

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NanoTrader: Now The SpringTrader Reference Architecture

What is SpringTrader?

Back in August, we provided a sneak peak at NanoTrader, VMware’s vFabric Reference Architecture.  It was referenced in several posts and then featured in a session at SpringOne recently.  The application has now been named SpringTrader, and we wanted to a) share more information about the SpringTrader app including some updated architecture graphics, b) provide a new tool (a version you can log into online), and c) share the location of the Spring Trader bits for download.

If you haven’t heard, the SpringTrader reference architecture is used to help Java-based application architects, developers, infrastructure, and operations teams advance their application roadmaps and provide reusable patterns.  Some might also consider how vFabric Application Director can be used with the SpringTrader app to enable continuous deployment or automatically provision and scale the app in a completely virtual data center (i.e. a software defined data center). As well, vFabric Application Performance Manager can be used to monitor the entire stack and trigger automated scaling events like adding a new JVM and tc Server to the SpringTrader app’s production environment.
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vFabric @ SpringOne Next Week

The vFabric team is headed to SpringOne 2GX 2012 next week – from October 15-18 in Washington, DC. This is set to be a great event to learn the latest on Spring with over 100 sessions covering a wide variety of topics. For those of you looking to learn more about how vFabric is the best place to run Spring applications, here are the highlights you won’t want to miss:

1. Sessions:  There are a number of speakers from SpringSource, CloudFoundry, and the VMware vFabric team on the schedule, including:

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VMware on How Big Data Meets Fast Data in the Cloud

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Big Data allows you to find opportunities you didn’t know you had.

Fast Data allows you to respond to opportunities before they’re gone.

The combination of Big Data and Fast Data working together may enable new business models you never could have achieved before.

To elaborate, the idea here is to analyze the historical “Big Data” and look for trends or patterns that have lead to good results in the past. Then you try to model those patterns in such a way that you can detect them as they are unfolding in real-time based on incoming Fast Data. If we could analyze large amounts historical as well as recent data quickly enough, we have an opportunity to influence the behaviors of the actors real-time, and have a better chance to steer them toward the patterns that produce results.

Some Examples of Big, Fast Data

For instance, look at location-based services.

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