GigaSpaces just launched an update to its XAP real-time analytics tool. This version extends the product's application performance monitoring capabilities with the goal of monitoring big data applications based upon many of the most common big data tools, such as such as Hbase, Cassandra, and MongoDB. The challenge, of course is to find ways to monitor these highly-distributed, database applications without imposing a great deal of overhead in the process.
Here's what GigaSpaces has to say about XAP9.0
GigaSpaces Technologies, a pioneer of data scalability and next-generation application platforms for mission-critical applications, announces the launch of XAP9.0. At the core of the latest release of the GigaSpaces platform is its ability to quickly launch a high-performance real-time analytics system for applications processing massive data sets, quickly and easily.
Major social networking platforms like Facebook and Twitter have developed their own architectures for handling the need for real-time analytics on huge amounts of data. However, not every company has the need or resources to build their own Twitter-like solution. This is where XAP 9.0 comes in. With the GigaSpaces real-time analytics solution for Big Data, there is no need to reinvent the wheel – GigaSpaces has taken the same Twitter/Facebook-like blueprint, and made it simple enough for developers to implement a Big Data analytics system in a matter of days, or even less. GigaSpaces XAP has a long record of providing elastic real-time processing, and in version 9.0 these capabilities have been honed to specifically meet today’s challenges to real-time analytics for Big Data.
GigaSpaces XAP 9.0 includes the exact feature set to meet these challenges:
- Real-time, scalable streaming data processing:
- Patent-pending parallel processing (FIFO Groups).
- Fine-grained data compression.
- Reduced memory footprint, with no compromise on query capabilities.
- Local view, which enables keeping local data continuously updated
- Ability of all the above to now work across multiple sites, dynamically, through XAP’s WAN Dynamic Topology API.
- Integration with Big Data back-end databases, such as Hbase, Cassandra, and MongoDB to ensure consistent flow of data without affecting performance.
- Big Data analytics on the cloud.
Built-in cloud-enablement features provide:
- Consistent automation of deployment, scaling, and failover of the entire Big Data application stack on any private or public cloud.
- Built-in support for managing Big Data services, such as Cassandra, MongoDB, Solr, and more.
- Support for bare metal environments for extreme I/O.
- Elastic scaling to reduce the total cost of ownership of running Big Data apps.
Big data applications that are hosted upon industry standard, X86-based systems typically require tens, hundreds, or perhaps thousands of processors to deal with the volume of data to be processed, the many different types of data that must be processed and the speed at which the data is presented to the system for processing. The systems that are involved with this processing are kept very busy with the task of acquiring and processing the data and have little extra capacity for detailed, granular monitoring that most application performance management products require.
GigaSpaces appears to have developed technology that can monitor these systems and offer real-time performance analytics for administrators without placing undue processing loads on the systems supporting big data applications. This clearly a difficult problem. Gathering data on processing, memory, networking and storage utilization without getting in the way of that processing.
GigaSpaces clearly is gathering only key performance data and sending it on to another system for processing and analysis. That other system, of course, could be local or out in the clouds somewhere depending upon the needs of the customers.
If you are interested in learning more about how the company achieved this feat, ask for a demo.