GigaSpaces Technologies has developed
infrastructure solutions for more than a decade and in recent years has been
enabling Big Data solutions as well. The company’s latest platform release –
XAP 9.5 [link] –
helps organizations that need to process Big Data fast. XAP harnesses the power
of in-memory computing to enable enterprise applications to function better,
whether in terms of speed, reliability, scalability or other business-critical
requirements. With the new version of XAP, increased focus has been placed on real-time
processing of big data streams, through improved data grid performance, better
manageability and end-user visibility, and integration with other parts of your
Big Data stack – in this version, integration with Cassandra.
XAP-Cassandra Integration
To build a real-time Big Data
application, you need to consider several factors.
First– Can you process your Big Data in
actual real-time, in order to get instant, relevant business insights? Batch
processing can take too long for transactional data. This doesn’t mean that you
don’t still rely on your batch processing in many ways… Second – Can you
preprocess and transform your data as it flows into the system, so that the
relevant data is made digestible and routed to your batch processor, making batch
more efficient as well. Finally, you also want to make sure the huge amounts of
data you send to long-term storage are available for both batch processing and
ad hoc querying, as needed.
XAP and Cassandra DB together can easily
enable all the above to happen. With built-in event processing capabilities,
full data consistency, and high-speed in-memory data access and local caching –
XAP handles the real-time aspect with ease. Whereas, Cassandra is perfect for
storing massive volumes of data, querying them ad hoc, and processing them
offline.
Several hurdles had to be overcome to
make the integration truly seamless and easy for end users – including XAP’s
document-oriented model vs. Cassandra’s columnar data model, XAP’s immediate
consistency (data must be able to move between models smoothly), XAP offers
immediate consistency with performance, while Cassandra trades off between
performance and consistency (with Cassandra as the Big Data store behind XAP
processing, both consistency and performance are maintained).
Together with the Cassandra integration,
XAP offers further enhancements. These include:
Data Grid Enhancements
To further optimize your queries over the
data grid XAP now includes compound
indices, which enable you to index multiple attributes. This way the grid
scans one index instead of multiple indices to get query result candidates
faster.
On the query side, new projections support enables you to
query only for the attributes you’re interested in instead of whole
objects/documents. All of these optimizations dramatically reduce latency and
increase the throughput of the data grid in common scenarios.
The enhanced change API includes the ability to change multiple objects using a
SQL query or POJO template. Replication of change operations over the WAN has
also been streamlined, and it now replicates only the change commands instead
of whole objects. Finally, a hook in the Space Data Persister interface enables
you to optimize your DB SQL statements or ORM configuration for partial
updates.
Visibility and Manageability Enhancements
A new web UI gives XAP users deep
visibility into important aspects of the data grid, including event containers,
client-side caches, and multi-site replication gateways.
Managing a low latency, high throughput,
distributed application is always a challenge due to the amount of moving
parts. The new enhanced UI helps users to maintain agility when managing their
application.
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