Distributed applications must be highly resilient and scalable. The integration of Riak and Mesos through the Apache Mesos Framework makes it easy to deploy and manage Riak KV and Riak TS clusters at enterprise scale, allows fine-grained resource utilization, and provides enhanced resilience against failures.
Why Apache Mesos and Riak?
Apache Mesos is becoming a common choice for highly scalable Big Data applications. Apache Mesos is a cluster manager that abstracts CPU, memory, storage, and other compute resources enabling fault-tolerant and elastic distributed systems. By integrating Riak with Mesos, customers no longer need to guess about the infrastructure requirements of their Riak nodes. Resource management is optimized by Mesos. With Riak managing the data tier and Mesos managing the underlying infrastructure, customers can efficiently and easily scale distributed applications. The integration also allows for true “push button” scale up/scale down as Mesos can aggregate and re-aggregate resources for Riak nodes.
Riak KV and Riak TS are integrated with Apache Mesos using the Riak Mesos Framework. Apache Mesos provides a set of APIs so that distributed systems technology, like Riak, can run in a Mesos environment. The service management is performed by the implementation of a Mesos Scheduler and the tasks are performed by implementation of a Mesos Executor. Additionally, Riak Mesos Framework has been integrated with Marathon and DC/OS CLI.
Apache Mesos with Riak ensures your Big Data and IoT applications are available, scalable, and easy to operate at scale.
“Companies using traditional architectures are struggling to accommodate the need for distributed applications and distributed data services,” said Dave McCrory, Chief Technology Officer at Riak. “By combining Riak’s Riak with Mesos, we’re able to deliver an easy-to-deploy platform for real-time data processing. We thereby enable a new class of modern datacenter developers who can break free of infrastructure restraints and give rise to a whole new class of hyper-scale applications.”