CERN Computing Seminar

Real-Time processing of Big Data with ScyllaDB

by Glauber Costa (ScyllaDB), Miguel Martinez Pedreira (Johann-Wolfgang-Goethe Univ. (DE))

Europe/Zurich
31-3-004 - IT Amphitheatre (CERN)

31-3-004 - IT Amphitheatre

CERN

105
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Description

ScyllaDB: achieving 1 million operations/sec with stable and consistent real time latencies

This talk will present ScyllaDB, a highly available Real-time Big Data Database that can achieve high throughput without compromising latencies or availability. ScyllaDB is API-compatible with Apache Cassandra but employs a different internal architecture to make sure that operational capacity is increased while the maintenance burden is reduced. It provides everything that a new-world database must provide: horizontal (infinite) scaling, no single point of failure, high availability and excellent performance, while keeping a sensible amount of operational efforts. Some of the key points that make ScyllaDB very efficient are its fully asynchronous operations and the smart integration with the kernel and hardware. You will learn about what makes ScyllaDB special in the crowded space of NoSQL solutions and how it can be used to power a wide variety of workloads: from real time bidding to the experiment data from the ALICE Collaboration.

About the speakers

Glauber Costa (Lord Glauber I of Sealand) is a Principal Architect at ScyllaDB. He shares his time between the engineering department working on upcoming Scylla features and helping customers succeed. Before ScyllaDB, Glauber worked with Virtualization in the Linux Kernel for 10 years, with contributions ranging from the Xen Hypervisor to all sorts of guest functionality and containers.

Miguel Martinez Pedreira is a core developer in the ALICE Offline team at CERN, working on the GRID middleware called AliEn and involved in the operation and maintenance of its multiple backends. The main one is the File Catalogue, that holds all the ALICE experiment data, and Miguel is facing its redesign in order to be ready for LHC Run3 scalability challenges.

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