Kx Technical Whitepaper: Disaster recovery planning for kdb+ tick systems

2 Nov 2017 | , , , ,
Share on:

By Stewart Robinson

Disasters are inevitable; hardware failure, network problems and data corruption are all events which could play havoc with a system. If these events are not fully understood and planned for they may lead to significant downtime and potentially severe business impact, including revenue loss, legal and financial implications, and impact to business reputation and brand. Recent high-profile systems failures of a large cloud computing provider and an international airline highlight the importance for IT systems to have a comprehensive disaster recovery plan in place.

This whitepaper discusses disaster recovery (DR) and failover concepts from the perspective of the gateway layer accessing a typical kdb+ tick system used in capital markets applications. The end goal of constructing this plan is to ensure high availability of the application via the gateway where possible, considering all conceivable failure scenarios and outlining any actions required to prevent data loss, minimize any downtime and keep the application accessible.

Please visit code.kx.com for a complete archive of valuable kdb+ technical whitepapers.

SUGGESTED ARTICLES

Kx Product Insights: Inter-Trading Alert

5 Dec 2018 | , , ,

by Aidan O’Neill Kx has a broad list of products and solutions built on the time-series database platform kdb+ that capitalize on its high-performance capabilities when analyzing very large datasets. Kx for Surveillance is a robust platform widely used by financial institutions for monitoring trades for regulatory compliance. The Surveillance platform instantly detects known trading […]

Kx extends relationship with NASA Frontier Development Lab and the SETI Institute

The Exploration of Space Weather at NASA FDL with kdb+

4 Dec 2018 | , , , ,

Our society is dependent on GNSS services for navigation in everyday life, so it is critically important to know when signal disruptions might occur. Physical models have struggled to predict astronomic scintillation events. One method for making predictions is to use machine learning (ML) techniques. This article describes how kdb+ and embedPy were used in the ML application.