Providing a reliable infrastructure for rapid, unattended, automated failover

Technology Overview

Traditional availability and replication solutions often require substantial capital investments in infrastructure, deployment, configuration, software licensing, and planning. Caché Database Mirroring (Mirroring) is designed to provide an economical solution for rapid, reliable, robust, automatic failover between two Caché systems, making mirroring the ideal automatic failover high-availability solution for the enterprise.

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Introduction

To overcome the performance limitations of traditional relational databases, applications - ranging from those running on a single machine to large, interconnected grids - often use in-memory databases to accelerate data access. While in-memory databases and caching products increase throughput, they suffer from a number of limitations including lack of support for large data sets, excessive hardware requirements, and limits on scalability.

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Introduction

If the administrators responsible for securing applications had their way, passwords would be long complex strings of random symbols, and users would memorize different passwords for every application they use. But in the real world, few people are capable of such prodigious feats of memory. The typical user can only remember a handful of relatively short passwords.

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Abstract

In a recent benchmark test of an application based on InterSystems Caché, a sustainable rate of 8.9million database accesses/second, with peaks of 16.9 million database accesses/second, was achieved. These results were from a test performed on a connected system of eight applications servers, using Intel Xeon 5570 processors, and running Linux as the operating system. This benchmark shows that:

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InterSystems has implemented a broad set of MultiValue extensions for its Caché multidimensional database. These extensions enable the migration of MultiValue applications to Caché and bring the full range of Caché object and SQL development technologies to MultiValue developers. The result: your existing MultiValue investments are preserved, you gain a broad spectrum of highly scalable deployment options, and your developers can combine the best of MultiValue, object, relational, and technologies to extend existing applications and build new ones.

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Introduction

Experts estimate that 85% of all data exists in unstructured formats – held in e-mails, documents (contracts, memos, clinical notes, legal briefs), social media feeds, etc. Where structured data typically accounts for quantitative facts, the more interesting and potentially more valuable expert opinions and conclusions are often hidden in these unstructured formats. And with massive volumes of text being generated at unprecedented speed, there’s very little chance this information can be made useful without some process of synthesis or automation.

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Executive Summary

The best way to compare the performance of database products is in a head-to-head test using a real application, preferably one of your own. This is especially true when evaluating Caché's post-relational technology, because "standard" transaction processing benchmarking methodologies assume the restrictive "row and columns" format of a relational database. They cannot accurately predict the performance of real applications, which often use complex data models.

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Introduction

By now, anybody working in the technology sector will have heard of Cloud computing. But the concept is increasingly being paid attention to outside of IT departments, with growing recognition among boardlevel executives of the potential of this range of innovations. Frequently, senior personnel are hearing stories about how the Cloud helps organizations reduce costs, boost efficiency and expand their operations, so they’ll be excited about what the Cloud can do for them.

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Introduction

With the maturation and wide acceptance of Java, object-oriented programming has moved to the foreground of the application development landscape. Because of their rich data models and support for productivity-enhancing concepts such as encapsulation, inheritance, and polymorphism, object technologies like Java, C++, and COM, are favored by today's application developers.

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Introduction - Analyzing Textual Big Data

Big Data for Enriching Analytical Capabilities - Big data is revolutionizing the world of business intelligence and analytics. Gartner predicts that big data will drive $232 billion in spending through 2016, Wikibon claims that by 2017 big data revenue will have grown to $47.8 billion, and McKinsey Global Institute indicates that big data has the potential to increase the value of the US health care industry by $300 billion and to increase the industry value of Europe's public sector administration by Ä250 billion.

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Customers who switch to Caché from relational databases report that their average performance is up to 20 time faster, running on the same hardware, with no changes to the application. What is it about Caché that lets applications run so fast?

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Introduction

This document is intended to provide a survey of various High Availability (HA) strategies that can be used in conjunction with InterSystems Caché, Ensemble, and HealthShare Foundation. This document also provides an overview of the various types of system outages that can occur, as well as how each strategy would handle a given outage, with the goal of helping you choose the right strategy for your specific deployment.

The strategies surveyed in this document are based on three different HA technologies:

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Introduction

Because of increasing business and governmental pressures to integrate their operations, the financial services industry is developing a number of standards for data exchange and other common functions. Standards such as XBRL, FpML, MDDL, RIXML, and FIXML are all specialized dialects of XML (Extensible Markup Language). Any financial services application with good support for XML will be able to communicate effectively using one or more of the emerging industry standards.

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Abstract

A global provider of mobile telecommunications software tested the performance of InterSystems Caché and Oracle as the database in a simulated data mart application. They found Caché to be 41% faster than Oracle at building a data mart. When testing the response time to SQL queries of the data mart, Caché's performance ranged from 1.8 times to 513 times faster than Oracle.

Introduction

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InterSystems encourages the adoption of a flexible, practical approach to application development, rather than strict adherence to one of the prevalent development theories. This paper offers advice based upon our experience. However needs, attitudes, and styles vary; we recommend that each programmer choose the development approach that works best for them. Caché supports a wide range of development methodologies, not just those recommended here.

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Executive Overview

One way financial services firms can improve their operational efficiency is to revamp their data management infrastructure. Creating a central repository for data that is used by multiple applications can ensure data consistency and quality across the enterprise, ease integration bottlenecks, and lower the number of failed trades.However, different applications have different database usage patterns. To satisfy them all, any central data repository must:

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Introduction

With the growing popularity of smart phones and tablet computers, consumers are coming to expect that software solutions will be presented as "apps" on their mobile devices. The challenge for most application developers is to find ways to make their existing solutions run on modern mobile platforms without incurring the delay and cost of a complete rewrite.

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Abstract

The European Space Agency (ESA) has chosen InterSystems Caché as the database technology for the AGIS astrometric solution that will be used to analyze the celestial data captured by the Gaia satellite.

The Gaia mission is to create an accurate phase-map of about a billion celestial objects. During the mission, the AGIS solution will iteratively refine the accuracy of Gaia's spatial observations, ultimately achieving accuracies that are on the order of 20 microarcseconds.

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The Caché System Monitor will call your custom subscriber class after your custom sensor class runs. Your custom subscriber class evaluates the sensor readings gathered by your customer sensor class to send an email and/or notification.

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The Caché System Monitor can be extended to create notifications and email based on application generated sensor readings. A custom sensor class gathers application sensor readings for evaluation and/or notification

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The Caché System Monitor calls your custom notifier class after your custom subscriber class evaluated sensor readings and created the notification. Notifications can be sent to cconsole.log, SystemManager.log or via email

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