Introduction

Ideally, IT managers would never need to think about the details of data

management technology. Market-leading, general-purpose DBMS (DataBase

Management Systems) would do a great job of meeting all information

management needs. But we don’t live in an ideal world. Even after decades of

great technical advances, conventional DBMS still can’t give your users all the

information they need, when and where they need it, at acceptable cost. As a

result, specialty data management products continue to be needed, filling the

gaps where more general DBMS don’t do an adequate job.

One category on the upswing is memory-centric data management

technology. While conventional DBMS are designed to get data on and off disk

quickly, memory-centric products (which may or may not be full DBMS)

assume all the data is in RAM in the first place. The implications of this design

choice can be profound. RAM access speeds are up to 1,000,000 times faster

than random reads on disk. Consequently, whole new classes of data access

methods can be used when the disk speed bottleneck is ignored. Sequential

access is much faster in RAM, too, allowing yet another group of efficient data

access approaches to be implemented.

If you want to query a used-book database a million times a minute, that’s hard

to do in a standard relational DBMS. But Progress’ ObjectStore gets it done for

Amazon. If you want to recalculate a set of OLAP (OnLine Analytic

Processing) cubes in real-time, don’t look to a disk-based system of any kind.

But Applix’s TM1 can do just that. And if you want to stick DBMS instances

on 99 nodes of a telecom network, all persisting data to a 100th node, a diskcentric

system isn’t your best choice – but Solid’s BoostEngine should get the

job done.

Those products are some leading examples of a diverse group of specialist

memory-centric data management products. Such products can be optimized for

OLAP or OLTP (OnLine Transaction Processing) or event-stream processing.

They may be positioned as DBMS, quasi-DBMS, BI (Business Intelligence)

features, or some utterly new kind of middleware. They may come from top-tier

software vendors or from the rawest of startups. But they all share a common

design philosophy: Optimize the use of ever-faster semiconductors, rather than

focusing on (relatively) slow-spinning disks.