Workload management

Discussion of workload management technology, typically in analytic or mixed-workload DBMS.

November 3, 2011

MarkLogic’s Hadoop connector

It’s time to circle back to a subject I skipped when I otherwise wrote about MarkLogic 5: MarkLogic’s new Hadoop connector.

Most of what’s confusing about the MarkLogic Hadoop Connector lies in two pairs of options it presents you:

Otherwise, the whole thing is just what you would think:

MarkLogic said that it wrote this Hadoop connector itself.

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September 25, 2011

Workload management and RAM

Closing out my recent round of Teradata-related posts, here’s a little anomaly:

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September 22, 2011

Aster Database Release 5 and Teradata Aster appliance

It was obviously just a matter of time before there would be an Aster appliance from Teradata and some tuned bidirectional Teradata-Aster connectivity. These have now been announced. I didn’t notice anything particularly surprising in the details of either. About the biggest excitement is that Aster is traditionally a Red Hat shop, but for the purposes of appliance delivery has now embraced SUSE Linux.

Along with the announcements comes updated positioning such as:

and of course

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August 26, 2011

Virtual data marts in Sybase IQ

I made a few remarks about Sybase IQ 15.3 when it became generally available in July. Now that I’ve had a current briefing, I’ll make a few more.

The key enhancement in Sybase IQ 15.3 is distributed query — what others might call parallel query — aka PlexQ. A Sybase IQ query can now be distributed among many nodes, all talking to the same SAN (Storage-Area Network). Any Sybase IQ node can take the responsibility of being the “leader” for that particular query.

In itself, this isn’t that impressive; all the same things could have been said about pre-Exadata Oracle.* But PlexQ goes somewhat further than just removing a bottleneck from Sybase IQ. Notably, Sybase has rolled out a virtual data mart capability. Highlights of the Sybase IQ virtual data mart story include:   Read more

August 21, 2011

Hadoop evolution

I wanted to learn more about Hadoop and its futures, so I talked Friday with Arun Murthy of Hortonworks.* Most of what we talked about was:

Arun previously addressed these issues and more in a June slide deck.
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July 5, 2011

Eight kinds of analytic database (Part 1)

Analytic data management technology has blossomed, leading to many questions along the lines of “So which products should I use for which category of problem?” The old EDW/data mart dichotomy is hopelessly outdated for that purpose, and adding a third category for “big data” is little help.

Let’s try eight categories instead. While no categorization is ever perfect, these each have at least some degree of technical homogeneity. Figuring out which types of analytic database you have or need — and in most cases you’ll need several — is a great early step in your analytic technology planning.  Read more

June 20, 2011

Vertica as an analytic platform

Vertica 5.0 is coming out today, and delivering the down payment on Vertica’s analytic platform strategy. In Vertica lingo, there’s now a Vertica SDK (Software Development Kit), featuring Vertica UDT(F)s* (User-Defined Transform Functions). Vertica UDT syntax basics start:  Read more

May 3, 2011

Oracle and IBM workload management

When last night’s Oracle/Exadata post got too long — and before I knew Oracle would request a different section be cut — I set aside my comments on Oracle’s workload management story to post separately. Elements of Oracle’s workload management story include:

*Recall that “degrees of parallelism” in Oracle Parallel Query can now be set automagically.

One reason I split out this discussion of workload management is that I also talked with IBM’s Tim Vincent yesterday, who added some insight to what I already wrote last August about DB2/InfoSphere Warehouse workload management. Specifically:

April 21, 2011

In-memory, parallel, not-in-database SAS HPA does make sense after all

I talked with SAS about its new approach to parallel modeling. The two key points are:

The whole thing is called SAS HPA (High-Performance Analytics), in an obvious reference to HPC (High-Performance Computing). It will run initially on RAM-heavy appliances from Teradata and EMC Greenplum.

A lot of what’s going on here is that SAS found it annoyingly difficult to parallelize modeling within the framework of a massively parallel DBMS such as Teradata. Notes on that aspect include:

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February 5, 2011

Comments on the Gartner 2010/2011 Data Warehouse Database Management Systems Magic Quadrant

Edit: Comments on the February, 2012 Gartner Magic Quadrant for Data Warehouse Database Management Systems — and on the companies reviewed in it — are now up.

The Gartner 2010 Data Warehouse Database Management Systems Magic Quadrant is out. I shall now comment, just as I did to varying degrees on the 2009, 2008, 2007, and 2006 Gartner Data Warehouse Database Management System Magic Quadrants.

Note: Links to Gartner Magic Quadrants tend to be unstable. Please alert me if any problems arise; I’ll edit accordingly.

In my comments on the 2008 Gartner Data Warehouse Database Management Systems Magic Quadrant, I observed that Gartner’s “completeness of vision” scores were generally pretty reasonable, but their “ability to execute” rankings were somewhat bizarre; the same remains true this year. For example, Gartner ranks Ingres higher by that metric than Vertica, Aster Data, ParAccel, or Infobright. Yet each of those companies is growing nicely and delivering products that meet serious cutting-edge analytic DBMS needs, neither of which has been true of Ingres since about 1987.  Read more

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