February 2, 2011

Exadata notes

It’s been a while since I penetrated Oracle’s tight message control and actually talked with them about Exadata. But Doug Henschen wrote a good article about Exadata based on an Andy Mendelsohn webcast. I agree with almost all of it. At first I was a little surprised that Exadata’s emphasis shift from data warehousing to OLTP/generic consolidation hasn’t gone more quickly, but on the other hand:

Doug did overstate when he said that columnar architectures give 100X or more compression. That doesn’t happen. Yes, columnar compression can be >10X in a variety of use cases, while pre-Exadata Oracle index bloat can approach 10X at times; but even if you’re counting that way I doubt there are many instances in which it actually multiplies out to >100.

In other Exadata news, the long-standing observation that Oracle doesn’t like to do on-site Exadata POCs still holds true. A couple of existing Oracle users — one rather well-known — recently told me that Oracle won’t let them text Exadata except on Oracle premises. In one case, this is a deal-breaker keeping Exadata from being considered for a purchase, and Oracle still won’t budge.

Finally, I’m pretty sure that this “new” Softbank Teradata replacement Oracle has been touting since September as competitive evidence — which Doug’s article also references — isn’t quite what it sounds like. I believe Teradata’s version of the story, which somewhat edited goes like this:  Read more

January 18, 2011

Architectural options for analytic database management systems

Mike Stonebraker recently kicked off some discussion about desirable architectural features of a columnar analytic DBMS. Let’s expand the conversation to cover desirable architectural characteristics of analytic DBMS in general.  Read more

October 17, 2010

Where ParAccel is at

Until recently, I was extremely critical of ParAccel’s marketing. But there was an almost-clean sweep of the relevant ParAccel executives, and the specific worst practices I was calling out have for the most part been eliminated. So I was open to talking and working with ParAccel again, and that’s now happening. On my recent California trip, I chatted with three ParAccel folks for a few hours. Based on that and other conversation, here’s the current ParAccel story as I understand it.
Read more

June 14, 2010

Best practices for analytic DBMS POCs

When you are selecting an analytic DBMS or appliance, most of the evaluation boils down to two questions:

And so, in undertaking such a selection, you need to start by addressing three issues:

Read more

June 11, 2010

Ingres VectorWise technical highlights

After working through problems w/ travel, cell phones, and so on, Peter Boncz of VectorWise finally caught up with me for a regrettably brief call. Peter gave me the strong impression that what I’d written in the past about VectorWise had been and remained accurate, so I focused on filling in the gaps. Highlights included:  Read more

April 16, 2010

Story of an analytic DBMS evaluation

One of our readers was kind enough to walk me through his analytic DBMS evaluation process. The story is:

Notes on the Vertica vs. ParAccel selection include: Read more

April 12, 2010

Greenplum Chorus and Greenplum 4.0

Greenplum is making two product announcements this morning. Greenplum 4.0 is a revision of the core Greenplum database technology. In addition, Greenplum is announcing Greenplum Chorus, which is the first product release instantiating last year’s EDC (Enterprise Data Cloud) vision statement and marketing campaign.

Greenplum 4.0 highlights and related observations include: Read more

March 18, 2010

XtremeData update

I talked with Geno Valente of XtremeData tonight. Highlights included:

Naming aside, Read more

October 30, 2009

A question on MDX performance

An enterprise user wrote in with a question that boils down to:

What are reasonable MDX performance expectations?

MDX doesn’t come up in my life very much, and I don’t have much intuition about it. E.g., I don’t know whether one can slap an MDX-to-SQL converter on top of a fast analytic RDBMS and go to town. What’s more, I’m heading off on vacation and don’t feel like researching the matter myself in the immediate future. :)

So here’s the long form of the question. Any thoughts?

I have a general question on assessing the performance of an OLAP technology using a set of MDX queries. I would be interested to know if there are any benchmark MDX performance tests/results comparing different OLAP technologies (which may be based on different underlying DBMS’s if appropriate) on similar hardware setup, or even comparisons of complete appliance solutions. More generally, I want to determine what performance limits I could reasonably expect on what I think are fairly standard servers.

In my own work, I have set up a star schema model centered on a Fact table of 100 million rows (approx 60 columns), with dimensions ranging in cardinality from 5 to 10,000. In ad hoc analytics, is it expected that any query against such a dataset should return a result within a minute or two (i.e. before a user gets impatient), regardless of whether that query returns 100 cells or 50,000 cells (without relying on any aggregate table or caching mechanism)? Or is that level of performance only expected with a high end massively parallel software/hardware solution? The server specs I’m testing with are: 32-bit 4 core, 4GB RAM, 7.2k RPM SATA drive, running Windows Server 2003; 64-bit 8 core, 32GB RAM, 3 Gb/s SAS drive, running Windows Server 2003 (x64).

I realise that caching of query results and pre-aggregation mechanisms can significantly improve performance, but I’m coming from the viewpoint that in purely exploratory analytics, it is not possible to have all combinations of dimensions calculated in advance, in addition to being maintained.

September 30, 2009

Facts and rumors

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