IBM and DB2

Analysis of IBM and various of its product lines in database management, analytics, and data integration.

July 20, 2013

The refactoring of everything

I’ll start with three observations:

As written, that’s probably pretty obvious. Even so, it’s easy to forget just how pervasive the refactoring is and is likely to be. Let’s survey some examples first, and then speculate about consequences. Read more

July 12, 2013

More notes on predictive modeling

My July 2 comments on predictive modeling were far from my best work. Let’s try again.

1. Predictive analytics has two very different aspects.

Developing models, aka “modeling”:

More precisely, some modeling algorithms are straightforward to parallelize and/or integrate into RDBMS, but many are not.

Using models, most commonly:

2. Some people think that all a modeler needs are a few basic algorithms. (That’s why, for example, analytic RDBMS vendors are proud of integrating a few specific modeling routines.) Other people think that’s ridiculous. Depending on use case, either group can be right.

3. If adoption of DBMS-integrated modeling is high, I haven’t noticed.

Read more

June 23, 2013

Hadoop news and rumors, June 23, 2013

Cloudera

*Of course, there will always be exceptions. E.g., some formats can be updated on a short-request basis, while others can only be written to via batch conversions.

Everybody else

May 27, 2013

IBM BLU

I had a good chat with IBM about IBM BLU, aka BLU Accelerator or Acceleration. BLU basics start:

And yes — that means Oracle is now the only major relational DBMS vendor left without a true columnar story.

BLU’s maturity and scalability basics start:

BLU technical highlights include: Read more

May 27, 2013

Data skipping

Way back in 2006, I wrote about a cool Netezza feature called the zone map, which in essence allows you to do partition elimination even in the absence of strict range partitioning.

Netezza’s substitute for range partitioning is very simple. Netezza features “zone maps,” which note the minimum and maximum of each column value (if such concepts are meaningful) in each extent. This can amount to effective range partitioning over dates; if data is added over time, there’s a good chance that the data in any particular date range is clustered, and a zone map lets you pick out which data falls in the desired data range.

I further wrote

… that seems to be the primary scenario in which zone maps confer a large benefit.

But I now think that part was too pessimistic. For example, in bulk load scenarios, it’s easy to imagine ways in which data can be clustered or skewed. And in such cases, zone maps can let you skip a large fraction of potential I/O.

Over the years I’ve said that other things were reminiscent of Netezza zone maps, e.g. features of Infobright, SenSage, InfiniDB and even Microsoft SQL Server. But truth be told, when I actually use the phrase “zone map”, people usually give me a blank look.

In a recent briefing about BLU, IBM introduced me to a better term — data skipping. I like it and, unless somebody comes up with a good reason not to, I plan to start using it myself. :)

May 20, 2013

Some stuff I’m working on

1. I have some posts up on Strategic Messaging. The most recent are overviews of messaging, pricing, and positioning.

2. Numerous vendors are blending SQL and JSON management in their short-request DBMS. It will take some more work for me to have a strong opinion about the merits/demerits of various alternatives.

The default implementation — one example would be Clustrix’s — is to stick the JSON into something like a BLOB/CLOB field (Binary/Character Large Object), index on individual values, and treat those indexes just like any others for the purpose of SQL statements. Drawbacks include:

IBM DB2 is one recent arrival to the JSON party. Unfortunately, I forgot to ask whether IBM’s JSON implementation was based on IBM DB2 pureXML when I had the chance, and IBM hasn’t gotten around to answering my followup query.

3. Nor has IBM gotten around to answering my followup queries on the subject of BLU, an interesting-sounding columnar option for DB2.

4. Numerous clients have asked me whether they should be active in DBaaS (DataBase as a Service). After all, Amazon, Google, Microsoft, Rackspace and salesforce.com are all in that business in some form, and other big companies have dipped toes in as well. Read more

March 18, 2013

DBMS development and other subjects

The cardinal rules of DBMS development

Rule 1: Developing a good DBMS requires 5-7 years and tens of millions of dollars.

That’s if things go extremely well.

Rule 2: You aren’t an exception to Rule 1. 

In particular:

DBMS with Hadoop underpinnings …

… aren’t exceptions to the cardinal rules of DBMS development. That applies to Impala (Cloudera), Stinger (Hortonworks), and Hadapt, among others. Fortunately, the relevant vendors seem to be well aware of this fact. Read more

February 27, 2013

Hadoop distributions

Elephants! Elephants!
One elephant went out to play
Sat on a spider’s web one day.
They had such enormous fun
Called for another elephant to come.

Elephants! Elephants!
Two elephants went out to play
Sat on a spider’s web one day.
They had such enormous fun
Called for another elephant to come.

Elephants! Elephants!
Three elephants went out to play
Etc.

–  Popular children’s song

It’s Strata week, with much Hadoop news, some of which I’ve been briefed on and some of which I haven’t. Rather than delve into fine competitive details, let’s step back and consider some generalities. First, about Hadoop distributions and distro providers:

Most of the same observations could apply to Hadoop appliance vendors.

Read more

February 21, 2013

One database to rule them all?

Perhaps the single toughest question in all database technology is: Which different purposes can a single data store serve well? — or to phrase it more technically — Which different usage patterns can a single data store support efficiently? Ted Codd was on multiple sides of that issue, first suggesting that relational DBMS could do everything and then averring they could not. Mike Stonebraker too has been on multiple sides, first introducing universal DBMS attempts with Postgres and Illustra/Informix, then more recently suggesting the world needs 9 or so kinds of database technology. As for me — well, I agreed with Mike both times. :)

Since this is MUCH too big a subject for a single blog post, what I’ll do in this one is simply race through some background material. To a first approximation, this whole discussion is mainly about data layouts — but only if we interpret that concept broadly enough to comprise:

To date, nobody has ever discovered a data layout that is efficient for all usage patterns. As a general rule, simpler data layouts are often faster to write, while fancier ones can boost query performance. Specific tradeoffs include, but hardly are limited to: Read more

February 6, 2013

Key questions when selecting an analytic RDBMS

I recently complained that the Gartner Magic Quadrant for Data Warehouse DBMS conflates many use cases into one set of rankings. So perhaps now would be a good time to offer some thoughts on how to tell use cases apart. Assuming you know that you really want to manage your analytic database with a relational DBMS, the first questions you ask yourself could be:

Let’s drill down. Read more

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