Columnar database management

Analysis of products and issues in column-oriented database management systems. Related subjects include:

June 23, 2013

Impala and Parquet

I visited Cloudera Friday for, among other things, a chat about Impala with Marcel Kornacker and colleagues. Highlights included:

Data gets into Parquet via batch jobs only — one reason it’s important that Impala run against multiple file formats — but background format conversion is another roadmap item. A single table can be split across multiple formats — e.g., the freshest data could be in HBase, with the rest is in Parquet.

Read more

May 27, 2013


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 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 are all in that business in some form, and other big companies have dipped toes in as well. Read more

April 29, 2013

More on Actian/ParAccel/VectorWise/Versant/etc.

My quick reaction to the Actian/ParAccel deal was negative. A few challenges to my views then emerged. They didn’t really change my mind.

Amazon Redshift

Amazon did a deal with ParAccel that amounted to:

Some argue that this is great for ParAccel’s future prospects. I’m not convinced.

No doubt there are and will be Redshift users, evidently including Infor. But so far as I can tell, Redshift uses very standard SQL, so it doesn’t seed a ParAccel market in terms of developer habits. The administration/operation story is similar. So outside of general validation/bragging rights, Redshift is not a big deal for ParAccel.

OEMs and bragging rights

It’s not just Amazon and Infor; there’s also a MicroStrategy deal to OEM ParAccel — I think it’s the real ParAccel software in that case — for a particular service, MicroStrategy Wisdom. But unless I’m terribly mistaken, HP Vertica, Sybase IQ and even Infobright each have a lot more OEMs than ParAccel, just as they have a lot more customers than ParAccel overall.

This OEM success is a great validation for the idea of columnar analytic RDBMS in general, but I don’t see where it’s an advantage for ParAccel vs. the columnar leaders. Read more

April 25, 2013

Goodbye VectorWise, farewell ParAccel?

Actian, which already owns VectorWise, is also buying ParAccel. The argument for why this kills VectorWise is simple. ParAccel does most things VectorWise does, more or less as well. It also does a lot more:

One might conjecture that ParAccel is bad at highly concurrent, single-node use cases, and VectorWise is better at them — but at the link above, ParAccel bragged of supporting 5,000 concurrent connections. Besides, if one is just looking for a high-use reporting server, why not get Sybase IQ?? Anyhow, Actian hasn’t been investing enough in VectorWise to make it a major market player, and they’re unlikely to start now that they own ParAccel as well.

But I expect ParAccel to fail too. Reasons include:

Read more

April 14, 2013

Introduction to Deep Information Sciences and DeepDB

I talked Friday with Deep Information Sciences, makers of DeepDB. Much like TokuDB — albeit with different technical strategies — DeepDB is a single-server DBMS in the form of a MySQL engine, whose technology is concentrated around writing indexes quickly. That said:

*For reasons that do not seem closely related to product reality, DeepDB is marketed as if it supports “unstructured” data today.

Other NewSQL DBMS seem “designed for big data and the cloud” to at least the same extent DeepDB is. However, if we’re interpreting “big data” to include multi-structured data support — well, only half or so of the NewSQL products and companies I know of share Deep’s interest in branching out. In particular:

Edit: MySQL has some sort of an optional NoSQL interface, and hence so presumably do MySQL-compatible TokuDB, GenieDB, Clustrix, and MemSQL.

Also, some of those products do not today have the transparent scale-out that Deep plans to offer in the future.

Read more

March 26, 2013

Platfora at the time of first GA

Well-resourced Silicon Valley start-ups typically announce their existence multiple times. Company formation, angel funding, Series A funding, Series B funding, company launch, product beta, and product general availability may not be 7 different “news events”, but they’re apt to be at least 3-4. Platfora, no exception to this rule, is hitting general availability today, and in connection with that I learned a bit more about what they are up to.

In simplest terms, Platfora offers exploratory business intelligence against Hadoop-based data. As per last weekend’s post about exploratory BI, a key requirement is speed; and so far as I can tell, any technological innovation Platfora offers relates to the need for speed. Specifically, I drilled into Platfora’s performance architecture on the query processing side (and associated data movement); Platfora also brags of rendering 100s of 1000s of “marks” quickly in HTML5 visualizations, but I haven’t a clue as to whether that’s much of an accomplishment in itself.

Platfora’s marketing suggests it obviates the need for a data warehouse at all; for most enterprises, of course, that is a great exaggeration. But another dubious aspect of Platfora marketing actually serves to understate the product’s merits — Platfora claims to have an “in-memory” product, when what’s really the case is that Platfora’s memory-centric technology uses both RAM and disk to manage larger data marts than could reasonably be fit into RAM alone. Expanding on what I wrote about Platfora when it de-stealthedRead 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 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 5, 2013

Comments on Gartner’s 2012 Magic Quadrant for Data Warehouse Database Management Systems — evaluations

To my taste, the most glaring mis-rankings in the 2012/2013 Gartner Magic Quadrant for Data Warehouse Database Management are that it is too positive on Kognitio and too negative on Infobright. Secondarily, it is too negative on HP Vertica, and too positive on ParAccel and Actian/VectorWise. So let’s consider those vendors first.

Gartner seems confused about Kognitio’s products and history alike.

Gartner is correct, however, to note that Kognitio doesn’t sell much stuff overall.

* non-existent

In the cases of HP Vertica, Infobright, ParAccel, and Actian/VectorWise, the 2012 Gartner Magic Quadrant for Data Warehouse Database Management’s facts are fairly accurate, but I dispute Gartner’s evaluation. When it comes to Vertica: Read more

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