The Rise of the Columnar Database

photo by eflon

I’m continually surprised that more vendors haven’t hurled themselves onto the columnar database bandwagon. The more this space matures, the more evident it becomes that analytics is a perfect match for column-based database architectures.

One of the most frustrating phenomena to IT is adherence to a theoretical view. In the 1970s the entire relational database industry implemented what was really an academic precept. For those pragmatists who haven’t dusted off their textbooks recently, I’ll recall the writings of Codd and Date. They introduced the concepts of organizing data in tuples, organizing primary values along with their descriptive details (aka: attributes). Vendors interpreted this to mean that data should be physically stored in this fashion, architecting their products to store data in tables, populated with rows consisting of columns. If you wanted to access a value, you had to retrieve the entire row.

With all due respect, this approach has been cumbersome since Day 1. The fact is, storing data the way the business looks doesn’t lend itself to the way people ask questions. When I create an outbound marketing list, I need a name, a phone number, and an address. I don’t need information on household, demographic segment, or the name of a customer’s dog.

While I do need to store all the customer data, I don’t want to be bogged down by processing all that data in order to answer my question. Herein lies the quandary: do I structure the data based on all the information we have, or based on the information I might access?

Vendors have tried to bridge the gap. We’ve seen partitioning, star indexes, query pre-processing, bitmap and index joins, and even hashing in an attempt to support more specific data retrieval. Such solutions still require examining the contents of the entire row.

Although my background is in engineering, I know enough about Occam’s razor to know that it applies here: the simplest solution is the best one. Vendors like Kickfire, Vertica, Paraccel, and Sybase—whose pioneering IQ product launched over a dozen years ago–went back to the drawing board and fixed the problem, architecting their products structure and store the data the way people ask questions—in columns.

For you SQL jockeys, most of the heavy-lifting in database processing is in the where clause. Columnar databases are faster because their processing isn’t inhibited by unnecessary row content. Because many database tables can have upwards of 100 columns, and because most business questions only request a handful of them, this just makes business sense. And In these days of multi-billion row tables and petabyte-sized systems, columnar databases make more sense than ever.

As the data warehouse market continues to consolidate through acquisitions, look for column-based startups—including several open-source solutions—to fill the void. If you ask me, there’s plenty of room.

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About Evan Levy

Evan Levy is management consultant and partner at IntegralData. In addition to his day-to-day job responsibilities, Evan speaks, writes, and blogs about the challenges of managing and using data to support business decision making.

2 responses to “The Rise of the Columnar Database”

  1. Ramon Chen says :

    Excellent overview as usual Evan. I linked to your post from my new post “Will Hadoop render RDBMs Irelephant? Hadoop and MapReduce for beginners and non-techies.”

  2. Roni says :

    Thank you for the post Evan.
    SiSense just launched a columnar based in memory SMB BI tool, based on MonetDB. We’re seeing amazing query response times, and can contain much more data; completely eliminating any need for a data warehouse approach.

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