In response to Monash's post on the four categories of RDBMS
As I did last week, I am using this post to respond to an article published by Curt Monash. You can read his full post, titled "Database management system choices - 4 categories of relational," here. In this post, I will discuss the issue I have with Curt's category characterization of DBMS systems.
First, I see two categories of relational analytic/data warehouse databases, row stores and column stores. They have very different characteristics. I would not lump them together, as this post does. Moreover, I expect the overwhelming majority of analytic data management workloads to move to column stores over time as these products become more mature because of the overwhelming performance advantage they offer on most analytic workloads.
I don't know what competitive challenge to current high-end OLTP vendors Curt has in mind; however, I will offer my own. If performance is not a big issue, then current open-source relational DBMSs work quite well. As a result, I expect the "low end" to go to open source systems.
On the other hand, if high performance is required, then I have shown in a recent paper (2007 VLDB proceedings) that current high-end relational engines can be beaten by a factor of 80 or so on TPC-C. This new collection of ideas may be leveragable into ultra-fast future commercial products that will challenge the current vendors at the high end. I think it is likely that the current vendors will be "caught in the middle."
Lastly, most customers that I talk to are upset with the "out-of-box" experience of the current offerings from the high-end vendors. The products are hard to install, hard to tune, hard to learn, and just generally hard to use. If the products don't get much easier to use, then data administration costs will go to 100% sooner or later -- relegating these products to niche markets.
First, I see two categories of relational analytic/data warehouse databases, row stores and column stores. They have very different characteristics. I would not lump them together, as this post does. Moreover, I expect the overwhelming majority of analytic data management workloads to move to column stores over time as these products become more mature because of the overwhelming performance advantage they offer on most analytic workloads.
I don't know what competitive challenge to current high-end OLTP vendors Curt has in mind; however, I will offer my own. If performance is not a big issue, then current open-source relational DBMSs work quite well. As a result, I expect the "low end" to go to open source systems.
On the other hand, if high performance is required, then I have shown in a recent paper (2007 VLDB proceedings) that current high-end relational engines can be beaten by a factor of 80 or so on TPC-C. This new collection of ideas may be leveragable into ultra-fast future commercial products that will challenge the current vendors at the high end. I think it is likely that the current vendors will be "caught in the middle."
Lastly, most customers that I talk to are upset with the "out-of-box" experience of the current offerings from the high-end vendors. The products are hard to install, hard to tune, hard to learn, and just generally hard to use. If the products don't get much easier to use, then data administration costs will go to 100% sooner or later -- relegating these products to niche markets.
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