Discussion of Kognitio – formerly Whitecross – and what it dubiously claims is its in-memory analytic DBMS. Related subjects include:
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 calls Kognitio an “in-memory” DBMS, which is not accurate.
- Gartner doesn’t remark on Kognitio’s worst-in-class* compression.
- Gartner gives Kognitio oddly high marks for a late, me-too Hadoop integration strategy.
- Gartner writes as if Kognitio’s next attempt at the US market will be the first one, which is not the case.
- Gartner says that Kognitio pioneered data warehouse SaaS (Software as a Service), which actually has existed since the pre-relational 1970s.
Gartner is correct, however, to note that Kognitio doesn’t sell much stuff overall.
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
In a call Monday with a prominent company, I was told:
- Teradata, Netezza, Greenplum and Vertica aren’t relational.
- Teradata, Netezza, Greenplum and Vertica are all data warehouse appliances.
That, to put it mildly, is not accurate. So I shall try, yet again, to set the record straight.
In an industry where people often call a DBMS just a “database” — so that a database is something that manages a database! — one may wonder why I bother. Anyhow …
1. The products commonly known as Oracle, Exadata, DB2, Sybase, SQL Server, Teradata, Sybase IQ, Netezza, Vertica, Greenplum, Aster, Infobright, SAND, ParAccel, Exasol, Kognitio et al. all either are or incorporate relational database management systems, aka RDBMS or relational DBMS.
2. In principle, there can be difficulties in judging whether or not a DBMS is “relational”. In practice, those difficulties don’t arise — yet. Every significant DBMS still falls into one of two categories:
- Was designed to do relational stuff* from the get-go, even if it now does other things too.
- Supports a lot of SQL.
- Was designed primarily to do non-relational things.*
- Doesn’t support all that much SQL.
*I expect the distinction to get more confusing soon, at which point I’ll adopt terms more precise than “relational things” and “relational stuff”.
3. There are two chief kinds of relational DBMS: Read more
Since my recent post about Kognitio, things have gotten worse. The company is insistently pushing the marketing message that Kognitio has always been an in-memory product, and at one point went so far as to publicly pretend that I had agreed.
I do not agree. Yes, it’s fair to say — as I did in 2008 — that Kognitio is very RAM-centric, but that’s not at all the same thing. In particular:
- I did due diligence for Warburg Pincus’ original investment in Kognitio in the 1990s (it was then called White Cross). I have no memory of an in-memory positioning, nor of discussing same with anybody.
- I checked my notes from a 2006 briefing, which included Kognitio CTO Roger Gaskell. There was no claim that Kognitio was an in-memory product.
- Indeed, as I also posted in 2008, Kognitio keeps indexes on disk. If you use indexes on disk, you’re not an in-memory product.
The truth is that Kognitio offers a disk-based DBMS that has long been worked on by a small team. I believe that the team really has put considerable effort into how Kognitio uses RAM. But there’s no basis to give Kognitio credit for being “really” in-memory vs. a variety of other analytic RDBMS alternatives. And a row-based product that doesn’t currently offer compression is at a large disadvantage versus, say, columnar products that already do.*
*Columnar systems don’t clobber row-based ones in-memory as extremely as they do in some disk-based use cases. But even in-memory it’s good not to have to move around data that isn’t relevant to your query.
Until Kognitio gets at least somewhat more honest in its marketing, I recommend avoiding Kognitio like the plague. It’s simply not a big enough company to buy from unless you have some level of trust in the management team.
|Categories: Columnar database management, Database compression, In-memory DBMS, Kognitio, Memory-centric data management||1 Comment|
I had dinner tonight with the Kognitio folks. So far as I can tell:
- Branding has been mercifully simplified. Everything is now called “Kognitio” (as opposed to, for example, “WX2″).
- Notwithstanding its long history of selling disk-based DBMS and denigrating memory-only configurations, Kognitio now says that in fact it’s always been an in-memory DBMS vendor.
- Notwithstanding its long history of selling (or attempting to sell) analytic DBMS, Kognitio wants to be viewed as an accelerator to your existing DBMS. This is apparently inspired in part by SAP HANA, notwithstanding that HANA’s direction is to evolve into a hybrid OLTP/analytic general-purpose DBMS.
- Notwithstanding its lack of analytic platform features, Kognitio wants to be viewed as selling an analytic platform.
- Notwithstanding its memory-centric focus, Kognitio doesn’t want to compress data. Kognitio’s opinion — which to my knowledge is shared by few people outside Kognitio — seems to be that the CPU cost of compression/decompression isn’t justified by the RAM savings from compression.
- Kognitio still is pushing a cloud/SaaS (Software as a Service) story. Even if you want to use Kognitio (the product) on-premises, Kognitio (the company) calls that “private cloud” and offers to let you pay annually.
Kognitio believes that this story is appealing, especially to smaller venture-capital-backed companies, and backs that up with some frieNDA pipeline figures.
Between that success claim and SAP’s HANA figures, it seems that the idea of using an in-memory DBMS to accelerate analytics has legs. This makes sense, as the BI vendors — Qlik Tech excepted — don’t seem to be accomplishing much with their proprietary in-memory alternatives. But I’m not sure that Kognitio would be my first choice to fill that role. Rather, if I wanted to buy an unsuccessful analytic RDBMS to use as an in-memory accelerator, I might consider ParAccel, which is columnar, has an associated compression story, has always had a hybrid memory-centric flavor much as Kognitio has, and is well ahead of Kognitio in the analytic platform derby. That said, I’ll confess to not having talked with or heard much about ParAccel for a while, so I don’t know if they’ve been able maintain technical momentum any more than Kognitio has.
|Categories: Cloud computing, Data warehousing, Database compression, Kognitio, Memory-centric data management, ParAccel, Software as a Service (SaaS)||2 Comments|
I’m frequently asked to generalize in some way about in-memory or memory-centric data management. I can start:
- The desire for human real-time interactive response naturally leads to keeping data in RAM.
- Many databases will be ever cheaper to put into RAM over time, thanks to Moore’s Law. (Most) traditional databases will eventually wind up in RAM.
- However, there will be exceptions, mainly on the machine-generated side. Where data creation and RAM data storage are getting cheaper at similar rates … well, the overall cost of RAM storage may not significantly decline.
Getting more specific than that is hard, however, because:
- The possibilities for in-memory data storage are as numerous and varied as those for disk.
- The individual technologies and products for in-memory storage are much less mature than those for disk.
- Solid-state options such as flash just confuse things further.
Consider, for example, some of the in-memory data management ideas kicking around. Read more
This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is out.* I shall now comment, just as I did on the 2010, 2009, 2008, 2007, and 2006 Gartner Data Warehouse Database Management System Magic Quadrants, to varying extents. To frame the discussion, let me start by saying:
- In general, I regard Gartner Magic Quadrants as a bad use of good research.
- Illustrating the uselessness of — or at least poor execution on — the overall quadrant metaphor, a large majority of the vendors covered are lined up near the line x = y, each outpacing the one below in both of the quadrant’s dimensions.
- I find fewer specifics to disagree with in this Gartner Magic Quadrant than in previous year’s versions. Two factors jump to mind as possible reasons:
- This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is somewhat less ambitious than others; while it gives as much company detail as its predecessors, it doesn’t add as much discussion of overall trends. So there’s less to (potentially) disagree with.
- Merv Adrian is now at Gartner.
- Whatever the problems may be with Gartner’s approach, the whole thing comes out better than do Forrester’s failed imitations.
*As of February, 2012 — and surely for many months thereafter — Teradata is graciously paying for a link to the report.
Specific company comments, roughly in line with Gartner’s rough single-dimensional rank ordering, include: Read more
Reported or rumored merger discussions between IBM and Sun are generating huge amounts of discussion today (some links below). Here are some quick thoughts around the subject of how the IBM/Sun deal — if it happens — might affect the database management system industry. Read more
|Categories: Actian and Ingres, Data warehousing, EnterpriseDB and Postgres Plus, Greenplum, IBM and DB2, Infobright, Kickfire, Kognitio, Microsoft and SQL*Server, Mid-range, MySQL, Open source, ParAccel, PostgreSQL, solidDB||9 Comments|
A few months ago, CEO Mayank Bawa of Aster Data commented to me on his surprise at how “profound” the relationship was between design choices in one aspect of a data warehouse DBMS and choices in other parts. The word choice in that was all Mayank, but the underlying thought is one I’ve long shared, and that I’m certain architects of many analytic DBMS share as well.
For that matter, the observation is no doubt true in many other product categories as well. But in the analytic database management arena, where there are literally 10-20+ competitors with different, non-stupid approaches, it seems most particularly valid. Here are some examples of what I mean. Read more
|Categories: Aster Data, Data warehousing, Exadata, Kognitio, Oracle, Theory and architecture, Vertica Systems||22 Comments|
Way back in the 1970s, a huge fraction of analytic database management was done via timesharing, specifically in connection with the RAMIS and FOCUS business-intelligence-precursor fourth-generation languages. (Both were written by Gerry Cohen, who built his company Information Builders around the latter one.) The market for remoting-computing business intelligence has never wholly gone away since. Indeed, it’s being revived now, via everything from the analytics part of Salesforce.com to the service category I call data mart outsourcing.
Less successful to date are efforts in the area of pure database software-as-a-service. It seems that if somebody is going for SaaS anyway, they usually want a more complete, integrated offering. The most noteworthy exceptions I can think of to this general rule are Kognitio and Vertica, and they only have a handful of database SaaS customers each. To wit: Read more
|Categories: 1010data, Analytic technologies, Business intelligence, Cloud computing, Data mart outsourcing, Data warehousing, Information Builders, Kognitio, Software as a Service (SaaS), Vertica Systems||9 Comments|
I went to Bracknell Wednesday to spend time with the Kognitio team. I think I came away with a better understanding of what the technology is all about, and why certain choices have been made.
Like almost every other contender in the market,* Kognitio WX-2 queries disk-based data in the usual way. Even so, WX-2′s design is very RAM-centric. Data gets on and off disk in mind-numbingly simple ways – table scans only, round-robin partitioning only (as opposed to the more common hash), and no compression. However, once the data is in RAM, WX-2 gets to work, happily redistributing as seems optimal, with little concern about which node retrieved the data in the first place. (I must confess that I don’t yet understand why this strategy doesn’t create ridiculous network bottlenecks.) How serious is Kognitio about RAM? Well, they believe they’re in the process of selling a system that will include 40 terabytes of the stuff. Apparently, the total hardware cost will be in the $4 million range.
*Exasol is the big exception. They basically use disk as a source from which to instantiate in-memory databases.
Other technical highlights of the Kognitio WX-2 story include: Read more