Faster or Free

I don’t know how Bruce Scott’s article showed up in my mailbox but I’m confused by it (happens a lot these days).

I agree with him that too much has been made about whether a system is a columnar system or a truly columnar system or a vertically partitioned row store and what really matters to a customer is TCO and price-performance in their own environment. Bruce says as much in his blog post

Let’s start talking about what customers really care about: price-performance and low cost of ownership. Customers want to do more with less. They want less initial cost and less ongoing cost.

Then, he goes on to say

On this last point, we have found that we always outperform our competitors in customer created benchmarks, especially when there are previously unforeseen queries. Due to customer confidentiality this can appear to be a hollow claim that we cannot always publicly back up with customer testimonials. Because of this, we’ve decided to put our money where our mouth is in our “Faster or Free” offer. Check out our website for details here: http://www.paraccel.com/faster_or_free.php

So, I went and looked at that link. There, it says:

Our promise: The ParAccel Analytic Database™ will be faster than your current database management system or any that you are currently evaluating, or our software license is free (Maintenance is not included. Requires an executed evaluation agreement.)

To be consistent, should that not make the promise that the ParAccel offering would provide better price-performance and lower TCO than the current system or the one being evaluated? After all, that is what customers really care about.

I’m confused. More coffee!

Oh, there’s more! Check out this link http://www.paraccel.com/cash_for_clunkers.php

Talk about fine print:

* Trade-in value is equivalent to the first year free of a three year subscription contract based on an annual subscription rate of $15K/user terabyte of data. Servers are purchased separately.

ParAccel TPC-H 30TB results challenged!

Watch the feeding frenzy now that ParAccel’s TPC-H 30TB results have been challenged.

Before I had my morning cup of coffee, I found an email message with the subject “ParAccel ADVISORY” sitting in my mail box. Now, I’m not exactly sure why I got this message from Renee Deger of GlobalFluency so my first suspicion was that this was a scam and that someone in Nigeria would be giving me a million dollars if I did something.

But, I was disappointed. Renee Deger is not a Nigerian bank tycoon who will make me rich. In fact, ParAccel’s own blog indicates that their 30TB results have been challenged.

We wanted you to hear it from us first.  Our TPC-H Benchmark for performance and price-performance at the 30-terabyte scale was taken down following a challenge by one of our competitors and a review by the TPC.  We executed this benchmark in collaboration with Sun and under the watch of a highly qualified and experienced auditor.   Although it has been around for many years, the TPC-H specification is still subject to interpretation, and our interpretation of some of the points raised was not accepted by the TPC review board.

None of these items impacts our actual performance, which is still the best in the industry.  We will rerun the benchmark at our earliest opportunity according to the interpretations set forth by the TPC review board. We remain committed to the organization, as outlined in a blog post by Barry Zane, our CTO, here: http://paraccel.com/data_warehouse_blog/?p=74#more-74.

Please see the company blog for a post by David Steinhoff in the office of the CTO for further info: http://paraccel.com/data_warehouse_blog/?p=104#more-104

I read David Steinhoff’s blog as well. He writes

This last week, our June 2009 30TB results were challenged and found to be in violation of certain TPC rules. Accordingly, our results will soon be taken down from the TPC website.

We published our results in good faith. We used our standard, customer available database to run the benchmark (we wanted the benchmark to reflect the incredible performance our customers receive). However, the body of TPC rules is complex and undergoes constant interpretation; we are still relatively new to the benchmark game and are still learning, and we made some mistakes.

While we cannot get into the details of the challenges to our results (TPC proceedings are confidential and we would be in violation if we did), we can state with confidence that our query performance was in no way enhanced by the items that were challenged.

We can also say with confidence that we will publish again, soon.

Now, some competitor or competitor loyalist may try to make more of this than there is … we all know there is the risk of tabloid frenzy around any adversity in a society with free speech … and we wouldn’t have it any other way.

It is unfortunate that the proceedings are confidential and cannot be shared. I hope you republish your results at 30TB.

Contrary to some a long list of pundits, I believe that these benchmarks have an important place in the marketing of a product and its capabilities.

I reiterate what I said in a previous blog post

ParAccel’s solution is based on high-performance trilithium crystals. (Note: I don’t know why this wasn’t disclosed in the full disclosure report).

I hope the challenge was not about the trilithium crystals and the fact that you didn’t disclose it in the full disclosure report.

Wow, the TPC-H speculation continues!

The real technology behind the ParAccel TPC-H results are revealed here!

It is definitely interesting to see that the ParAccel TPC-H result saga is not yet done. Posts by Daniel Abadi (interesting analysis but it seems simplistic at first blush) and reflections on Curt Monash’s blog are proving to be amusing, to say the least.

At the end of the day, whether ParAccel is a “true column store” (or a “vertically partitioned row store”), and whether it has too much disk capacity and disk bandwidth strike me as somewhat academic arguments that don’t recognize some basic facts.

  • ParAccel’s system (bloated, oversized, undersized, truly columnar, OR NOT) is only the second system to post 30TB results.
  • That system costs less than half of the price of the other set of published results. Since I said “basic facts”, I will point out that the other entry may provide higher resiliency and that may be a part of the higher price. I have not researched whether the additional price is justified, related to the higher resiliency, etc., I want to make the point that there is a difference in the stated resiliency of the two solutions that is not apparent from the performance claims.

It might just be my sheltered upbringing but:

  • I know of few customers who purchase cars solely for the manufacturers advertised MPG and similarly, I know of no one who purchases a data-warehouse from a specific vendor because of the published TPC-H results.
  • I know of few customers who will choose to make a purchase decision based on whether a car has a inline engine, a V-engine or a rotary engine and similarly, I know of no one who makes a buying decision on a data warehouse based on whether the technology is “truly columnar”, “vertically partitioned row store”, “row store with all indexes” or some other esoteric collection of interesting sounding words?

So, I ask you, why are so many people so stewed about ParAccel’s TPC-H numbers? In a few more days, will we have more posts about ParAccel’s TPC-H numbers than we will about whether Michael Jackson really died or whether this is all part of a “comeback tour”?

I can’t answer either of the questions above for sure but what I  do know is this, ParAccel is getting some great publicity out of this whole thing.

And, I have it on good authority (it came to me in a dream last night) that ParAccel’s solution is based on high-performance trilithium crystals. (Note: I don’t know why this wasn’t disclosed in the full disclosure report). I hear that they chose 43 nodes because someone misremembered the “universal answer” from The Hitchhikers Guide to the Galaxy. By the time someone realized this, it was too late because the data load had begun. Remember you read it here first 🙂

Give it a rest folks!

P.S. Within minutes of posting this a well known heckler sent me email with the following explanation that confirms my hypothesis.

When a beam of matter and antimatter collide in dilithium we get a plasma field that powers warp drives within the “Sun” workstations. The warp drives that ParAccel uses are the Q-Warp variant which allow queries to run faster than the speed of light. A patent has been filed for this technique, don’t mention it in your blog please.

Not so fast, maybe relational databases aren’t dead!

Maybe the obituary announcing the demise of the relational database was premature!

Much has been written recently about the demise (or in some cases, the impending demise) of the relational database. “Relational databases are dead” writes Savio Rodrigues on July 2nd, I guess I missed the announcement and the funeral in the flood of emails and twitters about another high profile demise.

Some days ago, Michael Stonebraker authored an article with the title, “The End of a DBMS Era (Might be Upon Us)”. In September 2007 he made a similar argument in this article, and also in this 2005 paper with Uğur Çetintemel.

What Michael says here is absolutely true. And, in reality, Savio’s article just has a catchy title (and it worked). The body of the article makes a valid argument that there are some situations where the current “one size fits all” relational database offering that was born in the OLTP days may not be adequate for all data management problems.

So, let’s be perfectly clear about this; the issue isn’t that relational databases are dead. It is that a variety of use use cases are pushing the current relational database offerings to their limits.

I must emphasize that I consider relational databases (RDBMS’s) to be those systems that use a relational model (a definition consistent with http://en.wikipedia.org/wiki/Relational_database). As a result, columnar (or vertical) representations, row (or horizontal) representations, systems with hardware acceleration (FPGA’s, …) are all relational databases. There is arguably some confusion in terminology in the rest of this post, especially where I quote others who tend to use the term “Relational Database” more narrowly, so as to create a perception of differentiation between their product (columnar, analytic, …) and the conventional row oriented database which they refer to as an RDBMS.

Tony Bain begins his three part series about the problem with relational databases with an introduction where he says

“The specialist solutions have be slowly cropping up over the last 5 years and now today it wouldn’t be that unusual for an organization to choose a specialist data analytics database platform (such as those offered from Netezza, Greenplum, Vertica, Aster Data or Kickfire) over a generic database platform offered by IBM, Microsoft, Oracle or Sun for housing data for high end analytics.”

While I have some issues with his characterization of “specialist analytic database platforms” as something other than a Relational Database, I assume that he is using the term RDBMS to refer to the commonly available (general purpose) databases that are most often seen in OLTP environments.

I believe that whether you refer to a column oriented architecture (with or without compression), an architecture that uses hardware acceleration (Kickfire, Netezza, …) or a materialized view, you are attempting to address the same underlying issue; I/O is costly and performance is significantly improved when you reduce the I/O cost. Columnar representations significantly reduce I/O cost by not performing DMA on unnecessary columns of data. FPGA’s in Netezza serve a similar purpose; (among other things) they perform projections thereby reducing the amount of data that is DMA’ed. A materialized view with only the required columns (narrow table, thin table) serves the same purpose. In a similar manner (but for different reasons), indexes improve performance by quickly identifying the tuples that need to be DMA’ed.

Notice that all of these solutions fundamentally address one aspect of the problem; how to reduce the cost of I/O. The challenges that are facing databases these days are somewhat different. In addition to huge amounts of data that are being amassed (The Richard Winter article on the subject) there is a much broader variety of things that are being demanded of the repository of that information. For example, there is the “Search” model that has been discussed in a variety of contexts (web, peptide/nucleotide), the stream processing and data warehousing cases that have also received a fair amount of discussion.

Unlike the problem of I/O cost, many of these problems reflect issues with the fundamental structure and semantics of the SQL language. Some of these issues can be addressed with language extensions, User Defined Functions, MapReduce extensions and the like. But none of these address the underlying issue that the language and semantics were defined for a class of problems that we today come to classify as the “OLTP use case”.

Relational databases are not dead; on the contrary with the huge amounts of information that are being handled, they are more alive than ever before. The SQL language is not dead but it is in need of some improvements. That’s not something new; we’ve seen those in ’92, ’99, … But, more importantly the reason why the Relational Database and SQL have survived this long is because it is widely used and portable. By being an extensible and descriptive language, it has managed to adapt to many of the new requirements that were placed on it.

And if the current problems are significant, two more problems are just around the problem and waiting to rear their ugly heads. The first is the widespread adoption of the virtualization and the abstraction of computing resources. In addition to making it much hardware to adopt solutions with custom hardware (that cannot be virtualized), it introduces a level of unpredictability in I/O bandwidth, latency and performance. Right along with this, users are going to want the database to live on the cloud. With that will come all the requirements of scalability, ease of use and deployment that one associates with a cloud based offering (not just the deployment model). The second is the fact that users will expect one “solution” to meet a wide variety of demands including the current OLTP and reporting through the real time alerting that today’s “Google/Facebook/Twitter Generation” has come to demand (look-ma-no-silos).

These problems are going to drive a round of innovation, and the NoSQL trend is a good and healthy trend. In the same description of all the NoSQL and analytics alternatives, one should also mention the various vendors who are working on CEP solutions. As a result of all of these efforts, Relational Databases as we know them today (general purpose OLTP optimized, small data volume systems) will evolve into systems capable of managing huge volumes of data in a distributed/cloud/virtualized environment and capable of meeting a broad variety of consumer demands.

The current architectures that we know of (shared disk, shared nothing, shared memory) will need to be reconsidered in a virtualized environment. The architectures of our current databases will also need some changes to address the wide variety of consumer demands. Current optimization techniques will need to be adapted and the underlying data representations will have to change. But, in the end, I believe that the thing that will decide the success or failure of a technology in this area is the extent of compatibility and integration with the existing SQL language. If the system has a whole new set of semantics and is fundamentally incompatible with SQL I believe that adoption will slow. A system that extends SQL and meets these new requirements will do much better.

Relational Databases aren’t dead; the model of “one-size-fits-all” is certainly on shaky ground! There is a convergence between the virtualization/cloud paradigms, the cost and convenience advantages of managing large infrastructures in that model and the business need for large databases.

Fasten your seat-belts because the ride will be rough. But, it is a great time to be in the big-data-management field!