Analytic Database Market 'Fly Over'

This is a follow up to my previous post where I laid out my initial thoughts about ParStream. This is a very high level 'fly over' view of the analytic database market. I'll follow this up with some thoughts about how ParStream can position themselves in this market.

Powerhouse Vendors

The power players in the Analytic Database market are: Oracle (particularly Exadata), IBM (mostly Netezza, also DB2), and Teradata. Each of these vendors employs a large, very well funded and sophisticated sales force. A new vendor competing against them in accounts will find it very, very hard to win deals. They can easily put more people to work on a bid than a company like ParStream *employs*. If you are tendering for business in a Global 5000 corporation then you should expect to encounter them and you need a strategy for countering their access to the executive boards of these companies (which you will not get). In terms of technology their offerings have become very similar in recent years with all 3 emphasising MPP appliances of one kind or another, however most of the installed base are still using their traditional SMP offerings (Netezza and Teradata excepted).

New MPP niche players

There are a number of recent entrants to the market who also offer MPP technology, particularly: Greenplum, AsterData and ParAccel. All 3 offer software-only MPP databases, although Greenplum's emphasis has shifted slightly since being acquired by EMC. These vendors seem to focus mostly on (or succeed with) customers who have very large data volumes but are small companies in terms of employees. Many of these customers are in the web space. These vendors also have strong stories about supporting MapReduce/Hadoop inside their databases, which also plays to the leanings of web customers. According to testimonials on the vendor's websites customers seem to choose them because they are very fast and software only.

Microsoft

Microsoft is a unique case. They do not employ a direct sales force (as far as I know) however they have steadily become major force in enterprise software. Almost all companies run Windows desktops, have at least a few Windows servers and at least a few instances of SQL Server in production. Therefore Microsoft will be considered in virtually every selection process you're involved in. Microsoft have been steadily adding BI-DW features to the SQL Server product line and generally those features are all "free" with a SQL Server license. This doesn't necessarily make SQL Server cheaper but it does make it feel like very good value. Recent improvements include the Parallel Data Warehouse appliance (with HP hardware), columnar indexing for the next release and PowerPivot for local analysis of large data volumes.

Proprietary columnar

Columnar databases have been the hot technology in analytic databases for the last few years. The biggest vendors are Sybase with their very mature IQ product, SAND with an equally mature product and Vertica with their newer (and reportedly much faster) product. These databases can be used in single server (SMP / scale-up) and MPP (multi-server / scale-out) configurations. They appear to be most popular with customers who appreciate the high levels of compression that these databases offer and already have relatively mature star-schema / Kimball style data warehouses in place.  In my experience Sybase and SAND are used most in companies where they were introduced by an OEM as part of another product. Vertica is so new that it's not clear who their 'natural' customers are yet.

Open Source columnar

In the open source world there are 2 MySQL storage engines and a standalone product offering columnar databases. The MySQL engine Infobright was the first open source columnar database. It features very high compression and very fast loading however it is not suited for lots of joins and may be better thought of as a OLAP tool managed via SQL. The InfiniDB MySQL engine on the other hand is very good at joins and very good at squeezing all the available performance out of a server, however it does not have any compression currently. Finally there is LucidDB which is a Java based standalone product and has performance characteristics somewhere between the other two. LucidDB features excellent compression, index support and generally good performance but can be slow to load.


Vectorised columnar
There is only one player here: VectorWise. VectorWise is a columnar database (AFAIK) that has been architected from top to bottom to take advantage of the vector pipelines built into all recent CPUs. Vectorisation is a way of running many highly parallel operations through a single CPU. It basically removes all of the waiting and memory shifting that slows a CPU down. Initial testers have been very positive about the performance of VectorWise and had nothing but good things to say. There is also talk of an open source release so they are covering a lot of bases. They also have the advantage of being part of Ingres who may not be the force they once were but have a significant installed base and are well placed to sell VectorWise. They are the biggest direct competitor to ParStream that I can see right now.


Open Source MapReduce/NoSQL
ParStream will also compete with a new breed of open source MapReduce/NoSQL products, most notably Hadoop (and it's variants). These products are not databases per se but they have gained a lot of mindshare among developers who need to work with large data volumes. Part of their attraction is their 'cloud friendliness'. They are perfect for the cloud because they have been designed to run on many small servers and to expect that a single server could fail at any time. There is a trade-off to be made and MapReduce products tend to be much more complex to query, however for a technically savvy audience the trade is well worth it.

Next time I'll talk about where I think ParStream need to place themselves to maximise their opportunity.

UPDATE: Actually, in the next post I talk about how analytic database vendors are positioned and introduce a simple market segmentation. A further post about market opportunities will follow.

My take on why businesses have problems with ETL tools

Check out this very nice piece by Rick about the reasons why companies have failed to get the most out of their ETL tools.

My take is from the other side of the fence. As a business user I'm often frustated by ETL tools and have been known to campaign against them for the following reasons:

> ETL tools have been too focussed on Extract-Transform-Load and too little focused on actual data integration. I have complex integration challenges that are not necessarily a good fit for the ETL strategy and sometimes I feel like I'm pushing a square peg into a round hole.

> It's still very challenging to generate reusable logic inside ETL tools and this really should be the easiest thing in the world (ever heard the mantra Don't Repeat Yourself!). Often the hoops that have to be jumped through are more trouble than they are worth.

> Some ETL tools are a hodge podge of technologies and approaches with different data types and different syntaxes wherever you look. (SSIS I'm looking at you! This still is not being addressed in Denali.)

> ETL tools are too focused on their own execution engines and fail miserably to take advantage of the processing power of columnar and MPP databases by running processes on the database. This is understandable in open source tools (database specific SQL may be a bridge too far) but in commercial tools it's pathetic.

> Finally, where is the ETL equivalent of SQL? Why are we stuck with incompatible formats for each tool. The design graphs in each tool look very similar and the data they capture is near identical. Even the open source projects have failed to utilise a common format. Very poor show. This is the single biggest obstacle to more widespread ETL. Right now it's much easier for other parts of the stack to stick with SQL and pretend that ETL doesn't exist.

2011 Preview: BI-DW Top 5

Here are the trends I expect to see in 2011, but beware my crystal ball is hazy and known to be biased.


Top 5 for 2011


5) Niche BI acquisitions take off   
  Big BI consolidation may well be finished, but I think 2011 will be the start of niche vendor acquisitions as established BI vendors seek new growth in a (hopefully) recovering economy.  I don't expect any given deal size to be huge (probably sub $100m) however we could easily see half a dozen vendors being picked up.

  The driver for such acquisitions should be clear; Big BI vendors have ageing product stacks and many have been through post-merger product integration pains.  Their focus on innovation has been sorely lacking (non-existent?).  Also, there is huge leverage in applying a niche product to an existing portfolio.  The Business Objects / Xcelsius acquisition is a great example of this (although BO seems to think Xcelsius is a lot better and more useful than I do).

  I will not make any predictions about who might be acquired. However, here are some examples of companies with offerings that are not available from Big BI vendors.  Tableau's data visualisation offering is 1^st class IMHO and is a perfect fit for the people who actually use BI products in practice.  Lyza's BI/ETL collaboration offering is unique (and hard to describe) and a great fit for business oriented BI projects.  Jedox' Palo offering brings unique power to Excel power users and appears to be the only rival to Microsoft's PowerPivot offerings; I suspect a stronger US sales force would help them immensely.

4) GPU based computing comes to the fore
  I blogged some time ago about GPU's offering a glimpse of the many-core future.  Since then I've been waiting (and waiting) for signs that GPUs were making the jump into business servers.  Finally, in April 2010, Jedox released Palo OLAP Accelerator for GPUs. And this autumn I discovered ParStream's new GPU accelerated database (I blogged about it last week).  Finally in December we saw the announcement of a new class of Amazon EC2 instance featuring a GPU as part of the package.

  Based on these weak signals, I think 2011 will be the year that GPU processing and GPU acceleration starts to become a widely accepted part of business computing.  The most recent GPU cards from Nvidia and AMD offer many hundreds (512+) of processing cores and multiple cards can be used in a single server.  There is a large class of business computing problems that could be addressed by GPUs: analytic calculations (e.g. SAS / R), anything related to MapReduce / Hadoop, anything related to enterprise search / e-discovery, anything related to stream processing / CEP, etc.  As final note I would strongly suggest that vendors who sell columnar databases or in-memory BI products (or are losing sales to such) should point their R&D team at GPUs and get something together quickly. Niche vendors have an opportunity to push the price/perform baseline up by an order of magnitude and take market share while Big BI vendors try to catch up.

3) Data Warehousing morphs into Data Intensive Computing

  I once asked Netezza CTO Justin Lindsey if he considers Netezza machines to be supercomputers.  He said no he didn't but that the scientific computing 'guys' call it a "Data Intensive Supercomputer" and use it in applications where the ratio of data to calculations is very high, i.e., the opposite of classical supercomputing applications.  That phrase really stuck with me and it seems to describe the direction that data warehousing is headed.

  If you've been around BI-DW for a while you'll be familiar with the Inmon v Kimball ideology war. That fight illustrates the idea that data warehouses had a well defined purpose simply because we could argue about the right way to do 'it'.  I've noticed the purpose of the data warehouse stretching out over the last few years. The rise of analytics and ever increasing data volumes mean that more activities are finding a home on the data warehouse as a platform.  Either the activity cannot be done elsewhere or the data warehouse is the most accessible platform for data driven projects with short term data processing needs.

  In 2011 we need to borrow this term from the supercomputing guys and apply it to ourselves.  We need to change our thinking from delivering and supporting a data warehouse to offering a Data Intensive Computing service (that enables a data warehouse).  Those that fail to make the change should not be surprised when departments implement their own analytic database, make it available to the wider business and start competing with them for funding.

2) SharePoint destabilises incumbent BI platforms

  SharePoint is not typically considered a BI product and is rarely mentioned when I talk to fellow BI people. Those who specialise in Microsoft's products occasionally mention the special challenges (read headaches) associated with supporting it but it's "just a portal".  Right?  Not quite.  Microsoft has managed to drive a nuclear Trojan horse into the safety of incumbent BI installations.  SharePoint contains extensive BI capabilities and enables BI capabilities in other Microsoft products (like, um, Excel!).  Worst of all, if you're the incumbent BI vendor, SharePoint is everywhere!  It has something like 75% market share overall and effectively 100% market share in big companies.

  So what?  Well, when you want to deploy a dashboard solution where is the natural home for such content?  The intranet portal.  When you need to collaborate on analysis with widely dispersed teams, what can you use that's better than email?  Excel docs on the portal.  If report bursting is filling up your inboxes like sand in an hourglass, where can you put reports instead?  Maybe the intranet?  You get the point. We have a history in BI of pushing yet another friggin' portal onto the business when we select our BI platform.  Our chosen platform comes with such a nice portal, heck that's part of why we bought it. A year later we wonder why it doesn't get used.  We wonder why we spend more time unlocking expired logins than answering questions about reports.

   Right now businesses are only using a small fraction of SharePoint's capability. But they pay for all of them and I expect business to push for more return from SharePoint investments in 2011.  I expect a lot of these initiatives to involve communicating business performance (BI) and collaborating on performance analysis (BI again).  The trouble for incumbent vendors is clear: SharePoint has no substitute; your BI suite has direct substitutes, Microsoft offers some substitutes for free, your BI content is going to end up on SharePoint, once it's there its SharePoint content. BI vendors should expect hard conversation about maintenance fees and upgrade cycles in any account where dashboards are being hosted on SharePoint.

  As a final note, I would suggest that vendors who sell to large customers need to have a compelling SharePoint story.  It's basically a case of "if you can't beat them, join them".  If you have a portal as part of your suite you need to integrate with SharePoint (yesterday).  You need to make you products work better with SharePoint than Microsoft's own products do.  This will be a huge, expensive PITA - do it anyway.  You must find a way to embrace SharePoint without letting it own you.  Good luck. 

1) BI starts to dissolve into other systems

  My final trend for 2011 is about BI becoming bifurcated (love that word) between the strategic stuff (dashboards and analysis) and everything else. That "everything else" doesn't naturally live on a portal or in a report that gets emailed out.  It belongs in the system that generates the data in the first place; it belongs right at the point of interaction. James Taylor and Neil Raden talked about this idea in the book "Smart Enough Systems". I won't repeat their arguments here but I will outline some of the reason why I think it's happening now.

   First, 'greenfield' BI sites are a thing of the past. Everyone now has BI, it may not work very well but they have it.  New companies use BI from day 1.  The market is effectively saturated.  Second, most of the Big BI vendors are now part of large companies that sell line of business systems.  There is a natural concern about diluting the value of the BI suite, however "BI for the masses" is a dead-end and I think they probably get that.  Third, deep integration is one of the last remaining levers that Big BI vendors can use against nimble niche vendors and against SharePoint.  They will essentially have to go down this route at some point.  Finally, many system vendors have reached an impasse with their customers regarding upgrades. Customers are simply refusing to upgrade systems that work perfectly well. These vendors must create a real, tangible reason for the customers to move. I suspect that deep BI integration is their best bet.

  I have had too many conversations about 'completing the circle' and feeding the results of analysis back into source systems.  Sadly it never happens in practice, the walls are just too high.  Once the data has left the source system it is considered tainted and pushing tainted data into production systems is never taken lightly.  Thus the ultimate answer seems to be to push the "smarts" that have been generated by analysis down into the source system instead.  Expect to see plenty of marketing talk in 2011 about systems getting 'smarter' and more integrated.

2010 Review: a BI-DW Top 5

This post is written completely 'off the cuff' without any fact checking or referring back to sources. Just sayin'…

Top 5 from 2010

5) Big BI consolidation is finished
  There were no significant acquisitions of "Big BI" vendors in 2010.  Since Cognos went to IBM and BO went to SAP, the last remaining member of the old guard is MicroStrategy. (It's interesting to consider why they have not been acquired but that's for another post.)  In many ways the very definition of Big BI has shifted to encompass smaller players. Analysts, in particular, need things to talk about and they have effectively elevated a few companies to Big BI status that were previously somewhat ignored, e.g., SAS (as a BI provider), InformationBuilders, Pentaho, Acuate, etc.  All of the major conglomerates now have a 'serious' BI element in their offerings and so I don't see further big spending on BI acquisitions in 2011.  The only dark horse in this race seems to be HP and it's very unclear what their intentions are, particularly with the rumours of Neoview being cancelled; if HP were to move I see them going for either a few niche players or someone like InformationBuilders with solid software but lacking in name recognition.

4) Analytic database consolidation began
  We've seen an explosion of specialist Analytic databases over the last ~5 years and 2010 saw the start of a consolidation phase amongst these players. The first big acquisition of 2010 was Sybase by SAP; everyone assumed Sybase's IQ product (the original columnar database) was the target but the talk since then has been largely about the Sybase mobile offerings. I suspect both products are of interest to SAP; IQ allows them to move some of their ageing product lines forward and Mobile will be an enabler for taking both SAP and Business Objects to smartphones going forward.
  The banner acquisition was Netezza by IBM. I've long been very critical/sceptical of IBM's claims in the Data Warehouse / Analytic space. Particularly as I've worked with a number of DW's that were taken off DB2 (onto Teradata) but never come across one actively running on DB2. I'm a big Netezza fan so my hope is that they survive the integration and are able to leverage the resources of IBM going forward.
  We also saw Teradata acquiring the dry husk of Kickfire's ill-fated MySQL 'DW appliance'. Kickfire's fundamental technology appeared to be quite good but sadly their market strategy was quite bad. I think this a good sign from Teradata that they are open to external ideas and they see where the market is going. The competition with Netezza seems to have revitalised them and given them a new enemy to focus on. A new version of Teradata database that incorporated some columnar features (and an 'free' performance boost) could be just the ticket to get their very conservative customers migrated onto the latest version.

3) BI vendors started thinking about mobile
  Mobile BI became a 'front of mind' issue in 2010. MicroStrategy has marketed aggressively in this space but other vendors are in the hunt and have more or less complete mobile offerings. Business Objects also made some big noise about mobile but everything seemed to be demos and prototypes. Cognos has had a 'mobile' offering for some time but they remained strangely quiet, my impression is that their mobile offerings are not designed for the iOS/Android touchscreen world.
  Niche vendors have been somewhat quiet on the mobile front, possibly waiting to see how it plays out before investing, with the notable exception of Qlikview who have embraced it with both arms. This is a great strategic move for Qlikview (who IMHO prove the koan that 'strategy trumps product') because newer mobile platforms are being embraced by their mid-market customers far faster than at Global 5000 companies that the Big BI vendors focus on. Other niche and mid-market vendors should take note of this move and get something (anything!) ready as quickly as possible.

2) Hadoop became the one true MapReduce
  I remain somewhat non-plussed by MapReduce personally, however a lot of attention has been lavished on it over the last 2 years and during the course of 2010 the industry has settled on Hadoop as the MapReduce of choice.  From Daniel Adabadi's HadoopDB project to Pentaho's extensive Hadoop integration to Aster's "seamless connectivity" with Hadoop to Paraccel's announcement of the same thing coming soon and on and on.  The basic story of MapReduce was very sexy but in practice the details turned out to be "a bit more complicated" (as Ben Goldacre [read his book!] would say).  It's not clear that Hadoop is the best possible MR implementation but it looks likely to become the SQL of MapReduce. Expect other MapReduce implementations to start talking about Hadoop compatibility ad nauseum.
  All of this casts Cloudera in an interesting light. They are after all "the Hadoop company" according to themselves. It's far too early for a 'good' acquisition in this space however money talks and I wonder if we might see something happen in 2011.

1) The Cloud got real and we all got sick of hearing about it
  I'm not sure whether 2010 was truly the "year of the Cloud" but it certainly was the peak of it's hype cycle.  In 2010 the reality of cloud pricing hit home; the short version is that a lot of the fundamental cost of cloud computing is operational and we shouldn't expect to see continuous price/performance gains like we have seen in the hardware world.  Savvy observers have noted that the bulk of enterprise IT spending has been non-hardware for a long time but the existence of cloud offerings brings those costs into focus.
  Ultimately, my hope for the Cloud is that it will drive companies toward buying results, e.g., SaaS services that require little-to-no customisation, and away from buying potential, e.g. faster hardware and COTS software that is rarely fit for purpose. The cycle should go something like: "This Cloud stuff seems expensive, how much does it cost us to do the same thing?" > "OMG are you frickin' serious, we really spend that?!" > "Is there anyone out there that can provide the exact same thing for a monthly fee?".  Honestly, big companies are incredibly bad at hardware and even worse at software. The Cloud (as provided by Amazon, et al) is IMHO just a half step towards then endpoint which is the use of SaaS offerings for everything.

Initial thoughts about ParStream

So here are my thoughts about ParStream based on researching their product on the internet only. I have not used the product, so I am simply assuming it lives up to all claims. As an analytics user and a BI-DW practitioner I sincerely hope that ParStream succeeds.

I'm a GPU believer
I'm a long time believer in the importance of utilising GPU for challenging database problems. I wrote a post in July 2009 about using GPUs for databases and implored database vendors to move in that direction: "Why GPUs matter for DW/BI" (http://joeharris76.blogspot.com/2009/07/why-gpus-matter-for-dwbi.html).  Here's the key quote - "There's a new world coming. It has a lot of cores. It will require new approaches. That world is accessible today through GPUs. Database vendors who move in this direction now will gain market share and momentum. Those who think they can wait on Intel and 'traditional' CPUs to 'catch up' may live to regret it."

On the right track
I think ParStream is *fundamentally* on the right track with a GPU accelerated analytic database. The ParStream presentation from Mike Hummel (http://www.youtube.com/watch?v=knicXkXd9hQ) talks about a query that took 12 minutes on Oracle taking just a few *miliseconds* on ParStream. If that is even half right the potential to shake up the industry and radically raise the bar on database performance is very exciting.

Reminiscent of Netezza
I remember the first time I used Netezza back in 2004. I had just taken a new role and my new company had recently installed a first generation Netezza appliance. In my previous job we had an Oracle data warehouse that was updated *weekly* and contained roughly 100 million rows. Queries commonly took *hours* to return. The Netezza machine held just less than 1 *billion* rows. I ran the following query: "SELECT month,  COUNT(*), SUM(call_value) FROM cdr GROUP BY month;". It came back in 15 seconds! I was literally blown away.

A fast database changes the game
When you have a very fast analytic databases it totally changes the game. You can ask more questions, ask more complex questions and ask them more often. Analytics requires a lot of trial and error and removing time spent waiting on the database enables a new spectrum of possibilities. For example, Netezza enabled me to reprice _every_ call in our database against _every_ one of our competitors tariffs (i.e. an 'explosive' operation: 50 mil records in => 800 mil records out) and then calculate the best *possible* price for each customer on any tariff. I used that information to benchmark my company on "value for money" and to understand the hidden drivers for customer churn.

ParStream appliance strategy:
So, given that background, let's look at the positioning of ParStream, the potential problems they may face, and the opportunities they need to pursue.

ParStream is not Netezza
I've positively compared ParStream to Netezza above so you might expect me to applaud ParStream for offering an appliance. Sadly not; Netezza's appliance success was due to unique factors that ParStream cannot replicate. Netezza had to use custom hardware because they use a custom FPGA chip. Customers were (and are) nervous about investing heavily in such hardware, however Netezza goes to great lengths to reassure them; providing service guarantees, plenty of spare parts and using commodity components wherever possible (power supplies, disks, host server, etc.). Also we must remember that most customers looking at Netezza were using very large servers (or server clusters) and required *very many* disks to get reasonable I/O performance for their databases. Netezza was actually reducing complexity for those customers.

The world has changed going into 2011
ParStream cannot replicate those market conditions. The world has changed considerably going into 2011 and different factors need to be emphasised. ParStream relies on Nvidia GPUs that are widely available and installed on commodity interconnects (e.g. PCIe). Moreover there are high quality server offerings available in 2 form factors that make the appliance strategy more of a liability than an asset. First, Nvidia (and others) sell 1U rack mounted 'server' that contain 4 GPUs and connect to 'host' server via a PCIe card. Second Supermicro (and others) sell 4U 'super' servers that contain 2 Intel Xeons and  4 GPUs in a pre-integrated package. The ParStream appliance may well be superior to these offerings in some key way however such advantages will be quickly wiped by out as the server manufactures continuously refresh their product line.

Focus on the database software business
ParStream should focus on the database software business where they have a huge advantage not the server business where they have huge disadvantages. You should read this article if you have any further doubts: "The Power of Commodity Hardware" (http://www.svadventure.com/svadventure/2009/01/the-power-of-commodity-hardware.html). Key quotes: "Customers love commodity hardware.", "Competing with HP, IBM, and Dell is dumb.", "Commodity hardware is much more capital efficient".  Also consider the fates of Kickfire and Dataupia who floundered on a database appliance strategy, and ParAccel who is going strong after initially offering an appliance and quickly moving to emphasise software-only.

Position GPUs as a new commodity
ParStream must position GPUs and GPU acceleration as a new commodity. Explain that GPUs are an essential part of all serious supercomputers and the technology is being embraced by everyone; Intel with Larabee, AMD with Fusion, etc. Emphasise the option to add 'commodity' 4 GPU pizza boxes servers alongside a customer's existing Xeon/Opteron servers and, using ParStream, make huge performance gains. Talk to Dell customers about using a single Dell PowerEdge C410x GPU chasis (http://www.dell.com/us/en/enterprise/servers/poweredge-c410x/pd.aspx) to accelerate an entire rack of "standard" servers running ParStream. The message must be clear: ParStream runs on commodity hardware; you may not have purchased GPU hardware before but you can get exactly what ParStream needs from your preferred vendor.

One final point here; ParStream needs to make Windows support a priority. This is probably not going to be fun, technically speaking, but Windows support will be important for the markets that ParStream should target (which will have to be another post, sadly).

UPDATE - I followed this post up with:
An overview of the analytic database market, a simple segmentation of the main analytic database vendors, and a summary of the key opportunities I see in the analytic databases market (esp. for ParStream and RainStor)

Comment regarding Infobright's performance problems

UPDATE: This is a classic case of the comments being better than the post; make sure you read them! In summary, Jeff explained better and a lightbulb went off for me: Infobright is for OLAP in the classical sense with the huge advantage of being managed with a SQL interface. Cool.

I made a comment over on Tom Barber's blog post about a Columnar DB benchmarking exercise: http://pentahomusings.blogspot.com/2010/12/my-very-dodgy-col-store-database.html


Jeff Kibler said...
Tom –

Thanks for diving in! As indicated in your results, I believe your tests cater well to databases designed for star-schemas and full table-scan queries. Because a few of the benchmarked databases are engineered specifically for table scans, I would anticipate their lower query execution time. However, in analytics, companies overwhelmingly use aggregates, especially in ad-hoc fashion. Plus, they often go much higher than 90 gigs.

That said, Infobright caters to the full fledged analytic. As needed by the standard ad-hoc analytic query, Infobright uses software intelligence to drastically reduce the required query I/O. With denormalization and a larger data set, Infobright will show its dominance.

Cheers,

Jeff
Infobright Community Manager
8 December 2010 17:04


Joe Harris said...
Tom,

Awesome work, this is the first benchmark I've seen for VectorWise and it does look very good. Although, I'm actually surprised how close InfiniDB and LucidDB are, based on all the VW hype.

NFS on Dell Equilogic though? I always cringe when I see a database living on a SAN. So much potential for trouble (and really, really slow I/O).


Jeff,

I have to say that your comment is off base. I'm glad that Infobright has a community manager who's speaking for them but this comment is *not* helping.

First, your statement that "in analytics, companies overwhelmingly use aggregates" is plain wrong. We use aggregates as a fallback when absolutely necessary. Aggregates are a maintenance nightmare and introduce a huge "average of an average" issue that is difficult to work around. I'm sure I remember reading some Infobright PR about removing the need for aggregate tables.

Second, you guys have a very real performance problem with certain types of queries that should be straightforward. Just looking at it prima facie it seems that Infobright starts to struggle as soon as we introduce multiple joins and string or range predicates. The irony of the poor Infobright performance is that your compression is so good that the data could *almost* fit in RAM.

What I'd like to see from Infobright is: 1) a recognition of the issue as being real. 2) An explanation of why Infobright is not as fast in these circumstances. 3) An explanation of how to rewrite the queries to get better performance (if possible). 4) A statement about how Infobright is going to address the issues and when.

I like Infobright; I like MySQL; I'm an open source fan; I want you to succeed. The Star Schema Benchmark is not going away, Infobright needs to have a better response to it.

Joe