• Performance and bottleneck cleanup.
• “Omigod, you mean you didn’t have that feature before?” cleanup.

That said, the traditional release for cleaning up the last huge gaps in an analytic DBMS product seems have become 4.0; recent examples include Aster Data, Vertica and Greenplum. Infobright seems on track to be another example of that rule.

Ack. Now that I’ve said that, other vendors are going to be tempted to accelerate their numbering so as to reach the 4.0 mark sooner …

A lot of Infobright performance enhancements are in the vein “We used to rely on generic MySQL for that, but now we do it ourselves, and it works a lot better.” Examples include: 

• Infobright now does DELETEs all at once, vs. the previous row-by-row way. This makes DELETE performance similar to SELECT performance, when previously there was a big difference.
• Ditto, if I understood correctly, INSERTs and UPDATEs.
• Each release, Infobright covers more SQL functionality itself and passes less through to the generic MySQL engine.
• UTF-8 Unicode data can now be loaded via Infobright’s parallel loader. Previously, you had to use MySQL’s load.

Infobright has also added workload management in 3.4, and this is intertwined with multicore parallelization, apparently because the workload manager decides when a query should use multiple cores to execute. Infobright further says that multi-user INSERT performance has increased a lot more than single-user, but I have forgotten why that is.

Infobright now streams data back to the client faster. E.g., unless there’s some good reason not to, partial query results are pipelined back as they become available.* Finally, loading data no longer locks tables from being read (in my notes I wasn’t sure whether that was a current or future feature, but Infobright’s marketing seems to indicate it’s current). For some reason, Infobright is positioning this as a major, innovative feature.

*A good reason not to do this would be an aggregate that requires full materialization of the table before Infobright can carry it out.

]]> http://www.dbms2.com/2010/06/27/infobright-release-3-4/feed/ 6 http://www.dbms2.com/2010/06/05/algebraix/ http://www.dbms2.com/2010/06/05/algebraix/#comments Sat, 05 Jun 2010 08:54:59 +0000 Curt Monash http://www.dbms2.com/?p=2227 I talked Friday with Chris Piedemonte and Gary Sherman, respectively the Cofounder/CTO and Chief Mathematician of Algebraix, who hooked up together for this project back in 2003 or 2004. (Algebraix is the company formerly known as XSPRADA.) Algebraix makes an analytic DBMS, somewhat based on the ideas of extended set theory, that runs on SMP (Symmetric MultiProcessing) boxes. Like all analytic DBMS vendors, Algebraix has on some occasions run some queries orders of magnitude faster than they ran on the systems users were looking to replace.

Algebraix’s secret sauce is that the DBMS keeps reorganizing and recopying the data on disk, to optimize performance in response to expected query patterns (automatically inferred from queries it’s seen so far). This sounds a lot like the Infobright story, with some of the more obvious differences being: 

• Infobright has fixed data structures, with what serve for indexes and precomputed aggregates added on the fly. Algebraix apparently reorganizes everything, including data partitions. (I also presume that Algebraix’s indexes and aggregates look rather different than Infobright’s data packs.)
• Infobright compresses data aggressively. Algebraix doesn’t yet compress.
• Infobright’s product is presumably much more mature.

So far as I can tell, that’s about it. Experience teaches that when a small company claims that some big mathematical breakthrough lets it have huge product superiority in DBMS or analytic tools, the claim doesn’t pan out. Maybe the company has good technology somewhat inspired by the mathematics, but the more breathless claims are always overwrought. Examples include Infobright (rough set theory), CrossZ/QueryObject (fractals), and KXEN (support vector machines). Algebraix (extended set theory) shows every sign of being another such case. An extended discussion about whether or not join operations are commutative – without benefit of any examples of their supposed non-commutativity – did little to convince me otherwise.

Finally, as per email from diligent PR guy David King,

• Algebraix has 22 employees in Austin and at its HQ in San Diego.
• Algebraix’s biggest customer is the Department of Defense (through BAE Systems).
• Algebraix’s product just became commercially available on March 1.
• Algebraix has completed one round of funding for $11 million.
• Algebraix is 100% angel-backed.
• Algebraix’s target market is “any mid-large sized organization that needs rapid access to analytics on large volumes of data.”

Highlights on the market share/sector side include:

• Infobright’s customer base grew 500% over the past year, to 120 paying customers.
• This included end users (60%), as well as ISVs and SaaS providers (40%) who embed Infobright’s DBMS in their application.
• During the same period, Infobright’s open source software was downloaded 35,000 times.
• The end user applications were heavily clustered around web and online analytics tracking, with a focus on understanding customer behavior on the web.
• Infobright also continues to see the growth of application-specific data marts.
• There is also continued interest and growth in using Infobright technology to analyze IT logs and telecom CDR (Call Detail Record) data, to identify fraud or security issues, to understand and improve network performance, and other purposes.

Product highlights include:

• Infobright be much more transparent in 2010 about its plans.
• Infobright will start posting and commenting on future releases and themes in March of this year. (However, they haven’t run much of that by me yet, and we’re past the middle of March.)
• Infobright expects to drop 3-4 interim releases for every major release, with at least two major releases in 2010.
• Some of Infobright’s major improvements this year will be:
  • Continued SMP performance improvements “without the need for complex hardware configurations or administrative effort”.
  • Extending the “hit rate” of the Knowledge Grid, which is central to Infobright’s performance story.
  • Better international support with UTF-8 extensions.

• Personally selected annual lists of 12 “Most influential” companies and 36 “Companies to watch” in analytics- and database-related sectors.
• Made it clear that these are his personal selections.
• Nonetheless has called it an Editors’ Choice list, rather than Editor’s Choice.

(Actually, he’s really called it an “award.”)

People advising Doug — who come to think of it actually are Contributing Editors to Intelligent Enterprise or something like that — included Cindi Howson, Seth Grimes, three others, and me.

And if past is prologue, I will now get a flood of PR emails calling my attention to this award that I already have both participated in and blogged about.

As usual, the sense:nonsense ratio on these lists was pleasingly high. Analytic DBMS vendors cited included IBM, Microsoft, Netezza, Oracle, Sybase, and Teradata in the “Most influential” group, with Aster, Greenplum, HP, Infobright, and Vertica among the “To watch” crowd. It’s tough to argue with those selections, whose most questionable element is probably the not-ridiculous supposition that HP could do something interesting over the coming year. Cloudera and Intersystems also made the list, deservedly.

All three of QlikTech, Tableau, and TIBCO made the list, which is appropriate given the potential for and interest in interactive data exploration technology.  The BI majors, independent or otherwise, were all on as well. In text mining, Doug included Attensity and Clarabridge, which I think is exactly right. (Plus OpenCalais.)  Upon reflection, I probably should have nominated Mark Logic, even though most of its business is non-enterprise; but hey, nobody’s perfect, and the same goes for lists. Open source was well represented, with Apache, Actuate, Jaspersoft, Eclipse, Infobright, Nuxeo and R all being cited (but not Ingres or Pentaho). Kalido made the list, with my endorsement, their silly I-CASE like marketing messaging notwithstanding.

Speaking of imperfections — there only are a few category names, and so category assignments can be pretty bizarre. (In an ideal world, middleware wouldn’t be included under “enterprise applications”.) Greenplum hasn’t really “extended” its DBMS with a “cloud” option. As much as I’d like Netezza to be more influential than SAP, that’s probably not the best way to rank them. And there are a number of “This company is on a roll!” kinds of comments that I wouldn’t necessarily endorse.

But those are all nitpicks. On the whole, it’s another nice job.

*Links to Gartner Magic Quadrants commonly break, but that one worked at the time of this posting.

• Gartner thinks that data warehouse appliances are on the rise, due to their simplicity.
• Gartner correctly says that Teradata has been a data warehouse appliance vendor from the getgo.
• Gartner characterizes IBM as being an appliance vendor as well.
• Gartner suggests that HP is having trouble living up to its technical promises for Neoview.
• Gartner further suggests — no surprise here — that HP Neoview has had very few new customers past its initial wave.
• Gartner notes IBM’s difficulties in selling data warehouse installations of DB2, despite what on paper is great-sounding technology.
• Gartner says — also no surprise — that illuminate “has seen little success in North America since opening its first office in the U.S. over two years ago.”
• Ingres has evidently gotten a few BI-centric “appliance” deals, e.g. with Jaspersoft. But basically Ingres isn’t doing well in data warehousing.
• Gartner does say Ingres has “the strongest open-source DBMS offering for data warehousing.” Being very literal about “open source,” that’s a defensible claim — but it’s pretty irrelevant in a world where Greenplum Single-Node Edition can be had for free. It also waves away all the data mart use cases in which Infobright Community Edition shines.
• Gartner says that Netezza is working out as a “complex workload” enterprise data warehouse provider, according to reference checks, in addition to its established success in data mart scenarios.
• Gartner says Oracle’s offering has finally become “accepted” in the market for databases >50 TB. I guess I can live with that fairly weak claim, but I wouldn’t go much further than that.
• Gartner asserts that, unlike software-only Oracle, Oracle Exadata isn’t significantly harder to administer than “other mixed OLTP/OLAP DBMS vendors,” because Exadata is fast enough you don’t need to jump through all those hoops any more to get tolerable performance. The money quote is “one reference reported reducing the number of indexes by a factor of 100 to fewer than five.” Note, however, that Gartner does not seem to assert that Exadata’s ease of use rivals that of the newer analytic DBMS specialists.
• Gartner confirms Oracle’s reluctance to do onsite Exadata POCs, but says it is not absolute. This is roughly compatible with what I’m hearing elsewhere, and indeed with Oracle own claims to be ramping up availability of Exadata POC hardware.
• Gartner’s criteria for inclusion include at least 10 different organizations having a product “in production.” Thus, the big surprise was ParAccel being included. The money quote there is “With approximately 20 customers in the pharmaceutical, retail, financial and media/advertising analytics sectors, ParAccel has a good reference base.” That assessment is difficult to reconcile with other information, but I’ve been told Gartner is sticking to its guns. That assessment would be even harder to believe if those 20 references were all alleged to be true production customers.
• Gartner notes that you basically can’t run a 1 TB+ MySQL data warehouse without sharding. (Of course, Infobright has an alternative, and up to a small number of terabytes so does Kickfire.)
• Gartner reports that at least some customers are pleased with Sybase IQ’s mixed workload/enterprise data warehouse capabilities.
• Gartner correctly notes that Oracle Exadata is a price-competition challenge for Teradata.
• Gartner notes that 20% of Vertica’s customers are outside the US. While not shocking, that’s more than I realized.
• Gartner notes something I don’t think I’ve posted yet, which is that Vertica has a customer with 300 TB of data. (The identity is a deep dark secret, but if I told you you probably wouldn’t recognize the name anyway.)

As does any such piece, the Gartner Data Warehouse DBMS Magic Quadrant also has outright errors.  For example:

• Aster Data isn’t really “the newest entrant to the DBMS data warehouse world.”
• Aster’s SQL/MapReduce was not new in Release 4.0.
• Greenplum isn’t yet pushing down code to the storage tier.
• I’m not sure what kind of database-tier parallelism Gartner is claiming is new in Oracle in 11g Release 2 — but I doubt it’s really new. Rather, what Oracle has done recently is make parallelism less administratively cumbersome.
• Vertica wasn’t really the first DBMS in the cloud. At most it was the first pure-play analytic DBMS to get there.

Robert Hodges of Continuent offers a great outline of Continuent’s clustering story, with a lot of “Now we got right what we previously didn’t know/admit we got wrong.” Continuent now claims to have a strong clustering offering, both paid and free/open-source, for both MySQL and PostgreSQL, with Oracle support perhaps coming really soon.

Merv Adrian, who has overrated the importance of TPC benchmarks in the past, seems to have become more skeptical.

Interim CEO Mark Burton laid out Infobright’s focus pretty clearly when he took over:

… the focus must be in building products that fit market segments where ease-of-use and easily attainable performance are valued.  This doesn’t sound like the high end of Data Warehousing to me where highly complex MPP architectures and teams of DBAs spend their time.  It sounds like the realm of Departmental IT and SMB where business leaders are in a hurry to gain access to data and answers without the lead time and pain of complex architectures and high costs.

I’m hearing about a SaaS focus from a lot of companies. The Continuent link above mentions one. So does RainStor’s latest blog post. Gooddata, a SaaS vendor itself, seems focused on analyzing data that was originally created via SaaS. I haven’t talked with Cast Iron or Pervasive for a while, but when I did, their ETL market targeting was all about SaaS. And of course, I hear dumber SaaS-focus ideas as well. I think the biggest substantive reason for this trend is — if you don’t have the broadest feature set, and fear large enterprises therefore won’t want your stuff, going after SMBs makes sense. And SMBs are presumed to be going SaaS. Also in the mix, of course, are a single platform to support, a small number of large SaaS vendors to sell to or partner with, and/or general trendiness.

• Like Infobright, Calpont’s InfiniDB is a columnar DBMS consisting of a MySQL front end and a columnar storage engine.
• Community edition InfiniDB runs on a single server.
• One of commercial/enterprise edition InfiniDB’s main claims to fame will be MPP support.
• There’s no announced time frame for commercial edition InfiniDB.
• InfiniDB’s current compression story is dictionary/token only, with decompression occurring before joins are executed. Improvement is a roadmap item.
• Indeed, InfiniDB has many roadmap items, a few of which can be found here. Also, a great overview of InfiniDB’s current state and roadmap can be found in this MySQL Performance Blog thread. (And follow the links there to find performance discussions of other free analytic DBMS.)
• One thing InfiniDB already has that is still a roadmap item for Infobright is the ability to run a query across multiple cores at once.
• One thing free InfiniDB has that Infobright only offers in its Enterprise Edition is ACID-compliant Insert/Update/Delete. (Note: I wish people would stop saying that Infobright Enterprise Edition isn’t ACID-compliant, since that point was cleared up a while ago.)
• InfiniDB has no indexes or materialized views.
• However, InfiniDB’s retrieval is expedited by something called “Extents,” which sounds a lot like Netezza’s zone maps.

Being on vacation, I’ll stop there for now. (If it weren’t for Tropical Storm/ depression Ida, I might not even be posting this much until I get back.)

Greenplum Single-Node Edition:

• Is free of charge, although you can buy support.
• Has no restrictions on use, production or otherwise.
• Has no restrictions on database size.
• Is closed-source.

For those who want free, terabyte-scale data warehousing software, Greenplum Single-Node Edition may be quite appealing, considering that the main available alternatives are:

• General-purpose open-source DBMS, such as PostgreSQL and MySQL (lacking analytic DBMS performance and features)
• Infobright Community Edition (the other best choice – Infobright’s commercial sales success indicates the solidity of Infobright’s technology)
• Rough research-project code and other other questionable open source offerings
• Crippleware from other commercial analytic DBMS vendors (e.g., Teradata)

For example, comparing PostgreSQL-based Greenplum with PostgreSQL itself, Greenplum offers:

• The ability to scale out queries across all cores in your box (and no, pgpool is not a serious alternative)
• Storage alternatives such as columnar (I am told that EnterpriseDB recently stopped funding a project for a PostgreSQL columnar option)

Greenplum would surely also argue that its software is superior to PostgreSQL in parallel load, compression, MapReduce integration, and general fit-and-finish. I imagine that in some (perhaps not all) cases it would be right. PostgreSQL’s main technical advantages over Greenplum would probably lie in the area of datatype extensibility.

The main target users for Greenplum’s Single-Node Edition are obviously individual enterprise power users or very small analytic teams. I.e., it’s people with a data mart need that a central data warehouse isn’t meeting. Potential benefits to Greenplum include:

• Adding value to its Enterprise Data Cloud story
• Seeding the market for future enterprise sales
• Depriving competitors of revenue, perhaps at enterprises too small to ever be paying Greenplum customers

In addition, I see free Greenplum as a charity offering that could be appealing to scientists who face PostgreSQL performance limitations.

Related links

• Greenplum Free Single-Node Edition press release (I’m quoted)
• MySQL Performance blog on MonetDB and Infobright community edition
• PostgreSQL’s restriction to one core per query
• Infobright’s restriction to one core per query

• Infobright now has >100 paying customers.
• Typical database size is from the low 100s of gigabytes to the low single-digit number of terabytes.
• Agile development is at or approaching two-week release cycles.
• Like Kickfire, Infobright  has a multi-year deal with MySQL that insulates it against many potential Oracle/MySQL shenanigans.
• From an industry perspective, Infobright’s customer base sounds a lot like other vendors’:
  • Data mart outsourcing/online analytics
  • Log files for websites
  • Telecommunications
  • Financial services
  • OEM, especially in the markets cited above
  • “Hey, we’re beginning to see the occasional energy deal”
  • A few random others
• Infobright is seeing some household-name customers, who surely have big-name analytic DBMS products, but who also have a policy that open source is the default choice, and if open source can get the job done then the favorite closed-source choices aren’t used.
• Infobright has the usual open-source community story — lots of involvement and engagement in the forums, but contributions are limited mainly to connectivity, utility scripts, etc. (Maybe some national language translation too; I’m not sure.)