VectorWise
Analysis of VectorWise and its columnar analytic DBMS efforts. Related subjects include:
- Data warehousing
- Open source database management systems
- Columnar database management systems
- Ingres, which is partnering closely with VectorWise
- MonetDB, a related research project
Highlights of a busy news week
I put up 14 posts over the past week, so perhaps you haven’t had a chance yet to read them all. 
Highlights included:
- My most important post of the week was a general guide to IT vendor strategy. That one has already spawned discussion at many companies, from the tiny to the multi-billion-dollar.
- The best comment thread of the week was probably on my post about scale-out relational OLTP choices, in which people discussed the merits of various particular alternatives.
- I recommended that people strongly consider attending XLDB 5 in Menlo Park on October 18-19.
Most of the posts, however, were reactions to news events. In particular:
- Teradata announced that Teradata 14 will be hybrid-columnar, more in Vertica’s way than in Greenplum’s or Aster Data’s. (Pay no attention to the Wall Street Journal’s apparent belief that no other analytic DBMS is hybrid-columnar at all.)
- Aster announced the unsurprising news that there will be a Teradata Aster appliance. Also, Aster talked about greater analytic flexibility in the forthcoming Aster 5.0.
- With Oracle OpenWorld coming up, Oracle decided to get some of its announcing out of the way early. In particular, it announced the Oracle Database Appliance, which is small-business-friendly hardware for running the Oracle DBMS. However, the Oracle Database Appliance doesn’t seem to do much about the complexity of running the Oracle DBMS software.
- In a catch-all Hadoop post, I noted that:
- Oracle has now clearly said it has a Hadoop appliance coming, no doubt next week at OpenWorld.
- I still can’t see why Hadoop appliances would succeed, but a lot of smart folks seem to disagree with me.
- Greenplum announced what looks like a nice but unimportant little product upgrade.
- It’s a really good thing that previously reported plans to revamp Hadoop are underway.
- DataStax announced that it really is a Cassandra company after all. Pay no attention to previous marketing that seemed to put DataStax in the same Hadoop-alternative category as, say, MapR.
- Ingres has changed its name to Actian. The announcement seems like a confession that Ingres and VectorWise are going nowhere.
| Categories: Aster Data, Data warehousing, DataStax, Greenplum, Hadoop, Ingres, Teradata, VectorWise | Leave a Comment |
Ingres deemphasized, company now named Actian
Ingres, the company, is:
- Changing its name to Actian.
- Deemphasizing Ingres, the product.
- Emphasizing a set of products that don’t exist yet (or at least aren’t shipping), namely lightweight mobile apps that are business-intelligence-plus-an-action, and technology for building them. These are called “Action Apps”, and are discussed on the Actian company blog.
- Positioning all this as something to do with “big data” (what a shock).
It turns out that Actian was the name of an ancient athletic competition commemorating Augustus’ defeat of Anthony at Actium, a battle that was more recently memorialized in the movie Cleopatra. Frankly, I think Cleopatra Software might have been a more interesting company name, although that could mean execs would have to arrive at sales calls rolled up in a carpet.
| Categories: Business intelligence, Hadapt, Ingres, Market share and customer counts, VectorWise | 7 Comments |
Hadapt update
I met with the Hadapt guys today. I think I can be a bit crisper than before in positioning Hadapt and its use cases, namely:
- Hadapt is additional software on a cluster that also runs fully functional Hadoop/HDFS. (Cloudera Hadoop more than straight-from-Apache Hadoop to date, but that’s not a requirement.)
- The cluster also runs a DBMS on every node, such as PostgreSQL or one of Infobright/Vectorwise.
- Hadapt’s software manages parallel SQL queries by distributing them to the DBMS living on each node. Hadapt says that the resulting query performance far outshines Hive’s.
- Hadapt further says that, by exploiting the partner DBMS, its SQL functionality outpaces Hive’s as well.
- Target Hadapt use cases are centered around keeping machine-generated or other poly-structured data in Hadoop, and extracting, enhancing, or otherwise deriving some of it to live in the relational store.
- In particular, Hadapt seems like an interesting choice when you want to use that relational data as you work on other data that’s still in HDFS, or if you want to keep using the relational data in other kinds of MapReduce jobs.
- That all fits well with my thoughts about the importance of derived data.
Other evolution from what I wrote about Hadapt a few months ago includes:
- Hadapt is in beta now.
- Hadapt has added adult supervision in the form of Philip Wickline, late of Endeca.
In other news, Hadapt is our newest client.
| Categories: Analytic technologies, Cloudera, Data models and architecture, Data warehousing, Hadapt, Hadoop, Infobright, MapReduce, Open source, PostgreSQL, VectorWise | Leave a Comment |
Hadapt (commercialized HadoopDB)
The HadoopDB company Hadapt is finally launching, based on the HadoopDB project, albeit with code rewritten from scratch. As you may recall, the core idea of HadoopDB is to put a DBMS on every node, and use MapReduce to talk to the whole database. The idea is to get the same SQL/MapReduce integration as you get if you use Hive, but with much better performance* and perhaps somewhat better SQL functionality.** Advantages vs. a DBMS-based analytic platform that includes MapReduce — e.g. Aster Data — are less clear. Read more
| Categories: Analytic technologies, Data warehousing, Hadapt, Hadoop, MapReduce, MySQL, Open source, Parallelization, PostgreSQL, Theory and architecture, VectorWise | 12 Comments |
Ingres VectorWise technical highlights
After working through problems w/ travel, cell phones, and so on, Peter Boncz of VectorWise finally caught up with me for a regrettably brief call. Peter gave me the strong impression that what I’d written in the past about VectorWise had been and remained accurate, so I focused on filling in the gaps. Highlights included: Read more
| Categories: Analytic technologies, Benchmarks and POCs, Columnar database management, Data warehousing, Database compression, Ingres, Open source, VectorWise | 1 Comment |
Fun with quotes in the VectorWise press release
Ingres forgot to prebrief me on the VectorWise announcement, and despite valiant efforts hasn’t succeeded in connecting with me since they realized the lapse. Meanwhile, I took a look at the VectorWise press release, and found the quotes to be somewhat amusing.
Read more
| Categories: Ingres, VectorWise | 7 Comments |
PAX Analytica? Row- and column-stores begin to come together
Column-store proponents are prone to argue, in effect, that the only reason to implement an analytic DBMS with row-based storage is laziness. Their case generally runs along the lines:
- Analytic queries commonly return only a fraction of all possible columns.
- Only returning the columns needed
- Saves I/O
- Saves cache space
- Reduces processing
- Facilitates compression
- Presumably all those row-based MPP vendors just went row-based because they had a fine row-based DBMS (usually but not always PostgreSQL) to build on.
Pushbacks to this argument from row-based vendors include:
- Yes, but it’s harder to update a column store
- Yes, but there are more steps to retrieving a bunch of columns than there are to retrieving the same information from row stores
| Categories: Analytic technologies, Columnar database management, Data warehousing, Theory and architecture, VectorWise, Vertica Systems | 9 Comments |
Vertica’s version of MapReduce integration
I talked with Omer Trajman of Vertica Monday night about Vertica’s MapReduce integration, part of its Vertica 3.5 release. Highlights included:
- By “integrating Vertica and MapReduce,” Vertica means “integrating Vertica and Hadoop.”
- Vertica’s Hadoop integration is based on Cloudera’s DBInputFormat.
- Omer called out for me several features of Vertica’s Hadoop integration that didn’t just come from Cloudera, namely:
- Cloudera’s DBInputFormat assumes the database runs on a single computer, or a single head node of an MPP system. Vertica’s technology, however, runs on peer parallel nodes with no head, and so Vertica adapted the DBInputFormat technology accordingly.
- Vertica lets you push down Map functions to the database. Omer reports a roughly even division among users and prospects between those who want to do this and ones who don’t.
- Vertica lets you do Reduce functions (or Map functions, if you don’t push them down to the database) on a separate cluster than you run the database software. Vertica asserts that its customers and prospects all want to do this. Right here is the big difference between Vertica’s MapReduce integration and Aster’s or Greenplum’s. (Aster would also say that Vertica’s weaker MapReduce/SQL programming integration is a big difference as well.)
- Indeed, Vertica lets you Reduce into a different DBMS than Vertica, if you choose.
- Vertica gives you flexibility on the size of the Map and Reduce clusters. Omer agreed with me when I said there were some limits on how fast one can add or subtract nodes in a Vertica grid, because there’s data redistribution involved. But one can add/change/delete Hadoop clusters extremely quickly.
Apparently, the use cases for Vertica/Hadoop integration to date lie in algorithmic trading and two kinds of web analytics. Specifically: Read more
VectorWise, Ingres, and MonetDB
I talked with Peter Boncz and Marcin Zukowski of VectorWise last Wednesday, but didn’t get around to writing about VectorWise immediately. Since then, VectorWise and its partner Ingres have gotten considerable coverage, especially from an enthusiastic Daniel Abadi. Basic facts that you may already know include:
- VectorWise, the product, will be an open-source columnar analytic DBMS. (But that’s not quite true. Pending productization, it’s more accurate to call the VectorWise technology a row/column hybrid.)
- VectorWise is due to be introduced in 2010. (Peter Boncz said that to me more clearly than I’ve seen in other coverage.)
- VectorWise and Ingres have a deal in which Ingres will at least be the exclusive seller of the VectorWise technology, and hopefully will buy the whole company.
- Notwithstanding that it was once named something like “MonetDB,” VectorWise actually is not the same thing as MonetDB, another open source columnar analytic DBMS from the same research group.
- The MonetDB and VectorWise research groups consist in large part of academics in Holland, specifically at CWI (Centrum voor Wiskunde en Informatica). But Ingres has a research group working on the project too. (Right now there are about seven “highly experienced” people each on the VectorWise and Ingres sides, although at least the VectorWise folks aren’t all full-time. More are being added.)
- Ingres and VectorWise haven’t agreed exactly how VectorWise and Ingres Classic will play together in the Ingres product line. (All of the obvious possibilities are still on the table.)
- VectorWise is shared-everything, just as Ingres is. But plans — still tentative — are afoot to integrate VectorWise with MapReduce in Daniel Abadi’s HadoopDB project.