Open source
Discussion of relational database management systems that are offered through some version of open source licensing. Related subjects include:
Greenplum Single-Node Edition — sometimes free is a real cool price
Greenplum is announcing today that you can run Greenplum software on a single 8-core commodity server, free. First and foremost, that’s a strong statement that Greenplum wants enterprises to pay it for Greenplum’s parallelization/”private cloud” capabilities. Second, it may be an attractive gift to a variety of folks who want to extract insight from terabyte-scale databases of various kinds.
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)
| Categories: Analytic technologies, Data warehousing, EnterpriseDB and Postgres Plus, Greenplum, Infobright, Open source, PostgreSQL, Pricing, Scientific research | 14 Comments |
Infobright notes
I had lunch w/ Bob Zurek and Susan Davis of Infobright today. This wasn’t primarily a briefing, but a few takeaways are:
- 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.)
How 30+ enterprises are using Hadoop
MapReduce is definitely gaining traction, especially but by no means only in the form of Hadoop. In the aftermath of Hadoop World, Jeff Hammerbacher of Cloudera walked me quickly through 25 customers he pulled from Cloudera’s files. Facts and metrics ranged widely, of course:
- Some are in heavy production with Hadoop, and closely engaged with Cloudera. Others are active Hadoop users but are very secretive. Yet others signed up for initial Hadoop training last week.
- Some have Hadoop clusters in the thousands of nodes. Many have Hadoop clusters in the 50-100 node range. Others are just prototyping Hadoop use. And one seems to be “OEMing” a small Hadoop cluster in each piece of equipment sold.
- Many export data from Hadoop to a relational DBMS; many others just leave it in HDFS (Hadoop Distributed File System), e.g. with Hive as the query language, or in exactly one case Jaql.
- Some are household names, in web businesses or otherwise. Others seem to be pretty obscure.
- Industries include financial services, telecom (Asia only, and quite new), bioinformatics (and other research), intelligence, and lots of web and/or advertising/media.
- Application areas mentioned — and these overlap in some cases — include:
- Log and/or clickstream analysis of various kinds
- Marketing analytics
- Machine learning and/or sophisticated data mining
- Image processing
- Processing of XML messages
- Web crawling and/or text processing
- General archiving, including of relational/tabular data, e.g. for compliance
Jacek Becla on issues in scientific data management
Just as Martin Kersten did, Jacek Becla emailed a response to my post on issues in scientific data management. With his permission, I’ve lightly edited his email too, and am posting it below, with some interspersed comments of my own. Read more
| Categories: Analytic technologies, Hadoop, MapReduce, Objectivity and Infinite Graph, Open source, Parallelization, SciDB, Scientific research | 4 Comments |
Issues in scientific data management
In the opinion of the leaders of the XLDB and SciDB efforts, key requirements for scientific data management include:
- A data model based on multidimensional arrays, not sets of tuples
- A storage model based on versions and not update in place
- Built-in support for provenance (lineage), workflows, and uncertainty
- Scalability to 100s of petabytes and 1,000s of nodes with high degrees of tolerance to failures
- Support for “external” data objects so that data sets can be queried and manipulated without ever having to be loaded into the database
- Open source in order to foster a community of contributors and to insure that data is never “locked up” — a critical requirement for scientists
However: Read more
Yahoo wants to do decapetabyte-scale data warehousing in Hadoop
My old client Mark Tsimelzon moved over to Yahoo after Coral8 was acquired, and I caught up with him last month. He turns out to be running development for a significant portion of Yahoo’s Hadoop effort — everything other than HDFS (Hadoop Distributed File System). Yahoo evidently plans to, within a year or so, get Hadoop to the point that it is managing 10s of petabytes of data for Yahoo, with reasonable data warehousing functionality.
Highlights of our visit included:
- There are dozens of people at Yahoo doing Hadoop development that will wind up getting open sourced. (Full-time or close to it.) In particular, everything Mark’s team does goes to open source.
- Yahoo is moving as much of its analytics to Hadoop as possible. Much of this is being moved away from Oracle and from Yahoo’s own Everest.
- A column store is being put on top of HDFS, based on Yahoo technology. Columns will be striped across nodes. Perhaps that’s why the effort is called Project Zebra.
- Mark believes that in a year Hadoop will be much further along in meeting traditional data warehousing requirements, in areas such as:
- Metadata
- SLAs/high availability/other workload management
- Data retention policies
- Security/privacy*
- Yahoo views the time-to-market benefits of Hadoop as being more important than TCO.
| Categories: Analytic technologies, Data warehousing, Hadoop, MapReduce, Open source, Oracle, Petabyte-scale data management, Web analytics, Yahoo | 6 Comments |
HadoopDB
Despite a thoughtful heads-up from Daniel Abadi at the time of his original posting about HadoopDB, I’m just getting around to writing about it now. HadoopDB is a research project carried out by a couple of Abadi’s students. Further research is definitely planned. But it seems too early to say that HadoopDB will ever get past the “research and oh by the way the code is open sourced” stage and become a real code line — whether commercialized, open source, or both.
The basic idea of HadoopDB is to put copies of a DBMS at different nodes of a grid, and use Hadoop to parcel work among them. Major benefits when compared with massively parallel DBMS are said to be:
- Open/cheap/free
- Query fault-tolerance
- The related concept of tolerating node degradation that isn’t an outright node failure.
HadoopDB has actually been built with PostgreSQL. That version achieved performance well below that of a commercial DBMS “DBX”, where X=2. Column-store guru Abadi has repeatedly signaled his intention to try out HadoopDB with VectorWise at the nodes instead. (Recall that VectorWise is shared-everything.) It will be interesting to see how that configuration performs.
The real opportunity for HadoopDB, however, in my opinion may lie elsewhere. Read more
Introduction to the XLDB and SciDB projects
Before I write anything else about the overlapping efforts known as XLDB and SciDB, I probably should explain and disambiguate what they are as best I can. XLDB was organized and still is run by guys who want to solve a scientific problem in eXtremely Large DataBase Management, most especially Jacek Becla of SLAC (the organization previously known as Stanford Linear Accelerator Center). Becla’s original motivation was that he needs a DBMS to manage what will be 55 petabytes of raw image data and 100 petabytes of astronomical data total for LSST (Large Synoptic Survey Telescope). Read more
| Categories: Data models and architecture, Database diversity, eBay, Michael Stonebraker, Open source, Petabyte-scale data management, Scientific research, Theory and architecture | 2 Comments |
What could or should make Oracle/MySQL antitrust concerns go away?
When the Oracle/MySQL deal was first announced, I wrote:
I can probably come up with business practices that could make things very hard on Oracle/MySQL competitors … but I haven’t found a compelling antitrust trigger on my first pass over the subject.
Subsequently, there’s been a lot of discussion about whether or not Oracle can use control of MySQL to make life difficult for third-party MySQL storage engine vendors.
Now that the European Commission is delaying the Oracle/Sun deal, explicitly because of Oracle/MySQL antitrust fears. That is, the European Commission wants to be reassured that an Oracle takeover of MySQL won’t unduly impinge upon the future availability of open source/low cost DBMS alternatives. This raises that natural question:
What could Oracle do to assure concerned parties that its ownership of MySQL won’t unduly hamper open-source-based DBMS competition?
I think that’s indeed the crucial question. The Oracle/Sun deal has enough momentum at this point that it both should and will be allowed to happen — perhaps with safeguards — rather than banned outright. If you have concerns about Oracle’s pending acquisition of MySQL, you should speak up and outline what kinds of regulatory safeguards would alleviate the problems you foresee.
More or less obvious possibilities include:
- Divest MySQL. This is obviously an extreme measure, but it surely would work.
- Provide some money and trademark rights to MySQL forkers. If MariaDB and Drizzle were put into strong competitive positions with MySQL today, it’s hard to argue how regulators could object to any future Oracle maneuverings Oracle might envision with the GPLed side of MySQL.
- Offer a standard, attractive, long-term deal to MySQL bundlers. The commercial/non-GPL version of MySQL is a requirement for appliance vendors (surely), OEM vendors (probably), and storage engine vendors (maybe — I disagree, but I’m evidently in the minority).
- Strengthen PostgreSQL. 🙂 Realistically, that’s not going to be part of any Oracle/MySQL resolution, so I’ll leave it as a subject for another time.
| Categories: Mid-range, MySQL, Open source, Oracle, PostgreSQL | 9 Comments |
Continuent on clustering
Robert Hodges, CTO of my client Continuent, put up a blog post laying out his and Continuent’s views on database clustering. Continuent offers Tungsten, its third try at database clustering technology, targeted at MySQL, PostgreSQL, and perhaps Oracle. Unlike Continuent’s more ambitious. second-generation product, Tungsten offers single-master replication, which in Robert’s view allows for great ease of deployment and administration (he likes the phrase “bone-simple”).
The downside to Continuent Tungsten ‘s stripped down architecture is that it doesn’t solve the most extreme performance scale-out problems. Instead, Continuent focuses on the other big benefits of keeping your data in more than one place, namely high availability and data loss prevention (i.e., backup).
Continuent has been around for a number of years, starting out in Finland but now being based in Silicon Valley. For most purposes, however, it’s reasonable to think of Continuent and Tungsten as start-up efforts.
As you might guess from the references to Finland and MySQL, Continuent’s products are open source, or at least have open source versions. I’m still a little fuzzy as to which features are open sourced and which are not. For that matter, I’m still unclear as to Tungsten’s feature list overall …
| Categories: Clustering, Continuent, MySQL, Open source, PostgreSQL | 3 Comments |