The Wonderful One-Hoss Shay
I often write of Bottleneck Whack-A-Mole, an engineering approach that ensues when parts of a system are out of balance. Well, the flip side of that is the One-Hoss Shay, as in Oliver Wendell Holmes’ marvelous poem. (Here’s a version with Howard Pyle illustrations.) Read more
| Categories: Humor, Theory and architecture | 1 Comment |
EMC is buying Greenplum
EMC is buying Greenplum. Most of the press release is a general recapitulation of Greenplum’s marketing messages, the main exceptions being (emphasis mine):
The acquisition of Greenplum will be an all-cash transaction and is expected to be completed in the third quarter of 2010, subject to customary closing conditions and regulatory approvals. The acquisition is not expected to have a material impact to EMC GAAP and non-GAAP EPS for the full 2010 fiscal year. Upon close, Bill Cook will lead the new data computing product division and report to Pat Gelsinger. EMC will continue to offer Greenplum’s full product portfolio to customers and plans to deliver new EMC Proven reference architectures as well as an integrated hardware and software offering designed to improve performance and drive down implementation costs.
Greenplum is one of my biggest vendor clients, and EMC is just becoming one, but of course neither side gave me a heads-up before the deal happened, nor have I yet been briefed subsequently. With those disclaimers out of the way, some of my early thoughts include:
- I wish my clients would never buy each other, but it’s inevitable.
- I don’t think anybody evaluating Greenplum should be much influenced by this deal one way or the other. (Whether they will be is of course a different matter.)
- EMC tends to run its bigger software acquisitions in a fairly hands-off manner. There’s no particular FUD (Fear/Uncertainty/Doubt) reason why this deal should stop anybody from buying Greenplum software.
- I also don’t think adding a rich parent adds much of a reason to buy from Greenplum. But if you’re the type who’s nervous about smaller vendors — well, Greenplum now isn’t so small.
- Greenplum Chorus could, in principle, work with non-Greenplum DBMS. That possibility suddenly looks a lot more realistic.
- The list of analytic DBMS vendors with an appliance orientation is pretty impressive, including:
- Oracle, with Exadata
- Microsoft, partially
- Teradata
- Netezza
- Now EMC/Greenplum, at least partially
- Weaker players such as:
- The ailing Kickfire, which a client (not Kickfire itself) tells me is being shopped around
- The reeling HP Neoview
- XtremeData, but I’m still waiting to hear of XtremeData’s first real sale
- Greenplum is something of a specialist in large databases. EMC has to love that.
- Greenplum’s weakness is concurrency.
- Greenplum’s “polymorphic storage” is a good fit for a storage vendor with appliance-y ideas.
- And finally — I think that even software-only analytic DBMS vendors should design their systems in an increasingly storage-aware manner, and have been advising my vendor clients of same. I’ll blog that line of reasoning separately when I get a chance, and edit in a link here after I do.
Related links (edit)
- Here’s the promised post as to why analytic DBMS need to be ever more storage-aware.
- Dave Kellogg crunched the EMC/Greenplum numbers, coming up with an estimated valuation range of $3-400 million, the high end of which is rumored to be correct.
- Merv Adrian suggests the big EMC/Greenplum loser is ParAccel, a viewpoint which presumably presupposes that the EMC/ParAccel partnership was significant in the first place.
- I talked with Ben Werther and posted more about Greenplum and EMC.
| Categories: Data warehouse appliances, EMC, Greenplum, Storage | 13 Comments |
Riptano, and Cassandra adoption
Tonight’s Cassandra technology post got plenty long enough on its own, so I’m separating out business and adoption issues here. For starters, known Cassandra users include:
- Facebook, which has said it has 150 or so Cassandra nodes (but see below)
- Twitter, which has said it has 45 or so Cassandra nodes
- Rackspace, which used to be Jonathan Ellis’ employer, and now is backing Cassandra company Riptano
- Digg, which along with Twitter and Rackspace was one of the three major users helping advance the Cassandra project
- OpenX, Simple Geo, Digital Reasoning, who Jonathan cited as production users in March
- Cloudkick, as noted and linked in my other post
- Two customers Riptano named at launch (but I’ve forgotten who they were*)
Fetlife, Meebo, and others seem to at least have a healthy interest in Cassandra, based on their level of involvement in a forthcoming Cassandra Summit. That said, the @Fetlife tweetstream features numerous yelps of pain, and I don’t mean the recreational kind. Read more
| Categories: Cassandra, DataStax, Facebook, Market share and customer counts, NoSQL, Open source, Parallelization, Pricing, Specific users | 5 Comments |
Cassandra technical overview
Back in March, I talked with Jonathan Ellis of Rackspace, who runs the Apache Cassandra project. I started drafting a blog post then, but never put it up. Then Jonathan cofounded Riptano, a company to commercialize Cassandra, and so I talked with him again in May. Well, I’m finally finding time to clear my Cassandra/Riptano backlog. I’ll cover the more technical parts below, and the more business- or usage-oriented ones in a companion Cassandra/Riptano post.
Jonathan’s core claims for Cassandra include:
- Cassandra is shared-nothing.
- Cassandra has good approaches to replication and partitioning, right out of the box.
- In particular, Cassandra is good for use cases that distribute a database around the world and want to access it at “local” latencies. (Indeed, Jonathan asserts that non-local replication is a significant non-big-data Cassandra use case.)
- Cassandra’s scale-out is application-transparent, unlike sharded MySQL’s.
- Cassandra is fast at both appends and range queries, which would be hard to accomplish in a pure key-value store.
In general, Jonathan positions Cassandra as being best-suited to handle a small number of operations at high volume, throughput, and speed. The rest of what you do, as far as he’s concerned, may well belong in a more traditional SQL DBMS. Read more
| Categories: Amazon and its cloud, Cassandra, DataStax, Facebook, Google, Log analysis, NoSQL, Open source, Parallelization | 4 Comments |
The essential questions of Fair Data Use
Today is Independence Day in the United States, which seems like a great time to return to the subject of liberty, privacy, and fair data use. I continue to believe:
- New technologies for information creation, gathering, and analysis offer dire new possibilities for abuse.
- Our law- and policy-makers need to create effective new safeguards in response.
- That’s not going to happen unless we in the technology community help them.
In this matter – as in many others – I think getting the questions right is at least as important and difficult as then choosing the answers. What’s more, I think that the questions naturally fall into the domain of the technologists – we know better what is possible, what will be possible in the future, and which distinctions lead to true differences. The answers, on the other hand, lie more properly in the domain of those whose expertise is the crafting of actual laws.
For my first draft of suggested Fair Data Use Questions, I am dividing things into three categories:
- The questions themselves.
- Different kinds of data (for which the questions may have different answers).
- Other qualifiers that could change the answers to the questions.
Suggested additions and other comments will be gratefully received. I intend for this to be a community effort. Read more
| Categories: Surveillance and privacy | 15 Comments |
Why you should go to XLDB4
Scientific data commonly:
- Comes in large volumes
- Is machine-generated
- Is augmented by synthetic and/or derived data
- Has a spatial and/or temporal structure
In those respects, it is akin to some of the hottest areas for big data analytics, including:
- Investment trade data – big, partly machine generated, augmented (often), temporal
- Web/network log data – big, machine-generated, post-processed into derived form, temporal
- Marketing analytic data – big, post-processed into derived form
- Genomic data
So when Jacek Becla started the XLDB conferences on the premise that scientific and big data analytic challenges have a lot in common, he had a point. There are several tough database problems that the science-focused folks have taken the leading in thinking about, but which are soon going to matter to the commercial world as well. And that’s one of two big reasons why you should consider participating in XLDB4, October 6-7, at the SLAC facility in Menlo Park, CA, as an attendee, sponsor, or both.
The other big reason is that it is important for the world that XLDB succeed. Read more
| Categories: Investment research and trading, Log analysis, Scientific research, Web analytics | 2 Comments |
Cloudera Enterprise and Hadoop evolution
I talked with Cloudera a couple of weeks ago in connection with the impending release of Cloudera Enterprise. I’d say: Read more
Details and analysis of the VoltDB argument
Todd Hoff (High Scalability blog) posted a lengthy examination of the case and use cases for VoltDB. That excellent post, in turn, is based on a Mike Stonebraker* webinar for VoltDB, for which the slide deck is happily available. It’s all nicely consistent with what I wrote about VoltDB last month, in connection with its launch. Read more
| Categories: In-memory DBMS, Michael Stonebraker, OLTP, Parallelization, Theory and architecture, VoltDB and H-Store | 3 Comments |
Infobright’s Release 3.4
Infobright called a couple weeks ago to discuss, among other subjects, its subsequently-released Infobright Release 3.4. I made no effort to distinguish between community/open source and professional/chargeable editions, but leaving that aside, it seems fair to characterize Infobright 3.4 as having two overlapping primary themes:
- 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: Read more
| Categories: Data warehousing, Infobright, MySQL, Workload management | 6 Comments |
Lots of Aster Data analytic packages
A number of vendors had announcements last week, notably:
- Netezza (user conference)
- Aster Data (to steal some of Netezza’s thunder)
- Infobright (so far as I can tell, just because it was time for a product release, and also to get ahead of the summer doldrums)
- Northscale (ditto)
Time to play some catchup.
I’ll start with Aster Data, which added to the list of analytic packages it previously announced, and kindly gave me permission to post a partial slide deck from the briefing on same. Highlights of Aster’s analytic packages story include: Read more