• Workday has forward-thinking ideas about SaaS enterprise applications and the integration of business intelligence into same.
• Workday has highly innovative ideas in how it manages data.
• Companies founded by Dave Duffield tend to feature smart, likeable people who talk to one pleasantly and forthrightly. Workday is no exception; CTO Stan Swete and the other Workday folks present were a delight to talk with.
• I’d invited Merv Adrian to come along with me. He asked great questions, and I could gather myself a bit despite how sleep-deprived I was for the first part of that trip.

Workday kindly allowed me to post this Workday slide deck. Otherwise, I’ve split out a quick Workday, Inc. company overview into a separate post.

The biggie for me was the data and object management part. Specifically: 

• Workday’s applications run entirely in-memory, in a highly object-oriented structure. Persistence is mainly for the sake of data safety …
• … but not entirely. In earlier releases, Workday kept absolutely everything in RAM. However, certain things are kept only on disk, such as:
  • Audit files.
  • Certain documents (notably resumes).
• Workday’s whole database – data and metadata alike – is persisted to disk in <10 MySQL/InnoDB tables. MySQL is basically just being used as a key-value store, albeit one with ACID transactional support.
  • There are 3 main tables: attributes, relationships, instances.
  • When I suggested this might be like an entity-attribute-value model, Workday said it would be even better to think in terms of instanceID-attribute-value.
  • As you might expect for a database that simple, its schema doesn’t change much.
  • By way of comparison, Workday estimates that if its software were written relationally, there would be 1000s of tables, which would take up 10-100X as much disk space.
• All write transactions are banged immediately into the MySQL database. I.e., RAM and disk are never allowed to get out of sync.
• Workday’s database is append-only. This is exploited for effective dating (pretty heavily, it seems, perhaps because that’s a useful concept in human resources) and snapshotted reporting.
• Workday’s built-in BI doesn’t have a lot of choice but to do scans, traversing the object model. This turns out to be fast enough.

Other notes on Workday’s data and object management strategy include:

• Workday is object-oriented through and through – no object-relational mapping – turtles all the way down. On average, a class has about 2 attributes.
• 94% of requests are reads, traversing the object hierarchy.
• Workday databases are pretty small.
  • The biggest database Workday supports uses 17 gigabytes of RAM.
  • Workday databases are much smaller on disk than in RAM.
• Workday’s “dream” is to move from disk to solid-state memory.
• Workday uses GPLed MySQL/InnoDB. So there’s no software license reason to ever move away (e.g., to a pure key-value store).
• Disaster recovery is based on local and remote MySQL slaves.

Obviously, serious apps have been built before in object-oriented and/or key-value ways, with the resulting objects then being banged to disk (or in some cases kept in memory). Examples include:

• Numerous applications are built on object-oriented DBMS. Generally they go against disk, although memory-centric implementations can save a lot of pointer-chasing. Often they’re queried via SQL.
• Basho’s website says that its key-value store Riak was originally conceived in connection with a planned salesforce automation product, but I don’t think that the application part of that plan ever got built.
• SAP has longstanding doubts about relational dogma, although not nearly to Workday’s extreme.
• Obviously, some major internet applications just bang data into key-value stores.

Still, perhaps because it wholly object-oriented yet doesn’t even bother with anything like a real object-oriented DBMS, Workday’s approach seems particularly cool.

Other highlights of Workday, Inc.’s technical story include:

• Workday has settled into a schedule of three releases per year, and has pretty much lived up to that for >2 years.
  • Every user is always on the latest Workday release.
  • You can delay turning on significant new Workday software functionality if you want to.
  • Pure UI changes to the Workday software are handled much as they are on various websites today. Sometimes you have no choice but to live with them; sometimes the prior version of the UI remains available to you for a while.
• Workday’s navigational approaches look pretty cool.
  • The core concept is a list of actions you can perform now, rather than more standard menus.
  • Roles/permissions are of course central to this.
  • Reports have lots of actionable links in them. (More than just drilldown, although specific examples have slipped my memory.)
  • Alternatively, you can navigate via a search box, searching both on names of objects (e.g. users, divisions) or on names of tasks. This is somewhat reminiscent of an approach SAP was considering a few years ago.
• Workday says it has four key design premises:
  • Web-Familiar Experience. I’d say that’s true to to the extent it makes sense. In many ways, the web needs to catch up to Workday.
  • Enterprise Reporting. The idea is that you get a report, then take actions based on it. Hence the report-centric options for navigation.
  • Integration On-Demand. That’s a fancy way of saying “Plays nicely with others.”
  • Configurable Business Processes. Duh. That’s pretty essential if you want to do serious SaaS applications.
• Workday maintains a strong separation between application logic and UI development. Developer do no screen layouts. Instead, Uis are automatically generated for:
  • Flash/FLEX
  • iPhone
  • Mobile HTML
  • PDF export
  • Excel export
• Workday only talks to the outside world via web services.
  • Workday is heavily into SOAP (Simple Object Access Protocol).
  • The acquisition of OEM partner CapeClear gave Workday an Integration Service (i.e., enterprise service bus) that translates SOAP into whatever else might be needed for integration, and also does reliable delivery.
  • All that said, Stan Swete sees integration among various SaaS offerings as an area needing significant future attention.
• Workday’s business intelligence ideas are interesting, but I think there’s a long way for that technology still to go.
  • Workday’s BI seems to be focused on report/drilldown kinds of functionality.
    • You can slice by up to 2 dimensions at once.
    • Then you can keep slicing, however, by more dimensions, as many times as you like.
  • While you can take actions straight from reports, some of the specific BI/app integration ideas we discussed are still futures. (E.g., analyzing spend at the time of expense report data entry or approval.)
  • Of course, Workday’s web services interface lets you export Workday data into 3rd-party tools. Indeed, if you want to integrate data from Workday and some other source(s), that’s your only choice.
• Workday offers a clever metaphor to illustrate that your data may be more secure offsite than on – the bank vault. (I have no idea whether that’s a SaaS industry standard, but I hadn’t heard it before.) Of course, that metaphor does beg some issues specific to the remote data case, such as:
  • When your data is on premises, you know whether the government has insisted on looking at it.
  • More than cash, data keeps traveling back and forth to the remote location, which creates at least a theoretical risk of interception.
• Workday says the toughest part of globalization is the issue of which personal data is or is not maintained. For example, in the US you’re not allowed to not ask a job applicant’s religion, but in the UK you’re not only permitted but indeed required to.

This post is part of a three-post series

• Workday Inc. company overview (brief)
• Workday Inc. technology overview (detailed)
• Workday Inc. CTO Stan Swete’s comments on database strategy

That said, I think I found four sensible points in Pentaho’s Hadoop strategy, namely:

1. If you use an ETL tool like Pentaho’s to move things in and out of HDFS, you may be able to orchestrate two more steps in the ETL process than if you used Hadoop’s native orchestration tools.
2. A lot of what you want to do in MapReduce is things that can be graphically specified in an ETL tool like Pentaho’s. (That would include tokenization or regex.)
3. If you have some really lightweight BI requirements (ad hoc, reporting, or whatever) against HDFS data, you might be content to do it straight against HDFS, rather than moving the data into a real DBMS. If so, BI tools like Pentaho’s might be useful.
4. Somebody might want to use a screwy version of MapReduce, where by “screwy” I mean anything that isn’t Cloudera Enterprise, Aster Data SQL/MapReduce, or some other implementation/distribution with a lot of supporting tools. In that case, they might need all the tools they can get.

The first of those points is, in the grand scheme of things, pretty trivial.

The third one makes sense. While Hadoop’s Hive client means you could roll your own integration with your own favorite BI tool in any case, having somebody certify it for you themselves could be nice. So if Pentaho ships something that works before other vendors do, good on them. (Target date seems to be October.)

The fourth one is kind of sad.

But if there’s any shovel-meet-pony aspect to all this — or indeed a reason for writing this blog post — it would be the second point. If one understands data management, but is in the “Oh no! Hadoop wants me to PROGRAM!” crowd, then being able to specify one’s MapReduce might be a really nice alternative versus having to actually code it.

• If your goal is to keep a certain class of queries from taking too many resources, Tim thinks a great way of doing that is to control how many of them are allowed to run concurrently.
• By way of contrast, Tim is cautious about the common approach of just lowering a query’s priority. His concern is that a long-running query could linger even longer, creating a long-lasting bottleneck in, for example, temp space.
• When running over (I believe) Linux and AIX, DB2 workload management is integrated with operating system workload management. I.e., the same “service class” or “workload class” (at a guess, the former is the official term and the latter is the term that makes sense) of queries and associated processes gets the same treatment in both DB2 and the OS.
• DB2’s workload management extends to buffer pools, to inhibit low-priority queries from evicting a higher-priority query’s data from cache.
• Sometimes, workload management doesn’t throttle a query, but just decides to collect stats for future analysis. (This is on the eminently reasonably theory that the best stats to collect are the ones that are live when performance problems are actually occurring.)

Finally, Tim spoke of what I regard as the weirdest workload management requirement, one I also heard about from Netezza (but didn’t explicitly mention) in June. Sometimes, it seems, you simply don’t want queries to finish too fast. Why? Because if you give great performance when the machine is lightly loaded, then business users might expect that performance too when the machine is heavily loaded and you can’t deliver it. Apparently, in some environments it’s better to never deliver great query performance than it is to do so only inconsistently.

• You really, really want to have multiple data streams coming out of temp space, as close to simultaneously as possible.
• The storage performance characteristics of such a workload are more reminiscent of “random” than “sequential” I/O.

If everybody else is cool with it too, I can live with that.

Meanwhile, I talked again with Tim Vincent of IBM this afternoon. Tim endorsed the temp space/Flash fit, but with a different emphasis, which upon review I find I don’t really understand. The idea is:

• Analytic DBMS processing generally stresses reads over writes.
• Temp space is an exception — read and write use of temp space is pretty balanced. (You spool data out once, you read it back in once, and that’s the end of that; next time it will be overwritten.)

My problem with that is: Flash typically has lower write than read IOPS (I/O per second), so being (relatively) write-intensive would, to a first approximation, seem if anything to disfavor a workload for Flash.

On the plus side, I was reminded of something I should have noted when I wrote about DB2 compression before:

Much like Vertica, DB2 operates on compressed data all the way through, including in temp space.

• Technology it already has released*, but has not published any reference architectures for
• A Barney partnership**

In other words, Vertica has succumbed to the common delusion that it’s a good idea to put out half-baked press releases the week of TDWI conferences. But if we look past that kind of all-too-common nonsense, Vertica is highlighting an interesting technical story, about how the analytic DBMS industry can exploit solid-state memory technology.

*Upgrades to Vertica FlexStore to handle Flash memory, actually released as part of Vertica 4.0

** With Fusion I/O

To set the context, let’s recall a few points I’ve noted in the past:

• Solid-state memory’s price/throughput tradeoffs obviously make it the future of database storage.
• The Flash future is coming soon, in part because Flash’s propensity to wear out is overstated. This is especially true in the case of modern analytic DBMS, which tend to write to blocks all at once, and most particularly the case for append-only systems such as Vertica.
• Being able to intelligently split databases among various cost tiers of storage – e.g. Flash and disk – makes a whole lot of sense.

Taken together, those points tell us:

For optimal price/performance, analytic DBMS should support databases that run part on Flash, part on disk.

While all this is a future for some other analytic DBMS vendors, Vertica is shipping it today.* What’s more, three aspects of Vertica’s architecture make it particularly well-suited for hybrid Flash/disk storage, in each case for a similar reason – you can get most of the performance benefit of all-Flash for a relatively low actual investment in Flash chips: 

• Vertica lets you split tables by column, and Vertica FlexStore is versatile enough to let you put only the most-used columns in Flash. (Vertica offers a figure that 85% of usage calls on only 15% of columns, but I don’t know how rigorously grounded those numbers are.)
• To the extent that Vertica data is more compressed than many of Vertica’s competitors’ (which it probably is, debates over the magnitude of Vertica’s advantage notwithstanding), the total storage-hardware cost of sticking stuff in Flash is less when you use Vertica than with other systems.
• Vertica has relatively less need for temp space than some other systems. (Vertica uses figures of <20% of total storage, vs. 30%+ for some other systems.) If you want to use Flash for temp space, so as to accelerate your toughest queries, that can save you some cash …
• … and by the way, temp space is an especially good use of Flash, because temp space is accessed in a less sequential manner than data storage is.

The least obvious of those points are about temp space; I only understood the particulars when Vertica development chief Shilpa Lawande explained them to me Thursday.

* At least in theory; customer adoption may be a different matter.

But before drilling down on temp space, let me first note that there’s one offsetting factor to all those “We need somewhat less Flash than the other guys” Vertica advantages. Like all serious databases, a Vertica installation keeps two or more copies of all data, to that there’s no storage single point of failure. In a flexible system like Vertica, you can put one copy on Flash and one on disk. But if you do that in Vertica, you forgo fully exploiting one possible benefit of Vertica’s architecture – the ability to store different copies of a column in different orders, which are beneficial for accelerating different groups of queries.*

*More precisely, you don’t get the full benefits of Flash acceleration for every query touching those columns.

OK. Back to temp space. There are four kinds of things you can put in storage if you’re running a database management system:

• The software itself.
• Persistent data. (I.e., tables, if the DBMS you’re running is relational.)
• Metadata, especially the kind that lets you find data – indexes, zone maps, catalogs, etc.
• Temporary data constructs built as part of, say, a sort-merge join. These, by definition, are what populate temp space.

Just to be clear, those constructs are NOT temporary tables of the sort created by, say, Microstrategy; such tables are handled like any other data. Rather, they are ephemeral creations and, so far as I can tell, not tables at all.

Vertica offered two theories as to why its DBMS requires less temp space than competitors do:

• To the extent data is decompressed before being operated on in memory by the DBMS, that decompression would of course also apply to temp space as well. Vertica prides itself on keeping data compressed all the way through, and seems to get away with smaller temp space allocations as a benefit.
• Since Vertica can store columns in expedient sort orders, it does less sorting overall, and sorting is a big use of temp space.

Obviously, no matter which DBMS you use, the amount of temp space you need is surely workload-dependent. Even so, Vertica’s claim to something of an advantage seems legit.

Truth be told, I’m not convinced the savings involved are great enough to matter a whole lot – but it’s a fun subject to think through.

And finally: One of my biggest surprises since starting to look at analytic-DBMS-on-Flash has been the centrality of temp space. Talking to Vertica Thursday, I finally uncovered a key reason why: Temp space tends to be accessed via multiple streams of data at once. I’m still struggling with WHY that is true, with two reasons suggested being:

• Temp space can be accessed by multiple operations at once. (But isn’t that also true of the rest of storage?)
• Merge sorts, a common use of temp space, read multiple streams of data. (Couldn’t you tweak your software to make that not be true?)

But if we grant that temp space naturally is accessed in multiple places at once – well, that’s a lot like random I/O, and if you’re doing a lot of random reads, you’d love to use something other than spinning disk.

]]> http://www.dbms2.com/2010/08/16/vertica-flash-temp-space/feed/ 10 http://www.dbms2.com/2010/08/12/teradata-future-product-strategy/ http://www.dbms2.com/2010/08/12/teradata-future-product-strategy/#comments Thu, 12 Aug 2010 10:37:14 +0000 Curt Monash http://www.dbms2.com/?p=2769 I think Teradata’s future product strategy is coming into focus. I’ll start by outlining some particular aspects, and then show how I think it all ties together.

The immediate hook here is that I had a short conversation with Scott Gnau of Teradata yesterday, triggered by Teradata’s acquisition of Kickfire’s assets. Takeaways from that part included:

• The acquisition is all about Kickfire’s data pipelining technology.
• Scott (in my opinion rightly) thinks that isn’t particularly tied to Kickfire’s choice of particular DBMS architecture (fairly vanilla columnar).
• No decision has been made about whether the right vehicle for this technology is an FPGA (Field Programmable Gate Array), conventional Intel CPU, RAM, etc.

If you want to handicap Teradata’s future data pipelining strategy, you might note that:

• Kickfire’s own choice – and hence its existing implementation – is an FPGA.
• VectorWise’s approach to pipelining is Intel-based, apparently at the cost of being closely tied to specific generations of Intel CPUs.
• XtremeData’s approach to pipelining is FPGA-based.
• Teradata has a lot more development resources than any of those other companies, as well as important existing products, and hence has both means and motive to shoehorn new technology into older system designs.

While I had Scott on the phone, I brought up a few other subjects too. Highlights included:

• Teradata’s Flash-based appliance is doing just fine in beta test and customer POCs (Proofs of Concept).
• Other kinds of Teradata appliance are not inconceivable.
• Scott thinks Michael McIntire’s condemnation of Active-Active architectures is overstated. That said,
  • Scott does acknowledge a need for greater Active-Active scalability, and suggests that the reason Xkoto’s current products are being discontinued is their lack of scaling.
  • Scott seems quietly confident the scaling will get done.
• Scott is emphatic that Teradata is not going to go to a two-tier architecture. In particular, the point of splitting storage/lightweight database processing and heavyweight database processing on separate tiers is generally to save bandwidth, and Teradata’s BYNET is typically less than 10% loaded.
• Scott didn’t dispute my claim that this all suggests Teradata Virtual Storage is the future, at the expense of a rigid delineation among specific use-case-focused product lines.
• Unlike Netezza or Aster, Teradata doesn’t seem to plan analytic capability that works outside the UDF (User Defined Function) framework. However, Scott noted that Teradata has long had the capability that Aster and Netezza now also have of letting you run analytic code either in “protected mode” (if the process fails the whole database doesn’t crash) or in the database kernel (best performance, if you’re sufficiently confident in the code’s stability to take the risk). Scott also spoke of the release later this quarter of Teradata FastPath, which will offer yet better performance (however, there’s a gotcha to Teradata FastPath that’s still NDA).

Putting all that together with the rest of what we know about Teradata, I’m going to call out three pillars of Teradata’s long-term product strategy:

• Same fundamentals as always. Teradata’s core product strategy is:
  • Single DBMS, capable of meeting all analytic needs while running in a single instance, usually running on …
  • … proprietary hardware …
  • … built from conservatively-chosen parts.
• Selective vertical application stack. No matter how horizontally-oriented they are, many companies that have been in the analytic technology business for a while wind up with some vertical applications. It sort of just happens. Teradata is no exception. Teradata also likes to sell services to its product customers, and some of those are quite vertical-aware.
• Mutable, modular platform. This is what I highlighted above. Note that it’s philosophically attuned with the one-system-does-everything approach Teradata prefers. More subtly, please also note that it goes well with customer-by-customer price customization, which is almost a must for Teradata given the Innovator’s Dilemma kind of pricing box it finds itself in.

So far, that’s not too exciting, except in the details of how Teradata’s engineers make that all work. But there’s a fourth pillar to Teradata’s technical strategy as well, and it’s a wild card: tight partnerships. Every time I talk with Teradata hardware chief Carson Schmidt, he seems excited about some particular version of a part or other – sometimes from a reasonably established vendor (once it was LSI Logic), sometimes from a tiny one (notably the “stealth” start-up on which Teradata bet its first solid-state product.) In the future, I expect tight business intelligence partnerships as well. Cognos BI will be increasingly integrated with IBM’s DBMS and hardware; Business Objects’ BI will increasingly be integrated with SAP’s applications; and Oracle’s BI will eventually be integrated with everything. How do you compete with that if you‘re Microstrategy? Well, you try to have superior product, of course – but you also partner as closely with DBMS vendors as you can, an approach Microstrategy has already started. Predictive analytics stalwart SAS, of course, is on a partnership binge as well.

Teradata has a larger installed base than almost all its competitors, and enjoys richer third-party software and service support as a result. But I suspect that going forward, for Teradata to remain a leading competitor at price points it is willing to accept, Teradata’s “ecosystem” advantages will need to ratchet up one or several notches.

• People
• Financial trades
• Electronic networks
• Everything else

The most varied, interesting, and valuable of those four categories is the first one.

That may change some day, with the growing importance of machine-generated data, and of big-data science in particular. But I think it’s a fair assessment at the present, and for at least the next few years.

Some of the most interesting use cases are concentrated in the areas of identifying individuals, groups of people, or behaviors of (groups of) people. For example:

• comScore works hard to identify individual web surfers – i.e. to deanonymize them — even though they may have given incomplete or false personal information.
• Other companies at least try to figure out which information in a user’s profile is unreliable, so as to classify them better. (Yes, there are 62-year-old video-game-obsessed Lady Gaga fans, but that’s generally not the way to bet.)
• Multiple telecom vendors try to identify who their most influential customers are (to a first approximation, they’re the ones most often called by the most people, but it surely gets more sophisticated than that). This information is then used to reduce churn, either by working hard to retain those users, or – if they do churn – to move very fast to retain the business from their friends.
• Other kinds of companies do similar kinds of analysis, to the extent that they have enough of a social graph to do so. (This application is a case where the term “social graph” is not a misnomer.)
• Turing detectives (I just coined that phrase) try to determine whether users are humans or bots.
• Central to detecting insurance fraud is identifying suspiciously close connections between claimants, service providers, and so on.
• Identifying groups of people is also important in flagging insider trading. Even more important are other kinds of analysis, along the lines of “is this normal innocent trading behavior?”
• Intelligence agencies try to detect networks of terrorists and their sympathizers. They further try to identify unusual patterns of communication or meetings along those networks that might indicate terrorist acts are being planned. (Civilian law enforcement agencies can use similar techniques.)

In most cases, the analysis and/or run-time execution of the relevant models is done with the help of analytic DBMS. Other technologies that come into play include non-DBMS MapReduce (Hadoop), graph engines, and CEP (Complex Event Processing). The vendor most heavily represented on that list is probably Aster Data, because:

• Aster Data is focused on hard-core analytics.
• I talk a lot with Aster Data, and in particular had a long, detailed use-cases discussion with them last week.
• The comScore example happens to come from a speaker at an Aster event I also participated in.

And by the way, all this only scratches the surface of what will be possible down the road. It’s based mainly on where you live, what you purchase, how you behave on websites, and who you communicate with. Other kinds of data, which could be used to be yet more intrusive, generally aren’t involved.

I actually have two points in drawing up this list. One is golly-gee-whiz about how a lot of analytically sophisticated applications are actually getting into production. The other is to highlight the privacy and liberty threats If This Goes On Unchecked (which is why I didn’t include some other less-people-focused examples). There’s also a related danger that, to the extent we don’t get some smart regulations to keep us safe(r), we’ll get a bunch of stupid regulations instead.

The Analytic Era has only just begun.

• Aster Data showed me a lot of customer names and deal sizes, across a bunch of industries (mainly enterprise rather than web). Yes, Aster’s market success is for real. (But almost all those details are NDA.)
• Sybase’s product plans for IQ are pretty impressive. (But the most interesting parts are, you guessed it, NDA.)
• I’ve kissed and made up* with ParAccel, now that they’ve replaced their CEO, replaced their marketing chief, and stopped the worst of the marketing nonsense I used to complain about. ParAccel has some interesting plans for ParAccel 3.0 which are, naturally, NDA.
• The Peoplesoft guys are doing it over again at Workday. Only this time, their platform isn’t a relational DBMS. Rather, it’s an in-memory, completely object-oriented data model, with disk used only on a “Just in case the power ever goes out” basis. (Thankfully, nothing at all about our conversation was NDA.)
• I’m finally feeling good about Northscale’s memcached-compatible persistent store Membase. The main reason is that they showed me a near-term path to interfaces that are richer than key-value. Also, Todd Hoff reassured me that even pure persistent memcached has a place.
• Rumor says that even the one app for which Facebook was using Cassandra — in-box search — has been decommissioned. On the other hand, numerous other scale-0ut DBMS (SQL or otherwise) seem to have Facebook footholds. But details are — all together now! — NDA.

*If you know ParAccel’s new marketing chief Michael Weir, you surely guessed I mean that only in a figurative sense.

From elsewhere:

• Daniel Abadi offered his analysis of Kickfire’s demise. In general I agree, but Daniel neglected to mention one hugely important factor — the chicken-egg negative effect of Kickfire’s lack of market or marketing traction. Customers were extremely reluctant to buy from Kickfire because they perceived, correctly, that Kickfire’s survivability was far from assured.
• While the InfiniDB forums suggest that there are at least a couple of production users of Calpont’s free InfiniDB, Calpont seemingly has a long way to go to be even as successful as Kickfire. But Calpont does have a bit of money to spend on lead generation; maybe some day they’ll even have actual customers.
• In a response to a question I messaged over, XtremeData tells me they have actual customers now. Press releases to follow.
• The admiration for the job Mark Hurd did at HP is in my opinion overstated. Sure, the financial/operational management appeared to work, but HP did little on Hurd’s watch to strengthen its reputation or customers’ loyalty. In particular:
  • HP’s analytics efforts have accomplished little.
  • HP’s data warehouse appliance efforts have failed pathetically.
  • From what I hear, HP’s execution in its Exadata partnership was not good.
  • HP’s server business in general is distinguished mainly by HP being a big company.
  • HP’s EDS acquisition has been rocky, not that EDS was sailing so smoothly on its own beforehand.
  • HP’s success in PCs amounts to “arguably, HP sucks a little less than the other guys”.
  • HP’s elite reputation is long gone (admittedly, for the most part that predates Hurd).
• Doug Henschen evidently favors really strong intellectual property protection for software, even forbidding plug-compatible reverse engineering. I agree with Doug up to the point that it should be forbidden to copy proprietary software, but I don’t see why he (or a court) would view such behavior as copying.

• This is starting off as a honeymoon deal. Everything Greenplum was planning to do is being continued. Additional resources are being poured into Greenplum to do more.
• Some Greenplum execs seem to envision staying long term, some seem to envision moving on to their next startups. The ones who envision moving on are, however, going to work hard first to make the merger a success.
• Greenplum has, for quite a while, had more of an advanced analytics/embedded predictive modeling story than I realized. Bad on them for not fleshing it out more in marketing and product packaging alike.
• Greenplum both denies the concurrency problems I previously noted and also has a very credible story as to how it will eliminate them. Seriously, Greenplum tells of one customer that routinely runs 150 simultaneous queries – on what I think is not a terribly big system — and a number of POCs (Proofs of Concept) that simulated similar levels of concurrency.

• Teradata is discontinuing Xkoto’s existing product Gridscale, which Scott characterized as being too OLTP-focused to be a good fit for Teradata. Teradata hopes and expects that existing Xkoto Gridscale customers won’t renew maintenance. (I’m not sure that they’ll even get the option to do so.)
• The point of Teradata’s technology + engineers acquisition of Xkoto is to enhance Teradata’s active-active or multi-active data warehousing capabilities, which it has had in some form for several years.
• In particular, Teradata wants to tie together different products in the Teradata product line. (Note: Those typically all run pretty much the same Teradata database management software, except insofar as they might be on different releases.)
• Scott rattled off all the plausible areas of enhancement, with multiple phrasings – performance, manageability, ease of use, tools, features, etc.
• Teradata plans to have one or two releases based on Xkoto technology in 2011.

Frankly, I’m disappointed at the struggles of clustering efforts such as Xkoto Gridscale or Continuent’s pre-Tungsten products, but if the DBMS vendors meet the same needs themselves, that’s OK too.

The logic behind active-active database implementations actually seems pretty compelling: 

• You may well be keeping a second copy of your database for high availability/hot standby.
• You might even be keeping a third copy for off-site disaster recovery.
• In some cases, you might have reasons beyond disaster recovery to distribute a database around the world.
• So why not allow queries to be run against all the copies?
• And by the way, splitting the workload up a bit by kinds (e.g., long-running vs. short query) might let you optimize the implementation of each copy of the database. (This last point becomes even more important with the rise of solid-state memory.)

Analytic DBMS vendors pretty much all need to offer this. (Possible exception: If they have a data-mart-only positioning so extreme that customers will never care about any form of failover.) That said, I must confess to not having done a good job of tracking who does or doesn’t have which features in this area to date; informative comments to this post in that regard would be much appreciated!