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As I was reading about Microsoft Azure recently, a military analogy came to my mind. Hypervisors are tanks. Application development and runtime platforms compose the air force.

Tanks (and more generally the mechanization of ground forces) transformed war in the 20th century. They multiplied the fighting capabilities of individuals and changed the way war was fought. A traditional army didn’t stand a chance against a mechanized one. More importantly, a mechanized army that used the new tools with the old mindset didn’t stand a chance against a similarly equipped army that had rethought its strategy to take advantage of the new capabilities. Consider France at the beginning of WWII, where tanks were just canons on wheels, spread evenly along the front line to support ground troops. Contrast this with how Germany, as part of the Blitzkrieg, used tanks and radios to create highly mobile – and yet coordinated – units that caused havoc in the linear French defense.

Exercise for the reader who wants to push the analogy further:

• Describe how Blitzkrieg-style mobility of troops (based on tanks and motorized troop transports) compares to Live Migration of virtual machines.
• Describe how the use of radios by these troops compares to the use of monitoring and control protocols to frame IT management actions.

Tanks (hypervisor) were a game-changer in a world of foot soldiers (dedicated servers).

But no matter how good your tanks are, you are at a disadvantage if the other party achieves air superiority. A less sophisticated/numerous ground force that benefits from strong air support is likely to prevail over a stronger ground force with no such support. That’s what came to my mind as I read about how Azure plans to cover the IaaS layer, but in the context of an application-and-data-centric approach. Where hypervisors are not left to fend for themselves based on the limited view of the horizion from the periscope of their turrets but rather orchestrated, supported (and even deployed) from the air, from the application platform.

(Yes, I am referring to the Azure vision as it was presented at PDC09, not necessarily the currently available bits.)

Does your Cloud vendor/provider need an air force?

Exercise for the reader who wants to push the analogy to the stratosphere:

• Describe how business logic/process, business transaction management and business intelligence are equivalent to satellites, surveying the battlefield and providing actionable intelligence.

The new Cloud stack (”military-cloud complex” version):

[Note: I have no expertise in military history (or strategy) beyond high school classes about WWI and WWII, plus a couple of history books and a few war movies. My goal here is less to be accurate on military concerns (though I hope to be) than to draw an analogy which may be meaningful to fellow IT management geeks who share my level of (in)expertise in military matters. This is just yet another way in which I try to explain that, for Clouds as for plain old IT management, "it's the application, stupid".]

New England Database Day has now, in its third year, become a “Summit.”  It’s a nice event, providing an opportunity for academics and business folks to mingle.  The organizers are basically the local branch of the Mike Stonebraker research tree, with this year’s programming head being Daniel Abadi. It will be on Thursday, January 28, 2010, once again in the Stata Center at MIT. It would be reasonable to park in the venerable 4/5 Cambridge Center parking lot, especially if you’d like to eat at Legal Seafood afterwards.

So far there are two confirmed speakers — Raghu Ramakrishnan of Yahoo and me.  My talk title will be something like “Database and analytic technology: The state of the union”, with all wordplay intended.

There’s more information at the official New England Database Summit website. There’s also a post with similar information on Daniel Abadi’s DBMS Musings blog.

My clients at Kickfire put out a press release last week quoting me as saying things I neither said nor believe. The press release is about a “Queen For A Day” kind of contest announced way back in April, in which users were invited to submit stories of their data warehouse problems, with the biggest sob stories winning free Kickfire appliances. The fabricated “quote” reads:

As we went through the contest entries in detail, it was readily apparent that today’s data warehousing solutions are either massively expensive or non-existent,” said Curt Monash, Founder of Monash Research. “Clearly, there is major dual-market opportunity for a product such as the Kickfire appliance that can not only provide an affordable data warehousing solution to small companies; but can also target larger companies that have made an initial investment in high-end solutions, yet still need to add some affordable query processing power in other areas of the organization.”

In reality:

• I spent a few minutes reviewing summaries of eight stories selected by Kickfire from the entrants, and emailed comments back to Kickfire about them. I have no further role to play in the contest.
• The part of the “quote” that slams Kickfire’s competitors is not reflective of my views.
• The “market opportunity” is in line with the positioning I’ve encouraged Kickfire to adopt. A good shorthand for it is the “Sybase IQ market.” In essence I see Kickfire as an interesting Sybase IQ alternative. But Sybase IQ is a formidable competitor, and there are many other competitors as well. This is hardly an untapped market ripe for Kickfire’s plucking.

I’m satisfied that this is all a case of lousy marketing execution – something Kickfire has a history of — rather than deliberate deception. Kickfire has recently turned over its VP of Marketing (twice) and PR resource (at least once). Scott Humphrey, Kickfire’s new outside PR guy, says he was incorrectly told by his predecessor that the press release and quote in question had been approved, and put it out without fact-checking. I believe him. I hope Kickfire CEO Bruce Armstrong will be able to add stronger marketing leadership soon. Bruce seems aware of the need, and is making reasonable marketing strategy decisions himself in the mean time, so there’s some basis for optimism.

And by the way – I don’t let vendors write press release quotes for me anyway. I let them edit in precise product names and so on, but otherwise the words are mine. The last occasion on which I recall bending this policy was inadvertent and over a year ago, when Greenplum emailed something to me — which was genuinely similar to my opinion — while I was on the phone with Aster at a particularly frenzied time, and I didn’t immediately realize the words weren’t my own.

Last month, Bob Zurek asked me to give a talk on “Big Data”, where “big” is anything from a few terabytes on up, then moderate a panel on cloud computing. We agreed that I could talk just from notes, without slides. So, since I have them typed up, I’m posting them below.

The top two points from Q&A probably were:

• Big Data and the cloud actually have relatively little to do with each other, a few exceptions notwithstanding, especially if the data is in a shared-nothing DBMS (as opposed to, say, a MapReduce-oriented file cluster). Two principal reasons are:
  • Redistributing data from node to node is a little slow, undermining some of the elasticity benefits of the cloud.
  • Getting data into the cloud in the first place is a lot slow.
• The NoSQL movement is a lot like the Ron Paul campaign — it consists of people who are dissatisfied with the status quo, whose dissatisfaction has a lot to do with insufficient liberty and/or excessive expenditure, and who otherwise don’t have a whole lot in common with each other.

Anyhow, here are my notes for the talk, edited in just a couple of places for readability or linkage.

Quick introduction

• Big Data vs. cloud
• How big is Big Data?
• At the low end of that range, there’s little you can’t do with conventional technology if you have:
  • An unlimited budget for hardware
  • An unlimited budget for software
  • An unlimited budget for people, especially Oracle DBAs

Big Data in OLTP

• Hard-core OLTP
  • Focus of DBMS technology for a long-time
  • Big budgets because each transaction has significant value
  • Tough to get users to change technologies
• Lighter-weight OLTP
  • Classic example = web companies
    • Big ones — retail-oriented ones (eBay, Amazon) partially excepted — rolled their own technology stacks
    • Reluctant to give money to anybody
      • Open source, etc.
  • Difficulty finding market
    • Product vs. feature
      • Clustering/HA/DR/whatever
      • Ditto cloud enablement
    • True products haven’t found much traction yet

Analytic Big Data use cases

• Kinds of data for analytics
  • More of same != big
  • More detail and/or new kinds
    • Complete data sets
    • Transactions
    • Call details
    • Tick/trade history
    • Web clickstreams
    • Network event logs
    • Other machine-generated data
    • CAM bottom line
      • Anything human-generated should and will be retained in its entirety
      • Quantities of machine-generated data retained should and will grow roughly in line w/ computing cost reductions (Moore’s Law, etc.)
• Analytic uses of Big Data
  • Analytics is mainly about three things
    • Problem detection
    • Customer relationship improvement
      • (Those overlap when the customer relationship is bad)
    • Financial statements on steroids
  • Main kinds of analytics
    • What BI vendors traditionally sell
      • General reporting and dashboards
      • Ad-hoc query (now driven from those reports and dashboards)
      • Planning (allegedly integrated with BI)
    • Research
      • Ad hoc relational query (worth mentioning twice because it drives so much of the market)
      • Data mining
      • Most web search and web mining
    • Operational/near-real-time
    • Archiving/compliance
  • What gets Big?
    • Mainly research and archiving
    • But when reporting or operational get Big, you have really interesting computing problems

Technology issues and trends

• Moore’s Law
  • CPUs — All about cores, hence parallelism is key
  • RAM
  • SSDs – hence replace disks
  • Sensors – hence generate lots more data
• Kryder’s Law
  • But rotational speeds up only 12.5X since Eisenhower Administration
  • Hence solid-state memory (or RAM) will soon take over
• In the mean time, I/O bottlenecks have had to beaten
  • Hence sequential scans
  • Hence index-light architectures
  • Hence columnar
• DBMS “overhead”
  • Raw license and maintenance fees – software increasing fraction of total
  • OLTP vestiges – locking and all that
  • DBAs
    • People costs = huge fraction of total
    • Index-lightness addresses
    • So does appliance
  • Many people don’t really know how to write SQL
• Configuration
  • Appliance/tightly-balanced
    • Netezza
    • Teradata earlier
    • Greenplum/Sun
    • Oracle
    • IBM
    • Microsoft/Madison
  • Commodity/do what you want
    • Vertica
    • Greenplum now
    • Infobright, Aster and others
    • MapReduce-oriented file systems
  • Extreme rigidity is silly
    • Teradata, Oracle have both signaled moving to more modularity
    • Big driver of that = heterogeneous storage
      • Cheap disk
      • Expensive disk
      • Solid-state
      • RAM
    • CPU/storage ratio is even more of a driver

Theoretically defensible ways to segment the market

• Latency requirements
  • High availability and low latency go together
• Query types
  • Simultaneous users for same
• Database size
• Budget

Actual segments right now

• Utter ADW/EDW
• Data mart
  • Size
  • Naturally columnar vs. naturally row-based
• Operational/frontline
• Less dramatic/smaller EDW

Things are heating up in the DMTF Cloud incubator. Back in September, VMWare submitted its vCloud API (or rather a “reader’s digest” version of it) to the group. Last week, the group released a white paper titled “Interoperable Clouds”. And a second submission, from Fujitsu, was made last week and publicly announced today.

The Fujitsu submission is called an “API design”. What this means is that it doesn’t tell you anything about what things look like on the wire. It could materialize as another “XML over HTTP” protocol (with or without SOAP wrapper), but it could just as well be implemented as a binary RPC protocol. It’s really more of an esquisse of a resource model than a remote API. The only invocation-related aspect of the document is that it defines explicit operations on various resources (though not their input and outputs). This suggest that the most obvious mapping would be to some XML/HTTP RPC protocol (SOAPy or not). In that sense, it stands out a bit from the more recent Cloud API proposals that take a “RESTful” rather than RPC approach. But in these days of enthusiastic REST-washing I am pretty sure a determined designer could produce a RESTful-looking (but contorted) set of resources that would channel the operations in the specification as HTTP-like verbs on these resources.

Since there are few protocol aspects to this “API design”, if we are to compare it to other “Cloud APIs”, it’s really the resource model that’s worth evaluating. The obvious comparison is to the EC2 model as it provides a pretty similar set of infrastructure resources (it’s entirely focused on the IaaS layer). It lacks EC2 capabilities around availability, security and monitoring. But it adds to the EC2 resource model the notions of VDC (”virtual data center”, a container of IaaS resources), VSYS (see below) and a lightly-defined EFM (Extended Function Module) concept which intends to encompass all kinds of network/security appliances (and presumably makes up for the lack of security groups).

The heart of the specification is the VSYS and its accompanying VSYS Descriptor. We are encouraged to think of the VSYS Descriptor as an extension of OVF that lets you specify this kind of environment:

Example content for a VSYS Descriptor

By forcing the initial VSYS instance to be based on a VSYS Descriptor, but then allowing the VSYS to drift away from the descriptor via direct management actions, the specification takes a middle-of-the-road approach to the “model-based versus procedural” debate. Disciples of the procedural approach will presumably start from a very generic and unconstrained VSYS Descriptor and, from there, script their way to happiness. Model geeks will look for a way to keep the system configuration in sync with a VSYS Descriptor.

How this will work is completely undefined. There is supposed to be a getVSYSConfiguration() operation which “returns the configuration information on the VSYS” but there is no format/content proposed for the response payload. Is this supposed to return every single config file, every setting (OS, MW, application) on all the servers in the VSYS? Surely not. But what then is it supposed to return? The specification defines five VSYS attributes (VSYSID, creator, createTime, description and baseDescriptor) so I know what getSYSAttributes() returns. But leaving getVSYSConfiguration() undefined is like handing someone an airplane maintenance manual that simply reads “put the right part in the right place”. A similar feature is also left as an exercise to the reader in section that sketches an “external configuration service”. We are provided with a URL convention to address the service, but zero information about the format and content of the configuration instructions provided to the VServer.

EC2 has a keypair access mechanism for Linux instances and a clumsy password-retrieval system for Windows instances. The Fujitsu proposal adopts the lowest common denominator (actually the greatest common divisor, but that’s a lost rhetorical cause): random password generation/retrieval for everyone.

I also noticed the statement that a VServer must be “implemented as a virtual machine” which is an unnecessary constraint/assumption. The opposite statement is later made for EFMs, which “can be implemented in various ways (e.g. run on virtual machines or not)”, so I don’t want to read too much into the “hypervisor-required” VServer statement which probably just needs an editorial clean-up.

From a political perspective this specification looks more like a case of “can I play with you? I brought some marbles” than a more aggressive “listen everybody, we’re playing soccer now and I am the captain”. In other words, this may not be as much an attempt to shape the outcome of the incubator as much as to contribute to its work and position Fujitsu as a respected member whose participation needs to be acknowledged.

While this is an alternative submission to the vCloud API, I don’t think VMWare will feel very challenged by it. The specification’s core (VSYS Descriptor) intends to build on OVF, which should be music to VMWare’s ears (it’s the model, not the protocol, which is strategic). And it is light enough on technical details that it will be pretty easy for vCloud to claim that it, indeed, aligns with the intent of this “design”.

All in all, it is good to see companies take the time to write down what they expect out of the DMTF work. And it’s refreshing to see genuine single-company contributions rather than pre-negotiated documents by a clique. Whether they look more like implementable specifications of position paper, they all provide good input to the DMTF Cloud incubator.

PaaS can most dramatically improve the IT experience in four areas:

• Hosting/operations efficiency
• Application-centric management
• Development productivity
• Security

To do so, there are technical characteristics that PaaS frameworks should eventually exhibit. These are not technical characteristics of a given PaaS container, they are shared characteristics that go across all container types, no matter what the operational capabilities of the containers are.

Here is a rough and unorganized list of the desirable characteristics (meta-capabilities) of PaaS Cloud containers:

• An application component model that supports deployment/configuration across all PaaS container types.
• Explicit interactions/invocations between application components (resilient connections between component: infrastructure-level retry/reroute)
• Uniform and consistent request tracking across all components. Ability to intercept component-to-component communication.
• Short-term (or externally persisted) state so that all instances can be quickly redirected out of any one node.
• Subset of platform management interface exposed to consumer, along with out of the box application management. Application metrics consolidated at application level rather than node level.
• Consistent, model-based application management interface across all container types. Hooks for component code to provide its manageability in the same framework.
• Minimal footprint of any container node for limited patching requirements.
• Assistance for debugging platform-hosted code (see this entry).
• No encroachment of container technology on application contract (e.g. no forced URL structure).
• Application uniformly scalable to the limit of the underlying hardware (no imposed partitioning).
• Shared authentication / authorization / auditing across containers.
• Minimum contract/interface exposed by each container.
• Governance of application services, aligned (in model/protocols) with the container management interfaces.
• [UPDATE: need to add metering+billing as William Louth pointed out in a comment]

This applies across the board to public, private and hybrid PaaS. The distinctions between these delivery models are real but at a different level. The important thing is that the PaaS administrator is different from the application administrator in all cases. On the other hand, most of these technical characteristics are not achievable for lower-level Cloud resources (like virtual hosts and low-level storage) which is why the IaaS form of Cloud leaves the Cloud promise only partially fulfilled.

What do we need from an on-demand application platform for enterprise software? Here is a proposed classification of container types on which you can create/scale/manage applications. Your application is made of modules that run on these containers (e.g. one app may have two “synchronous web request” modules, one “structured data service” module and one “scheduled job” module). All tied together using an application component model that dovetails with the capabilities of the different containers. The proposed containers map to the following capabilities:

• Synchronous web request processing
• Long-lived (persisted) process execution with introspectable/declarative flow
• Event processing
• Persistence (a few different types: structured vs. buckets/files vs. object cache)
• Batch/background/scheduled/queued processing
• Possibly some advanced Web/mobile UI (portals, human task flows…)
• User management (self-contained or as an abstraction layer for other systems)
• [UPDATED 2009/11/19: I should have included pluggable protocol/format adapters.]

While new applications may be built to run purely on such a platform, you will not be able to run your IT solely on it anytime in the foreseeable future. So if you are thinking about it from the perspective of the entire universe of virtualization containers needed to support your IT system, you’ll need to add lower-level container types, such as:

• Guest hosts (typically from hypervisors)
• Low-level (e.g. block) storage
• Networking between them

Another way to think about it is that Cloud/Utility computing is about having pools of resources dynamically allocated. There needs to be few enough pools that each pool is large enough and used across enough consumers to derive efficiencies from the act of sharing. But there needs to be enough pool types that applications have a complete infrastructure to run on that lets them abstracts away what is not business logic. This list is an attempt at this middle ground.

This is a classification of containers, not a description. There are many different ways to realize each of them. The synchronous  web request processing could look like a servlet engine, a Python handler or a PHP page. The persisted execution could be a BPEL engine or some other state machine definition/execution engine. The structured data interface could look like SQL, XQuery or SPARQL, etc… In addition, more specialized application infrastructure elements (e.g. video streaming, analytics) might enter the picture for some applications.

I hope to see the discussion move beyond “IaaS vs. PaaS” towards talking about more specific container types that are needed/supported by the different virtualization stacks. My application doesn’t care if the file container is considered IaaS or PaaS.

The next post will list desired characteristics of the PaaS environment (meta-capabilities that go across all the container types in the first list but may not be available from the lower-level containers in the second list).

Since its inception, Calpont has gone through multiple management teams, strategies, and investor groups. What it hadn’t done, ever, is actually shipped a product. Last week, however, Calpont introduced a free/open source DBMS, InfiniDB, with technical details somewhat reminiscent of what Calpont was promising last April. Highlights include:

• 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.)

Andi Mann recently wrote an interesting post about virtual appliances . He uses the domain name pleasediscuss.com for his blog so I figured I’d do just that. More specifically, I have three comments on his article.

Opaque or transparent appliance

Andi’s concerns about the security and management problems posed by virtual appliances are real, but he seems to assume that the content of the appliance is necessarily opaque to the customer and under the responsibility of the appliance provider. Why can’t a virtual appliance be transparent in the sense that the customer is able to efficiently manage at least some aspects of the software installed on it? “You can’t put agents on most virtual appliances, they don’t come with WMI, and most have only a GUI for management” says Andi. Why can’t an appliance come with an agent (especially in these days of consolidation where many vendors provide many layers of the stack – hypervisor / OS / application container / application / management tools – including their agent)? Why can’t it implement a standard management API (most servers nowadays implement WBEM, WS-Management and/or IPMI pre-boot – on the motherboard – which is a lot more challenging to do than supporting a similar protocol in a virtual appliance). Andi is really criticizing the current offering more than the virtual appliance model per se and in this I can join him.

Let me put it differently, since this is probably just a question of definition: what would Andi call a virtual appliance that does expose management APIs for its infrastructure (e.g. WS-Management for the OS, JMX for the java stack) or that comes with an agent (HP, IBM, BMC, Oracle…) installed on it?

Such an appliance (let’s call it a “transparent virtual appliance” for now) doesn’t provide all the commonly claimed benefits of an appliance (zero config/admin) but as Andi points out these benefits come with major intrinsic drawbacks. A transparent virtual appliance still drastically simplifies installation (especially useful for test/dev/demo/POC). It doesn’t entirely free you of monitoring and configuration but at least it provides you with a very consistent and controlled starting point, manageable from the start (no need to subsequently install an agent). In addition, it can be made “just enough” (just enough OS, just enough app server…) to require a lot less maintenance than an application stack that you assemble yourself out of generic parts. We’ll always have trade offs between how optimized/customized it is versus how uniform your overall environment can be, but I don’t see the use of an appliance as a delivery mechanism as necessarily cornering you into a completely opaque situation, from a management perspective.

Those who attended Oracle Open World a few weeks ago were treated to an example of such an appliance, if they attended any of the sessions that covered Oracle’s Appliance Builder (the main one was, I believe, Virtualizing Oracle Fusion Middleware in the Modern Data Center, in case you have access to the Open World On Demand replay and slides). I believe it’s probably the same content that @jayfry3 was shown when he tweeted about “Oracle is demoing their private cloud self-service app”. These appliances are not at all opaque from a management perspective. To the contrary, they are highly manageable, coming with an Enterprise Manager agent installed that can manage everything in the appliance (and when that “everything” doesn’t include the OS, it’s because there isn’t one thanks to JRockit Virtual Edition, making things slimmer, faster, safer and more manageable). And of course the OVM-based environment in which you deploy these appliances is also managed by Enterprise Manager. OK, my point here wasn’t to go into marketing mode, but this is cool stuff and an example of what virtual appliances should be. BTW, this was also demonstrated during Hasan Rizvi’s keynote at OpenWorld, including the management of these systems through Enterprise Manager.

In the long run it’s irrelevant

As with all things computer-related, the issue is going to get blurrier and then irrelevant . The great thing about software is that there is no solid line. In this case, we will eventually get more customized appliances (via appliance builders or model-driven appliance generation) blurring the line between installed software and appliance-based software.

Waiting for PaaS

Towards the end of his post, Andi paints an optimistic vision of the future: “I also think that virtual appliances have a bright future – but in some ways I continue to see them as a beta version of what could (or should) come next.  By adding in capabilities for responsible and accountable management, they could form the basis of more fully-functional virtual service management containers. These in turn could form the basis of elastic, mobile, network-deployed, responsible cloud appliances that deliver complete end-to-end service management without regard to physical location or domain of control.”

I mostly agree with this vision, though when I describe it it is in the guise of a PaaS platform. Where your appliance (which today goes from the OS all the way to the app) has shrunk to an application template that you deploy in the PaaS environment (rather than in a hypervisor). If/when the underlying PaaS environment has reached the right level of management automation you get all the benefits of an appliance while maintaining the consistency of your environment and its adherence to your management policies (because the environment is the PaaS platform and its management is driven from your policies).

[As is often the case, this started as a comment (on Andi's blog) and quickly outgrew that environment, leading to this new post. Plus, Andi's blog is brand new and seems to be well worth spreading the word about (Andi himself is under-marketing it).]

Since Linda and I are leaving on vacation in a few hours, Aster Data graciously gave me permission to morph its “12:01 am Monday, November 2” embargo into “late Friday night.”

Aster Data is officially announcing the 4.0 release of nCluster. There are two big pieces to this announcement:

• Aster is offering a slick vision for integrating big-database management and general analytic processing on the same MPP cluster, under the not-so-slick name “Data-Application Server.”
• Aster is also offering a sophisticated vision for workload management.

In addition, Aster has matured nCluster in various ways, for example cleaning up a performance problem with single-row updates.

Highlights of the Aster “Data-Application Server” story include:

• At its core, the Aster “Data-Application Server” is the Aster nCluster MPP analytic DBMS, enhanced with basic application server functionality (I didn’t ask for details of that part), running on the same nCluster worker nodes that answer SQL queries.
• Thus, Aster is eliminating a lot of the data movement that plagues three-tier architectures and other less-integrated approaches.
• The Aster “Data-Application Server” further offers integrated workload management for applications and queries; more on that below.
• The Aster “Data-Application Server” requires applications to be parallelized and invoked via Aster’s SQL/MapReduce.
• As befits a MapReduce-based system, the Aster “Data-Application Server” lets you write your applications in lots of different languages (the usual suspects, and it also does .NET).
• The Aster “Data-Application Server” runs applications in their own process spaces, protecting the DBMS server from crashes and other damaging behavior.
• The Aster “Data-Application Server” allows applications to manage memory themselves, persistently, and not just via relational constructs. Thus, if you want your application to maintain a graph, mini rules engine, and/or finite state machine, you can, without doing SQL contortions.

In a compelling proof point for the Aster Data-Application Server’s slickness, Aster has leapfrogged Teradata and Netezza in the extent to which SAS functionality is integrated into Aster’s DBMS. (Aster and SAS both say that you can do full SAS modeling in parallel on Aster, but even so I wouldn’t be surprised to discover there were some parts of SAS’ system that turned out to be exceptions.) Of course, Aster is hardly the only analytic DBMS vendor to have the idea of explicitly enhancing general analytic processing; that’s why we see lots of MapReduce announcements, and it’s also why Teradata enhanced its UDFs (User-Defined Functions) to have some kind of persistent memory.* But I don’t know of anybody else whose approach is quite so elegant and general at this time.

*Unfortunately, I don’t yet know much about Teradata’s UDF enhancements. I neglected to drill down on Global Persistent Memory when it was mentioned a couple of times at Teradata Partners last week, and Teradata was unable to accommodate my request this week for a rapid follow-up briefing on the subject.

Aster’s approach to workload management is similarly stylish. The idea is:

• Lots of variables are available to be taken into account (e.g., user role, expected query duration, actual duration of a running query, etc.)
• SQL statements can be written against any of these variables.
• The SQL statements serve as rules to set query/task priorities.
• There seem to be a few different ways to measure priority, including explicit allocation of CPU or I/O resources, as well as more conventional “This group of queries is gets higher priority than that one” kinds of metrics.
• The whole thing provides integrated workload management for queries, applications, load jobs, data redistribution, and so on.

Right now the interface is – well, you’re manipulating a SQL table. A more conventional workload management GUI is slated for the second quarter of 2010.

Discussing subjects such as mirroring and ILM (Information Lifecycle Management) with Aster can be tricky, as Aster uses the word “partition” in confusing ways. Anyhow, Aster has a few different levels of compression, and the ability to apply different levels of compression to different partitions, to change compression levels via ALTER TABLE, and to alter (presumably increase) compression on the fly when doing online backup. Aster is also part of a growing trend to eschew RAID, instead doing mirroring in its own software. (Other examples of this strategy would be Vertica, Oracle Exadata/ASM, and Teradata Fallback.) Prior to nCluster 4.0, this caused a problem, in that the block sizes for mirroring were so large as to create a lag in transactional updating. But Aster says this problem is now solved, and indeed claims that nCluster 4.0 is superior to most rivals in transactional efficiency.

And finally, while I was talking w/ Aster Data anyway, I checked up on cloud and MapReduce customer penetration. The answers were:

• Aster has two serious production cloud users, both of which have been disclosed for a while, namely:
  • ShareThis, which runs Aster nCluster on Amazon EC2
  • Didit, which runs Aster nCluster on AppNexus
• Outside of those two, Aster sees some cloud use for test, development, prototyping, etc.
• Every single Aster customer uses SQL/MapReduce — i.e., they invoke MapReduce via Aster nCluster SQL queries.
• Some of those customers use MapReduce for ETL, some use it for actual analytics.

An enterprise user wrote in with a question that boils down to:

What are reasonable MDX performance expectations?

MDX doesn’t come up in my life very much, and I don’t have much intuition about it. E.g., I don’t know whether one can slap an MDX-to-SQL converter on top of a fast analytic RDBMS and go to town. What’s more, I’m heading off on vacation and don’t feel like researching the matter myself in the immediate future.

So here’s the long form of the question. Any thoughts?

I have a general question on assessing the performance of an OLAP technology using a set of MDX queries. I would be interested to know if there are any benchmark MDX performance tests/results comparing different OLAP technologies (which may be based on different underlying DBMS’s if appropriate) on similar hardware setup, or even comparisons of complete appliance solutions. More generally, I want to determine what performance limits I could reasonably expect on what I think are fairly standard servers.

In my own work, I have set up a star schema model centered on a Fact table of 100 million rows (approx 60 columns), with dimensions ranging in cardinality from 5 to 10,000. In ad hoc analytics, is it expected that any query against such a dataset should return a result within a minute or two (i.e. before a user gets impatient), regardless of whether that query returns 100 cells or 50,000 cells (without relying on any aggregate table or caching mechanism)? Or is that level of performance only expected with a high end massively parallel software/hardware solution? The server specs I’m testing with are: 32-bit 4 core, 4GB RAM, 7.2k RPM SATA drive, running Windows Server 2003; 64-bit 8 core, 32GB RAM, 3 Gb/s SAS drive, running Windows Server 2003 (x64).

I realise that caching of query results and pre-aggregation mechanisms can significantly improve performance, but I’m coming from the viewpoint that in purely exploratory analytics, it is not possible to have all combinations of dimensions calculated in advance, in addition to being maintained.

The W3C released OWL 2 today. Most readers of this blog are IT management people (whether they call it “cloud computing” or “boring old system management”) and don’t follow RDF, OWL, SPARQL etc too closely (if at all). Yet there is a lot of potential value in using these technologies for IT management, so I thought it might be helpful to provide some practical resources on the topic. I have selected articles that cover the special (some may say “twisted”) approach of using OWL and its friends for validation rather than just inference, as this use case is very relevant to IT management.

• Dave Reynolds’ overview of OWL2, with a focus on vocabularies and validation.
• A good series of articles from Clark&Parsia, starting with this one.
• SPIN (which I blogged about before), from the TopQuadrant guys, who produce the TopBraid tool (their free edition is a great way to get started using Semantic Web technologies).

Of course you can also go to the W3C standard itself, starting with the overview of OWL 2.

Just so you don’t feel lonely if you decide to explore this path, have a look at Elastra’s sexy technology stack. ECML, EDML and EMML are all defined as OWL ontologies.

You know what I like the best about OSGi? That it doesn’t put the bar too high for architects. At first I was a bit intimidated by the size  (338 pages for the “core specification”, 862 pages for the “service compendium”) and the fact that I had to look up “compendium”. But then they put me right at ease:

“Architects should focus on the introduction of each subject. This introduction contains a general overview of the subject, the requirements that influenced its design, and a short description of its operation as well as the entities that are used. The introductory sections require knowledge of Java concepts like classes and interfaces, but should not require coding experience.”

I am like so totally overqualified for my job. Hell, I even know what packages are.

(from the recently released OSGi version 4.2.)

As the pun goes, Teradata’s cloud strategy is – well, it’s somewhat nebulous. More precisely, for the foreseeable future, Teradata’s cloud strategy is a collection of rather disjointed parts, including:

• What Teradata calls the Teradata Agile Analytics Cloud, which is a combination of previously existing technology plus one new portlet called the Teradata Elastic Mart(s) Builder. (Teradata’s Elastic Mart(s) Builder Viewpoint portlet is available for download from Teradata’s Developer Exchange.)
• Teradata Data Mover 2.0, coming “Soon”, which will ease copying (ETL without any significant “T”) from one Teradata system to another.
• Teradata Express DBMS crippleware (1 terabyte only, no production use), now available on Amazon EC2 and VMware. (I don’t see where this has much connection to the rest of Teradata’s cloud strategy, except insofar as it serves to fill out a slide.)
• Unannounced (and so far as I can tell largely undesigned) future products.

Teradata openly admits that its direction is heavily influenced by Oliver Ratzesberger at eBay. Like Teradata, Oliver and eBay favor virtual data marts over physical ones. That is, Oliver and eBay believe that the ideal scenario is that every piece of data is only stored once, in an integrated Teradata warehouse. But eBay believes and Teradata increasingly agrees that users need a great deal of control over their use of this data, including the ability to import additional data into private sandboxes, and join it to the warehouse data already there.

The Teradata Elastic Mart(s) Builder Viewpoint portlet automates the inclusion of outside data. If you’re already an authorized Teradata data warehouse user, you can fill in a very short form (three or so fields) and add authorization to import outside data, e.g. from a .CSV file. No fuss, little bother. Trivial as that sounds, when you combine it with Teradata’s pre-existing robust workload management tools, it creates a pretty good virtual data mart story.

Spinning out and maintaining consistency with physical data marts is a different matter. Teradata doesn’t seem too sure it believes in those. And while Teradata is obviously planning to increase its capability in that regard anyway, I didn’t get a lot of detail beyond the reference to Data Mover 2.0.

Related links

• My Greenplum-inspired post on the future of data marts, outlining issues in “private cloud” data warehousing.
• eBay’s “Analytics as a Service” pitch (about 1 ½ years old)
• A post by Teradata’s Dan Graham explaining the Teradata Agile Analytics Cloud and Elastic Mart(s) Builder Viewpoint portlet
• Home page and complete screen shot for the Teradata Elastic Mart(s) Builder Viewpoint portlet

In my opinion, the most important takeaways about Teradata’s hardware strategy from the Teradata Partners conference last week are:

• Teradata’s future lies in solid-state memory. That’s in line with what Carson Schmidt told me six months ago.
• To Teradata’s surprise, the solid-state future is imminent. Teradata is 6-9 months further along with solid-state drives (SSD) than it thought a year ago it would be at this point.
• Short-term, Teradata is going to increase the number of appliance kinds it sells. I didn’t actually get details on anything but the new SSD-based Blurr, but it seems there will be others as well.
• Teradata’s eventual future is to mix and match parts (especially different kinds of storage) in a more modular product line. Teradata Virtual Storage is of pretty limited value otherwise. I probably believe Teradata will go modular more emphatically than Teradata itself does, because I think doing so will meet users needs more effectively than if Teradata relies strictly on fixed appliance configurations.
  

In addition, some non-SSD componentry tidbits from Carson Schmidt include:

• Teradata really likes Intel’s Nehalem CPUs, with special reference to multi-threading, QuickPath interconnect, and integrated memory controller. Obviously, Nehalem-based Teradata boxes should be expected in the not too distant future.
• Teradata really likes Nehalem’s successor Westmere too, and expects to be pretty fast to market with it (faster than with Nehalem) because Nehalem and Westmere are plug-compatible in motherboards.
• Teradata will go to 10-gigabit Ethernet for external connectivity on all its equipment, which should improve load performance.
• Teradata will also go to 10-gigabit Ethernet to play the Bynet role on appliances. Tests are indicating this improves query performance.
• What’s more, Teradata believes there will be no practical scale-out limitations with 10-gigabit Ethernet.
• Teradata hasn’t decided yet what to do about 2.5” SFF (Small Form Factor) disk drives, but is leaning favorably. Benefits would include lower power consumption and smaller cabinets.
• Also on Carson’s list of “exciting” future technologies is SAS 2.0, which at 6 gigabits/second doubles the I/O bandwidth of SAS 1.0.
• Carson is even excited about removing universal power supplies from the cabinets, increasing space for other components.
• Teradata picked Intel’s Host Bus Adapters for 10-gigabit Ethernet. The switch supplier hasn’t been determined yet.

Let’s get back now to SSDs, because over the next few years they’re the potential game-changer. The big news on SSDs is that after last year’s Teradata Partners conference, a stealth supplier* introduced itself and convinced Teradata it offers really great SSD technology. For example, not a single SSD it has provided Teradata has ever failed. (In hardware, that is. There have of course been firmware bugs, suitably squashed.) I think SSD performance is also exceeding Teradata’s expectations. This supplier is where the 6-9 month time-to-market gain comes from.

*Based on how often the concept of “stealth” and “name is NDAed” came up, I do not believe this is the SSD company another vendor told me about that is going around claiming it has a Teradata relationship.

Teradata SSD highlights include:

• I/O speeds on “random medium blocks” are 520 megabytes/second, vs. 15 MB/second on their fastest disks. And that’s limited by SAS 1.0, load-balanced across two devices, not the hardware itself. (2 x 300+ MB/sec turns out to be 520 MB/sec in this case.) No wonder Carson is excited about SAS 2.0.
• Teradata is using SAS interfaces for its SSDs, and believes that’s unusual, in that other companies are using SATA or Fibre Channel.
• Never having had a part fail, Teradata has no real basis to make MTTF (Mean Time To Failure) estimates for its SSDs.
• Teradata’s SSD appliance design includes no array controllers. The biggest reason is that right now array controllers can’t keep up with the SSDs’ speed.
• In its SSD appliance, Teradata has abandoned RAID, doing mirroring instead via a DBMS feature called Fallback that’s been around since Teradata’s earliest days. (However, unlike Oracle in Exadata, Teradata continues to use RAID for disks.)
• Useful life for Teradata’s SSDs is estimated at 5-7 years.
• Teradata’s SSDs are SLC (Single-Level Cell), as opposed to MLC (Multi-Level Cell).

Data warehouse appliance and software appliance vendors like to claim that they’ve worked out just the right hardware configuration(s), and that a single configuration is correct for a fairly broad range of workloads. But there are a lot of reasons to be dubious about that. Specific vendor evidence includes:

• Teradata ascribes considerable importance to a Virtual Storage technology whose main purpose is to allow mixing of heterogeneous storage devices in a single system. And the discussion rarely suggests that these parts will be in a rigid fixed relationship.
• Netezza — as Teradata keeps reminding me — often sells boxes with the expectation that they won’t be filled with data, so as to increase spindle count and hence performance.
• Oracle/Sun have dropped some comments about Exadata being more flexibly configured going forward.
• Kickfire’s new “high-end” appliance lets you attach fairly arbitrary amounts of external storage.
• And of course, software-only analytic DBMS vendors run their software in all sorts of hardware and storage environments.

What’s more, the claim never made a lot of sense anyway. With the rarest of exceptions, even a single data warehouse’s workload will contain different queries that strain different parts of the system in different ratios. Calculating the “ideal” hardware configuration for that single workload would be forbiddingly difficult. And even if one could calculate it, it almost surely would be different than another user’s “ideal” configuration. How a single hardware configuration can be “ideally balanced” for a broad class of use cases boggles the imagination.

As a recovering “design by committee” offender I have to be careful when lurking near standards groups mailing list, for fear my instincts may take over and I might join the fray. But tonight a few tweets containing alluring words like “header” and “metadata” got the better of me and sent me plowing through a long and heated discussion thread in the OGF OCCI mailing list archive.

I found the discussion fascinating, both from a technical perspective and a theatrical perspective.

Technically, the discussion is about whether to use HTTP headers to carry “metadata” (by which I think they  mean everything that’s not part of the business payload, e.g. an OVF document or other domain-specific payload). I don’t have enough context on the specific proposal to care to express my opinion on its merits, but what I find very interesting is that this shines another light on the age-old issue of how to carry non-payload info when designing a protocol. Whatever you call these data fields, you have to specify (by decreasing order of architectural importance):

• How you deal with unknown fields: mustUnderstand or mustIgnore semantics.
• How you keep them apart (prevent two people defining fields by the same name, telling different versions apart).
• How you parse their content (and are they all parsed in the same manner or is it specific to each field).
• Where they go.

SOAP provides one set of answers.

• You can tag each one with a mustUnderstand attribute to force any consumer who doesn’t understand them to fault.
• They are namespace-qualified.
• They are XML-formatted.
• They go at the top of the XML doc, in a section called the SOAP header.

You may agree or not with the approach SOAP took, but it’s important to realize that at its core SOAP is just this: the answer (in the form of the SOAP processing model) to these simple questions (here is more about the SOAP processing model and the abuses it has suffered if you’re interested). WSDL is something else. The WS-* stack is also something else. It’s probably too late to rescue SOAP from these associations, but I wanted to point this out for the record.

Whatever you answer to the four “non-payload data fields” questions above, there are many practical concerns that you have to consider when validating your proposal. They may not all be relevant to your use case, but then explicitly decide that they are not. They are things like:

• Performance
• Ability to process in a stream-based system
• Ease of development (tool support, runtime accessibility…)
• Ease of debugging
• Field length limitations
• Security
• Ability to structure the data in the fields
• Ability to use different transports (way overplayed in SOAP, but not totally irrelevant either)
• Ability to survive intermediaries / proxies

Now leaving the technology aside, this OCCI email thread is also interesting from a human and organizational perspective. Another take on the good old Commedia dell standarte. Again, I don’t have enough context in the history of this specific group to have an opinion about the dynamics. I’ll just say that things are a bit more “free-flowing” than when people like my friend Dave Snelling were in charge in OGF. In any case, it’s great that the debate is taking place in public. If it had been a closed discussion they probably would not have benefited from Tim Bray dropping in to share his experience. On the plus side, they would have avoided my pontifications…

I enjoyed finishing reading The Atlantic with Barbara Wallraff’s “Word Fugitives” column every month. Until earlier this year, when it was replaced  with Jeffrey Goldberg’s attempts at humor. For old time sake, I am borrowing the “Word Fugitive” format and applying it to the world of blogs and tweets. Here is a list of blog/twitter situations for which “there should be a word”.

#1 The ego-crushing realization, in the course of a face to face conversation covering topics you’ve written about, that the other person has not read your blog/tweets on this. Even though the first thing they told you when you met 10 minutes earlier is that they love your blog.

Candidate: followimp (from Shlomo).

#2 Conversely when someone brings up in the conversation something you wrote and had forgotten you did (maybe we need two words here, one if you are happy to be reminded of this and one if you’d rather not have been).

Candidates: twegreat and twegrets, respectively (from Shlomo).

#3 Seeing the corner of the blogo-twitto-sphere where you hang out light up in response to someone’s post even though you wrote up the same thing two years ago. At least you were trying to explain the same thing, but your brilliance went unnoticed.

Candidate: deja-lu.

#4 The frustrating (for system modelers at least) intermixing of data (your text) and metadata (e.g. the identification of the tweet you are responding to) in Tweeter conversations.

Candidate: metamess.

#5 (This one comes from @Beaker) The art of carving up tweets from others to be able to retweet them in 140 characters.

Hoff has a suggestion: Twexter (Twitter + Dexter).

#6 The art of guessing early the Twitter #hashtag that will emerge as a winner for a given topic.

Candidate: foretweetude.

#7 The frustration of having too many blog drafts and no time to write them up.

Candidate: blocrastination. And Neil WD offered logjam in the comments.

#8 (added on 2009/10/22 after seeing this) The feeling of nakedness one has while his/her blog is offline.

Candidate: e-vanescence.

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)

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

Teradata tells me that its press embargoes are ending at 9:00 this morning. Here are some highlights of what’s going on, although names, dates, and details will have to await conversations and press releases this week.

• Teradata is productizing “private cloud,” under names including “Teradata Enterprise Analytics Cloud,” “Teradata Agile Analytics Cloud,” and “Teradata Elastic Mart Builder.” I.e., Teradata hopes to leapfrog Greenplum in its “Enterprise Data Cloud” strategy. This is only fair, in that Greenplum lifted the idea from Teradata and eBay in the first place. It also provides major support for what I think is an extremely sensible trend. Give or take issues of who announces and ships what a couple months before or after a competitor, my early thinking is that the main differences between Greenplum and Teradata in this regard will be:
  • Virtual as opposed to just physical data marts, based on robust workload management software. (Advantage: Teradata)
  • Pricing, deployment options. (Advantage: Greenplum)
  • Features that don’t directly relate to enterprise/private cloud. (Advantage: Either, often Teradata.)
• Teradata is generally strengthening its data movement technology, e.g. for making various appliances work in sync. I’m not too clear yet on the details of that. I think this is what Teradata’s phrase “ecosystem management” refers to.
• Teradata is (pre-)announcing – at least as a statement of direction — an appliance based on solid-state drives (SSDs). I’ve thought for a while that Teradata was a leader in thinking through the issues around solid-state memory in data warehousing, so it makes sense that they’re among the leaders in actually coming to market as well. I plan to say more after meeting with, e.g., Carson Schmidt.
• Teradata has achieved a 300%ish speed-up in geospatial processing. I gather this is largely a byproduct of the parallel analytics work Teradata did around strengthening its SAS integration. However, there don’t seem to be a lot of Teradata geospatial users yet.
• Teradata Express, Teradata’s free Windows-based crippleware, is being ported to Amazon EC2 and VMware as well. Presumably to avoid cannibalizing Teradata product sales, there are quite a few limitations on Teradata Express, including system capacity, database size, and “no production use.”
• Teradata continues to extend its optimizations to handle queries issued by business intelligence tools. Previously, the focus of what Teradata discussed in this regard was query rewrite. But soon automatic recommendation and creation of Aggregate Join Indexes – i.e.., materialized views – will be included as well.