Yesterday, Intel and Micron announced a generational step forward in NAND Flash Write I/O performance.  From the Intel Press release:

The new high speed NAND can reach speeds up to 200 megabytes per second (MB/s) for reading data and 100 MB/s for writing data, achieved by leveraging the new ONFI 2.0 specification and a four-plane architecture with higher clock speeds. In comparison, conventional single level cell NAND is limited to 40 MB/s for reading data and less than 20 MB/s for writing data.

They don’t actually say it’s an SLC device but they compare it to SLC and it has the typical wear characteristics of SLC (100,000 cycles).  More data from the Micron web site:

Expect shipments in the latter half of 2008. We should start seeing interesting applications of this technology in SSDs and other devices this year.

Intel Press Release: http://www.intel.com/pressroom/archive/releases/20080201corp.htm

More data from Micron: http://www.micron.com/products/nand/high_speed/index

                                                --jrh

James Hamilton, Windows Live Platform Services
Bldg RedW-D/2072, One Microsoft Way, Redmond, Washington, 98052
W:+1(425)703-9972 | C:+1(206)910-4692 | H:+1(206)201-1859 | JamesRH@microsoft.com

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I saw a video earlier today titled “Great Ideas are a Dime a Dozen” and I just loved it. Unfortunately it’s a Microsoft internal-only video so I can’t post it here but I can point to some related talks and videos. The speaker was Bill Buxton of Microsoft research.

I fell in love with this talk for a variety of reasons: 1) I love and agree with the principle that ideas are cheap but it’s the communicating of the ideas and making them real that is truly hard and where the greatest talent is required. 2) He argues that you need to get a user experienced running quickly and you need to keep it evolving quickly. You need a lightweight experimentation platform to take the user experience from good to great.  I’ve long believed that the difference between the iPhone and some other designs is not being satisfied when it’s “done” and, rather than triaging to ship, just keep polishing.  Get it running, then get it better. Then throw it out and try again.  Change it some more.  Get it 100% functionally correct and as good as you can possibly get it. Then keep polishing. Polish and refine further, and 3) he points out that we never have time to properly invest in design at the beginning when the team is small. Yet, we DO have time to be months or even years late partly as a consequence of not doing the design up front.  Late projects are when the team is fully staffed and at its biggest and most expensive.  Neither he nor I are arguing for waterfall design.  What’s Bill is arguing for is human centric design up front.  Ray Ozzie calls this experience-first design.  Invest in really getting the experience fully understood with super lightweight development methods.  If you REALLY understand the user experience and it’s really right, developing the product may be the easiest and perhaps most predictable part of the process.  I’ve seen large software teams working on an ill-defined and only barely designed products more than once.  As an industry, we need to take some of Bill’s advice.

Bill’s talks and videos are posted at: http://www.billbuxton.com.  The closest external example of the video I’m describing above is perhaps: What if Leopold Didn't Have a Piano.  Recommended whether you are a designer or a developer.

                                                --jrh

James Hamilton, Windows Live Platform Services
Bldg RedW-D/2072, One Microsoft Way, Redmond, Washington, 98052
W:+1(425)703-9972 | C:+1(206)910-4692 | H:+1(206)201-1859 | JamesRH@microsoft.com

H:mvdirona.com | W:research.microsoft.com/~jamesrh

Earlier today Microsoft, held an internal tribute to Jim Gray, celebrating his contributions to the industry and to each of us personally.  It’s been just over a year since Jim last sailed out of San Francisco Harbor in Tenacious.  He’s been missing since.

Speakers at the tribute to Jim included Rich Rashid, Butler Lampson, Peter Spiro, Tony Hey, David Vaskevitch, Bill Gates, Gordon Bell and myself. Some touched upon Jim’s broad technical contributions across many sub-fields, while others recounted how Jim has influenced or guided them personally through the years.  I focused on the latter, and described Jim’s contribution as a mentor, a connector of people, and a bridger of fields (included below).

                                                --jrh

What Would Jim Do?

Jim has many skills, but where he has most influenced and impressed me is as a mentor, a connector of people, and a bridger of fields. He became a mentor to me, whether he knew it or not, more than 13 years ago, before either of us had joined Microsoft. Jim is a phenomenal mentor. He invests deeply in understanding the problem you are trying to solve and always has time for deep discussion and debate. Later, I discovered that Jim was an uncanny connector. He knows everyone, and they all want to show him the details of what they are doing. He sees a vast amount of work and forwards the best broadly. He is a nexus for interesting papers, for valuable results, and for new discoveries across many fields. Over time I learned that one of his unique abilities is a bridger of fields. He can take great work in one field and show how it can be applied in others. He knows that many of the world’s most useful discoveries have been made in the gap between fields, and that some of the most important work has been the application of the technology from one field to the problems of another.

Back in 1994, Pat Selinger decided that I needed to meet Jim Gray, and we went to visit him in San Francisco. Pat and I spent an afternoon chatting with Jim about database performance benchmarks, what we were doing with DB2, compiler design, RISC System 6000 and hardware architecture in general. The discussion was typical for Jim. He’s deeply interested in every technical field from aircraft engine design through heart transplants. His breadth is amazing, and the conversation ranged far and wide. It seemed he just about always knew someone working in any field that came up.

A few months later, Bruce Lindsay and I went to visit Jim while he was teaching at Berkeley. Jim and I didn’t get much of a chance to chat during the course of the day—things were pretty hectic around his office at Berkeley—but he and I drove back into San Francisco together. As we drove into the sunset over the city, approaching the Bay Bridge, Jim talked about his experience at Digital Equipment Corporation. He believed a hardware company could sell software, but would never be able to really make software the complete focus it needed to be. He talked of DEC’s demise and said, “They were bound and determined to fail as a hardware company rather than excel as a software company.”

The sunset, the city, and the Bay Bridge were stretched across the windscreen. It was startlingly beautiful. Instead of making conversation with Jim, I was mostly just listening, reflecting and contemplating. At the time, I was the lead architect on IBM DB2. And yes, I too worked for a hardware company. Everything Jim was relating of his DEC experience sounded eerily familiar to me. It was as though Jim was summarizing my own experiences rather than his. I hadn’t really thought this deeply about it before, but the more I did, the more I knew he was right. This was the beginnings of me thinking that probably I should be working at a software company.

He didn’t say it at the time, and, knowing Jim much better now, I’m not sure he would have even thought it, but the discussion left me thinking that I needed to aim higher. I needed to know more about all aspects of the database world, more about technology in general, and to think more about how it all fit together. Having some time to chat deeply with Jim changed how I looked at my job and where my priorities were. I left the conversation pondering responsibility and the industry, and believing I needed to do more, or at least to broaden the scope of my thinking.

I met Jim again later that year at the High Performance Transaction Systems workshop. During the conference, Jim came over, sat down beside me, and said “How are you doing James Hamilton?” This is signature Jim. I’ll bet nearly everyone he knows has had one of those visits during the course of a conference. He drops by, sits down, matches eyes, and you have 110% of his attention for the next 15 to 20 minutes. Jim’s style is not to correct or redirect. Yet, after each conversation, I’ve typically decided to do something differently. It just somehow becomes clear and obviously the right thing to do by the end of the discussion.

In 2006 I got a note from Jim with the subject “Mentor—I need to say I’m helping someone so…”  While it was an honor to officially be Jim’s mentee, I didn’t really expect this to change our relationship much.  And, of course, I was wrong. Jim approaches formal mentorship with his typically thoroughness and, in this role, he believes he has signed up to review and assist with absolutely everything you are involved with, even if not work-related. For example, last year I had two articles published in boating magazines and Jim insisted on reviewing them both. His comments included the usual detailed insights we are all used to getting from him, and the articles were much better for it. How does he find the time?

For years, I’ve read every paper Jim sent my way. Jim has become my quality filter in that, as much as I try, I can’t cast my net nearly as wide nor get through close to as much as I should. Like him, I’m interested in just about all aspects of technology but, unlike him, I actually do need to sleep. I can’t possibly keep up. There are hundreds of engineers who receive papers and results from him on a regular basis. Great research is more broadly seen as a result of his culling and forwarding. Many of us read more than we would have otherwise, and are exposed to ideas we wouldn’t normally have seen so early or, in some cases, wouldn’t have seen at all.

Jim’s magic as a mentor, connector and bridger is his scaling. The stories above can be repeated by hundreds of people, each of whom feels as though they had Jim’s complete and undivided attention. To contribute deeply to others at this level is time-consuming, to do it while still getting work done personally is even harder, and to do it for all callers is simply unexplainable. Anyone can talk to Jim, and an astonishing number frequently do. And because his review comments are so good, and he’s so widely respected, a mammoth amount is sent his way. He receives early papers and important new results across a breadth of fields from computer architecture, operating system design, networking, database, transaction processing, astronomy, and particle physics. The most interesting work he comes across is forwarded widely. He ignores company bounds, international bounds, bounds of seniority, and simply routes people and useful data together. Jim effectively is a routing nexus where new ideas and really interesting results are distributed more broadly.

Over the past year I’ve received no papers from Jim. There has been no advice. I’ve not had anything reviewed by him. And, I’ve not been able to talk to him about the projects I’m working on. When I attend conferences, there have been no surprise visits from Jim. Instead, I’ve been operating personally on the basis of “What would Jim do?”

Each of us has opportunities to be mentors, connectors and bridgers. These are our chances to help Jim scale even further. Each of these opportunities is a chance to pass on some of the gift Jim has given us over the years. When you are asked for a review or to help with a project, just answer “GREAT!!!” as he has so many times, and the magic will keep spreading.

This year, when I face tough questions, interesting issues, or great opportunities, I just ask, “What would Jim do?”  And then I dive in with gusto.

James Hamilton, 2006-01-16.

James Hamilton, Windows Live Platform Services
Bldg RedW-D/2072, One Microsoft Way, Redmond, Washington, 98052
W:+1(425)703-9972 | C:+1(206)910-4692 | H:+1(206)201-1859 | JamesRH@microsoft.com

H:mvdirona.com | W:research.microsoft.com/~jamesrh

Founders at work (http://www.amazon.com/Founders-Work-Stories-Startups-Early/dp/1590597141) is a series of 32 interview with founders of well-known startups. Some have become very successful as independent companies such as Apple where Steve Wozniak was interviewed, Adobe Systems where Charles Geschke was interviewed,  and Research in Motion where Mike Lazaridis was interviewed.  Others were major successes through acquisition, including Mitch Kapor (Lotus Development), Max Levchin (PayPal), Steve Perlman (WebTv), and Ray Ozzie (Iris Associates & Groove Networks).  Some are still startups, and some failed long ago.  The book itself is not amazingly well-written, but I found the interviewees captivating and the book was great by that measure.

The book gives a detailed window into how startups are made, how some have succeeded, and how some have failed.  In portions of the book small windows into the VC community are opened.  The story of how Draper Fisher Jurvetson (DFJ) worked with Sabeer Bhatia (Hotmail) was revealing. 

Some common themes emerged for me as I read through the book.  One theme was that success often came from great people coming together without much funding but considerable motivation and they just kept trying things and evolving and failing and trying again, and trying some more and then changing again.  Often success comes not from a brilliant, well-funded ideas but from intense drive and trying things quickly and failing fast.  Often the VC funded idea X and the money was used to develop a completely unrelated idea.  Often success was found as the last dollar was spent.  I’m quite certain that the ones we didn’t read about were the ones where the last dollar was spent just before success was found.  The lesson for us is to spend small when investigating a new idea.  Move fast, spend little, keep the team small and keep evolving.  Admit when it’s not working and keep trying related ideas.  It was an enjoyable read.

                                                --jrh

James Hamilton, Windows Live Platform Services
Bldg RedW-C/1279, One Microsoft Way, Redmond, Washington, 98052
W:+1(425)703-9972 | C:+1(206)910-4692 | H:+1(206)201-1859 | JamesRH@microsoft.com

H:mvdirona.com | W:research.microsoft.com/~jamesrh

Exactly one year ago, Jim Gray guided his sailboat Tenacious out of San Francisco’s Gashouse Cove Marina into the Bay. He sailed under the Golden Gate Bridge and continued towards the Faralon Islands, some 27 miles off the California coast line. Until that morning, I chatted with Jim via email or phone several days a week.  He has reviewed everything I’ve written of substance for many years. When I consider job changes, I’ll always bounce them first off him first.  When I come across something particularly interesting, I’ll always send it Jim’s way.  Every month or so, he’ll send me an interesting pre-published paper.  If a conference deadline like CIDR or HPTS is approaching, he’ll start pushing me to write something up and keep pushing until it happens. Every four to six months, he’ll decide “I just have to meet” someone with overlapping interests, someone who’s work is particularly interesting, or perhaps they are just super-clear thinkers and worth getting to know.

What’s truly remarkable is that tens and perhaps hundreds of people can say exactly the same thing. He has time for everyone and everyone has similar stories of mentorship, advice, detailed explanation, patience, and insightful reviews. Jim’s magic is that he does this for a huge cross-section of our industry. He knows no bounds and always manages to find the time to help without regard for who’s asking.

Jim is still missing.  Over the past year I’ve received no papers from Jim.  There’s been no advice.  I’ve not had anything reviewed by him. And, I’ve not been able to talk to him about projects I’ve been working on.  When I attend a conference, I don’t get the usual surprise visits from Jim. It’s been exactly one year and we know no more today than we did a year ago. Jim remains missing. We all miss him deeply.

                                                --jrh

James Hamilton, Windows Live Platform Services
Bldg RedW-D/2072, One Microsoft Way, Redmond, Washington, 98052
W:+1(425)703-9972 | C:+1(206)910-4692 | H:+1(206)201-1859 | JamesRH@microsoft.com

H:mvdirona.com | W:research.microsoft.com/~jamesrh

If you are interested in boating, the Seattle Boat Show opened yesterday.  Jennifer and I will be presenting on the red stage at 4:15 on Saturday February 2^nd.  Our presentation will be some of our favorite anchorages and cruising areas selected from  Cruising the Secret Coast: Unexplored Anchorages on British Columbia’s Inside Passage  which just went to the printer a couple of weeks back.   Drop by if you want to talk about boating (or high-scale distributed systems).

                                                --jrh

 

In Designing and Deploying Internet Scale Services (http://research.microsoft.com/~jamesrh/TalksAndPapers/JamesRH_Lisa.pdf) I’ve argued that all services should expect to be overloaded and all services should expect to have manage mass failures. 

Degraded operations mode is a means of dealing with excess load that will happen at some point in the life of your service. Sooner or later, you’ll get an unpredicted number of new customers, more concurrent users, or you’ll have part of the server fleet down and get hit with unexpected load.  Sooner or later you’ll have more customer requests than you have resources to satisfy. When this occurs, many services just run slower and slower and eventually start failing in timeouts.  Basically every user in the system gets a very bad experience.  A more serious example is a login storm. For most services, steady state service is much less resource intensive than user login.  So, in the event of global or broad service failure, millions of users will arrive back at once attempting to login.  The service will fail again under the load and the cycle repeats. It’s not a good place to be. A more drastic approach to avoid this problem is admission control. Only allow users into the service where you have resources left to be able to serve them. Essentially give a few customers a bad experience by not letting them onto the service in order to avoid giving all customers a bad experience.

There is much that can be done between the first options, service failure under high load, and the other end of the spectrum, admission control.  I call this middle ground, degraded operations mode.  In the limit all services need to have admission control to avoid complete and repeating service failure under extreme loads but you hope that admission control is never used.  Degraded operations mode allows a service to continue to take on new load after it reaches capacity by shedding unnecessary tasks.  Most services have batch jobs that run tasks that need to be done but there isn’t actually a customer waiting on them.  For example reporting, backup, index creation, system maintenance, copying data to warehouse servers, etc.  In most services a substantial amount of this work can be deferred without negatively impacting the service.  Clearly these operations need to be run eventually and how long each can be delayed is task and service specific.  Temporarily shedding these batch jobs allows more customers to be served.  The next level of degraded operations mode is to restrict the quality of service in some way. If some operations are far more expensive, you may only allow users to access a subset of the full service functionality.  For example, if you may allow transactions but not reporting if that makes sense for your service.  Finding these degraded modes of operation is difficult and very application specific but they are always there and its always worth finding them.  There WILL be a time when you have more users than resources.

15 years ago I worked on an Ada language compiler and one of the target hardware platforms for this compiler was a Navy fire control system.  This embedded system had a large red switch tagged as “Battle Ready Mode”.  This switch would disable all automatic shutdowns and put the server into a mode where it would continue to run when the room was on fire or water is beginning to rise up the base of the computer.  In this mode, it runs until it dies.  In the services world, this isn’t exactly what we’re after but it’s closely related.  We want all system to be able to drop back to a degraded operation mode that will allow them to continue to provide at least a subset of service even when under extreme load or suffering from cascading sub-system failures.  We need to design and, most important, we need to test these degraded modes of operation in at least limited production or they won’t work when we really need them.  Unfortunately, all services but the very least successful will need these degraded operations modes at least once.

Degraded operation modes are service specific and, for many services, the initial developer gut reaction is that everything is mission critical and there exist no meaningful degraded modes for their specific service.  But, they are always there if you take it seriously and look hard.  The first level is to stop all batch processing and periodic jobs.  That’s an easy one and almost all services have some batch jobs that are not time critical.   Run them later.  That one is fairly easy but most are hard to come up with.  It’s hard to produce a lower quality customer experience that is still useful but I’ve yet to find an example where none were available. As an example, consider Exchange Hosted Services (an email anti-malware and archiving service).  In that service, the mail must get delivered.  What is the degraded operation mode?  They actually can be found there as well.  Here’s some examples: turn up the aggressiveness of email edge blocks, defer processing of mail classified as Spam until later, process mail from users of the service ahead of non-known users, prioritize platinum customers ahead of others.  There actually are quite a few options.  The important point is to think what they are and ensure they are developed and tested prior to the operations team needing them in the middle of the night.

A few months back Skype had a problem recently where the entire service went down or mostly down for more than a day.  What they report happened was that Windows Update forced many reboots and it lead to a flood of Skype login requests “that when combined with lack of peer to peer resources had a critical impact” (http://heartbeat.skype.com/2007/08/what_happened_on_august_16.html).  There are at least two interesting factors here, one generic to all services and one Skype specific.  Generically, it’s very common for login operations to be MUCH more expensive than steady state operation so all services need to engineer for login storms after service interruption.  The WinLive Messenger team has given this considerable thought and has considerable experience with this issue.  They know there needs to be an easy way to throttle login requests such that you can control the rate with which they are accepted (a fine grained admission control for login).  All services need this or something like this but it’s surprising how few have actually implemented this protection and tested it to ensure it works in production.  The Skype specific situation is not widely documented put hinted at by the “lack of peer-to-peer” resources note in the above referenced quote.  In Skype’s implementation, the lack of an available supernode will cause client to report login failure (http://www1.cs.columbia.edu/~salman/publications/skype1_4.pdf  sent to me by Sharma Kunapalli).  This means that nodes can’t login unless they can find a supernode.  This has a nasty side effect in that the fewer clients that can successfully login, the more likely it is that other clients won’t successfully find a supernode.  If they can’t find a supernode, they won’t be able to login either.  Basically, the entire network can become unstable due to the dependence on finding a supernode to successfully log a client into the network.  For Skype, a great “degraded operation” mode would be to allow login even when a supernode can’t be found. Let the client get on and perhaps establish peer connectivity later.

Why wait for failure and the next post-mortem to design in and production test degraded operations for your services?  Make it part of your next release.

                                --jrh

James Hamilton, Windows Live Platform Services
Bldg RedW-D/2072, One Microsoft Way, Redmond, Washington, 98052
W:+1(425)703-9972 | C:+1(206)910-4692 | H:+1(206)201-1859 | JamesRH@microsoft.com

H:mvdirona.com | W:research.microsoft.com/~jamesrh

A couple of weeks back I attended the Berkeley RAD Lab Retreat.  At this retreat, the RAD Lab grad students present their projects and, as is typical of Berkeley retreats, the talks we’re quite good.  It was held up at Lake Tahoe which was great for the skiers but also made for an interesting drive up there.  Chains were required for the drive from Reno to Lake Tahoe and I was in a rental car with less than great summer tires and, of course, no chains.   

It snowed hard for much of the retreat.  When leaving I took a picture of a pickup truck completely buried in the parking lot:

The talks included: Scalable Consistent Document Store, Prototype of the Instrumentation Backplane, Response time modeling for power-aware resource allocation, Using Machine Learning to Predict Performance of Parallel DB Systems, Diagnosing Performance Problems from Trace data using probabilistic models, Xtrace to find Flaws in UC Berkeley Wireless LAN, Exposing Network Service Failures with Datapath Traces, Owning Your Own Inbox: Attacks on Spam Filters, Declarative Distributed Debugging (D3), Policy Aware Switching Layer, Tracing Hadoop, Machine-Learning-Enabled Router to Deal with Local-Area Congestion, A Declarative API for Secure Network Applications, Deterministic Replay on multi-processor systems, and RubyOnRails.berkeley.edu.

Basically the list of talks presented came pretty close to what I would list as the most interesting challenges in services and service design.  Great stuff.   In addition to the talks, there are always an interesting group of folks from Industry and this year was no exception.  I had a good conversation over dinner with Luiz Barroso (http://research.google.com/pubs/author77.html) and brief chat with John Ousterhout (http://www.electric-cloud.com/).

The flight back was more than a bit interesting as well.  We left Reno heading towards Seattle in a small prop plane.  Thirty minutes into the trip, I was starting to wonder what was wrong in that I could see the aircraft landing gear doors opening and closing repeatedly from my wing side seat.  Shortly thereafter the pilot announced that we had a gear problem and we needed to return to Reno.  We returned and did a low pass over the Reno airport so that the tower could check the landing gear position via binoculars.  Then we circled back and landed with a fire trucking chasing us down the runway.  We stayed out on the active taxi ways with the airport closed to incoming or outgoing traffic while crew came out to the aircraft and pinned the gear in the down position before moving the plane to the terminal.

                                --jrh

James Hamilton, Windows Live Platform Services
Bldg RedW-D/2072, One Microsoft Way, Redmond, Washington, 98052
W:+1(425)703-9972 | C:+1(206)910-4692 | H:+1(206)201-1859 | JamesRH@microsoft.com

H:mvdirona.com | W:research.microsoft.com/~jamesrh

Dave Dewitt and Michael Stonebraker posted an article worth reading yesterday titled: MapReduce: A Major Step Backwards (Thanks to Kevin Merrit and Sriram Krishnan for sending this one my way). Their general argument is that MapReduce isn’t better than current generation RDBMS which is certainly true in many dimensions and it isn’t a new invention which is also true.  I’m not in agreement with the conclusion that MapReduce is a major step backwards but I’m fully in agreement with many of the points building towards that conclusion.  Let’s look at some of the major points made by the article:

1. MapReduce is a step backwards in database access

In this section, the authors argue that schema is good, separation of schema and application are good, and high level language access is good. On the first two points, I agree schema is good and there is no question that application/schema separation has long ago proven to be a good thing.  The thing to keep in mind is that MapReduce is only an execution framework.  The data store is GFS or sometimes Bigtable in the case of Google or HDFS or HBase in the case of Hadoop. MapReduce is only the execution framework so it’s not 100% correct to argue that MapReduce doesn’t support schema – that’s a store issue and it is true that most stores that MapReduce is run over don’t implement these features today. 

I argue that a separation of execution framework from store and indexing technology is a good thing in that MapReduce can be run over many stores.  You can use MapReduce over either BigTable (which happens to be implemented on GFS) or over GFS depending upon the type of data you have at hand.  I think that Dewitt and Stonebraker would both agree that breaking up monolithic database management systems into extensible components is a very good thing to do. In fact much of the early work in extensible database management systems was done by David Dewitt.  The point here is that Dewitt and Stonebraker would like to see schema enforcement as part of the store and, generally, I agree that this would be useful.  However, MapReduce is not a store. 

They also argue that high level languages are good.  I agree and any language can be used with MapReduce systems so this isn’t a problem and is supported today.

2. MapReduce is a poor implementation

The argument here is that any reasonable structured store will support indexes.  I agree for many workloads you absolutely must have indexes. However, for many data mining and analysis algorithms, all the data in a data set is accessed.  Indexes, in these cases, don’t help.  This is one of the reason why many data mining algorithms run poorly over RDBMS – if all they are going to do is repeatedly scan the same data, a flat file is faster.  It depends upon application access pattern and the amount of data that is accessed.  A common execution approach for data mining algorithms is to export the data to a flat file and then operate on it there.  An index helps when you are looking at a small subset of the data and there is point N where if you are looking at less than N% of the data, the index helps and should be used. But, if looking at more than N%, you are better off table scanning.  The point N is implementation dependent but storage technology trends have been pushing this number down over the years.  Basically some algorithms look at all the data and aren’t helped by indexes and some look at only a portion of the data and for those that look at more than N% of the data, the index again won’t help.

There is no question that indexes are a good thing and there is no arguing that much of the worlds persistent storage access is done through indexes.  Indexes are good.  But, they are not good for all workloads and for all access patterns.  Remember MapReduce is not a store – only an execution framework. To implement indexing in a store used by MapReduce would be easy and presumably someone will when it’s need is broadly noticed.  In the interim, indexes can be built using MapReduce jobs and then used by subsequent MapReduce jobs.  Certainly more of a hassle than stores that automatically maintain indexes but acceptable for some workloads.

3. MapReduce is not novel

This is clearly true. These ideas have been fully and deeply investigated by the database community in the distant past.  What is innovative is scale.  I’ve seen MapReduce clusters of 3,000 nodes and I strongly suspect that clusters of 5,000+ servers can be found if you look in the right places.  I’ve been around parallel database management systems for many years but have never seen multi-thousand node clusters of Oracle RAC or IBM DB2 Parallel Edition.  The innovative part of MapReduce is that it REALLY scales and, for where MapReduce is used today, scale matters more than everything else.  I’ll claim that 3,000 server query engines ARE novel but I agree that the constituent technologies have been around for some time.

4.  MapReduce is missing features

All of the missing features (bulk loader, indexing, updates, transactions, RI, views) are features that could be implemented in a store used by MapReduce.  As these features become important in domains over which MapReduce is used, they can be implemented in the underlying stores.  I suspect, as long as MapReduce is used for analysis and data mining workloads the pressing need for RI may never get strong enough to motivate someone to implement it.  However, it clearly could be done and the absence of RI in many stores is not a shortcoming of MapReduce.

5.  MapReduce is incompatible with the DBMS tools

I 100% agree. Tools are useful and today many of these tools target RDBMS.  It’s not mentioned by the authors but another useful characteristic of RDBMS is developers understand them and many people know how to write SQL.  It’s an data access and manipulation language that is broadly understood.  The thing to keep in mind is that MapReduce is part of a componentized system.  It’s just the execution framework. I could easily write a SQL compiler that emitted MapReduce jobs (SQL doesn’t dictate or fundamentally restrict the execution engine design).  MapReduce can be run over simple stores as it mostly is today or over stores with near database level functionality if needed.

I’m arguing that the languages with which MapReduce jobs are expressed could be higher level and there have been research projects to do this (for example: http://research.microsoft.com/research/sv/dryad/). Even a SQL Compiler is possible over MapReduce.  And I’m arguing that MapReduce could be run over very rich stores with indexes and integrity constraints should that become broadly interesting.  MapReduce is just an execution engine that happens to scale extremely well. For example, in the MapReduce-like system used around Microsoft, there exist layers of languages above the execution engine that offer different levels of abstraction and control on the same engine.

An execution engine that runs on multi-thousand node clusters really is an important step forward.  The separation of execution engine and storage engine into extensible parts isn’t innovative but it is a very flexible approach that current generation commercial RDBMS could profit from.

I love MapReduce because I love high scale data manipulation.  What can be frustrating for database folks is 1) most of the ideas of MapReduce have been around for years and 2) there has been decades of good research in the DB community focusing on execution engine techniques and algorithms that haven’t yet been applied to the MapReduce engines. Many of these optimizations from the DB world will help make better MapReduce engines.  But, for all these faults, MapReduce sure does scale and it’s hard not to love being able to submit a job and see several thousand nodes churning over several petabytes of data.  Priceless.

                                    --jrh

James Hamilton, Windows Live Platform Services
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H:mvdirona.com | W:research.microsoft.com/~jamesrh

The article below is a restricted version of what I view to be the next big thing.  If I was doing a start-up today, it would be data analysis and optimization as a service.  The ability to run real time optimization over an understandable programming platform for a multi-thousand node cluster is very valuable to almost every industry.  The smaller their margins, the harder it is for them to afford to not to use yield management and optimization systems.  The airlines showed us years ago that yield management systems are a big part of what it takes to optimize the profitability of an airline.  Walmart did the same thing to brick and mortar retail.  Amazon applied the same for online commerce.  The financial community has been doing this for years.  The online advertising business is driven by data analysis.  What’s different as a service is that the technology can be made available to the remaining 99% of the business world.  Those that don’t have the scale to afford doing their own.

Storing blobs in the sky is fine but pretty reproducible by any competitor.  Storing structured data as well as blobs is considerably more interesting but what has even more lasting business value is the storing data in the cloud AND providing a programming platform for multi-thousand node data analysis.  Almost every reasonable business on the planet has a complex set of dimensions that need to be optimized. The example below is a good one but it’s only one.  Almost every business can be made more profitable by employing some form of yield management.  And, it’s ideally set up for a service based solution: the output data is small, the algorithms to be run are small, the data that forms the input is HUGE but it accumulates slowly over times.  Historically large in aggregate but not that large per unit time.  The input data per unit time is of manageable size and could be sent to the service.  The service provides petabytes of storage and thousands of processors and a platform to run data analysis and optimization programs.  This service would also be great for ISVs with companies springing up all over to write interesting algorithms that can be rented and run in the infrastructure.

                                                                --jrh

From: Jeff Carnahan
Subject: Fluidity better than perfection?

Good reading for a Saturday morning…