MapReduce

Analysis of implementations of and issues associated with the parallel programming framework MapReduce. Related subjects include:

November 15, 2008

High-performance analytics

For the past few months, I’ve collected a lot of data points to the effect that high-performance analytics – i.e., beyond straightforward query — is becoming increasingly important. And I’ve written about some of them at length. For example:

Ack. I can’t decide whether “analytics” should be a singular or plural noun. Thoughts?

Another area that’s come up which I haven‘t blogged about so much is data mining in the database. Data mining accounts for a large part of data warehouse use. The traditional way to do data mining is to extract data from the database and dump it into SAS. But there are problems with this scenario, including:

Read more

October 22, 2008

Update on Aster Data Systems and nCluster

I spent a few hours at Aster Data on my West Coast swing last week, which has now officially put out Version 3 of nCluster. Highlights included:

Read more

October 15, 2008

eBay doesn’t love MapReduce

The first time I ever heard from Oliver Ratzesberger of eBay, the subject line of his email mentioned MapReduce.  That was early this year.  Subsequently, however, eBay seems to have become a MapReduce non-fan.  The reason is simple: eBay’s parallel efficiency tests show that MapReduce leaves most processors idle most of the time.  The specific figure they mentioned was parallel efficiency of 18%.

September 5, 2008

More on known MapReduce application areas

In surveying MapReduce applications to date, I said that they fell mainly into three overlapping categories:

and really should have included a fourth:

Nokia just released another MapReduce implementation, Disco, and its list of applications to date fits right into that template. The relevant quote is:

This far Disco has been succesfully used, for instance, in parsing and reformatting data, data clustering, probabilistic modelling, data mining, full-text indexing, and log analysis with hundreds of gigabytes of real-world data.

September 5, 2008

Three different implementations of MapReduce

So far as I can see, there are three implementations of MapReduce that matter for enterprise analytic use – Hadoop, Greenplum’s, and Aster Data’s.* Hadoop has of course been available for a while, and used for a number of different things, while Greenplum’s and Aster Data’s versions of MapReduce – both in late-stage beta – have far fewer users.

*Perhaps Nokia’s Disco or another implementation will at some point join the list.

Earlier this evening I posted some Mike Stonebraker criticisms of MapReduce. It turns out that they aren’t all accurate across all MapReduce implementations. So this seems like a good time for me to stop stalling and put up a few notes about specific features of different MapReduce implementations. Here goes.

Read more

September 4, 2008

Mike Stonebraker’s counterarguments to MapReduce’s popularity

In response to recent posting I’ve done about MapReduce, Mike Stonebraker just got on the phone to give me his views. His core claim, more or less, is that anything you can do in MapReduce you could already do in a parallel database that complies with SQL-92 and/or has PostgreSQL underpinnnings. In particular, Mike says: Read more

September 1, 2008

Yes, but what are the Very Biggest benefits of MapReduce?

On behalf of On-Demand Enterprise, nee’ Grid Today, Dennis Barker asked me to clarify the most important benefits, features, etc. to various constituencies (business users, programmers, DBAs, etc.) of the Greenplum and Aster Data MapReduce announcements. Questions like that are hard to answer simply. Here’s why.

The core benefit of MapReduce is price/performance (because it allows the cost benefits of parallelization to be applied to analyses that are hard to parallelize otherwise). Large price/performance gains commonly mix together three kinds of benefits.

1. They let you do what you did before, for less money.
2. They let you do a better version of what you did before, for similar money.
3. They let you do new things that didn’t make economic sense before, but now do.

Read more

August 26, 2008

Three approaches to parallelizing data transformation

Many MPP data warehousing vendors have told me their products are used for ELT (Extract/Load/Transform) instead of ETL (Extract/Transform/Load). I.e., needed data transformations are done on the MPP system, rather than on the — probably SMP — system the data comes from.* If the data transformation is being applied on a record-by-record basis, then it’s automatically fully parallelized. Even if the transforms are more complex, considerable parallel processing may still be going on.

*Or it’s some of each, at which point it’s called ETLT — I bet you can work out what that stands for.

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August 26, 2008

Why MapReduce matters to SQL data warehousing

Greenplum and Aster Data have both just announced the integration of MapReduce into their SQL MPP data warehouse products. So why do I think this could be a big deal? The short answer is “Because MapReduce offers dramatic performance gains in analytic application areas that still need great performance speed-up.” The long answer goes something like this.

The core ideas of MapReduce are:

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August 26, 2008

Known applications of MapReduce

Most of the actual MapReduce applications I’ve heard of fall into a few areas:

That covers all MapReduce apps I recall hearing about via commercial companies and users, and also includes most of what’s in the two big sources I found online.

Read more

August 25, 2008

MapReduce links

For whatever reason, I seem to be making the peripheral posts about MapReduce tonight before getting to the meat of the issues. So be it. There’s a rich set of links out there about MapReduce, and here are some of the best of them:

Read more

August 25, 2008

MapReduce sound bites

Last Thursday, both Greenplum and Aster Data — the two most recent of my numerous data warehouse specialist customers — both told me of the same major innovation. Both were rushing to announce it first, before anybody else did. This led to considerable tap dancing, with the upshot being that both are releasing the information tonight or tomorrow morning.

What’s going on is that Aster Data and Greenplum have both integrated MapReduce into their respective MPP shared-nothing data warehouse DBMS. Read more

February 7, 2008

Why the Great MapReduce Debate broke out

While chatting with Mike Stonebraker today, I finally understood why he and Dave DeWitt launched the Great MapReduce Debate:

It was all about academia.

DeWitt noticed cases where study of MapReduce replaced study of real database management in the computer science curriculum. And he thought some MapReduce-related research papers were at best misleading. So DeWitt and Stonebraker decided to set the record straight.

Fireworks ensued.

January 24, 2008

Is MapReduce a good underpinning for next-gen scientific DBMS?

Back in November, Mike Stonebraker suggested that there’s a need for database management advances to serve “big science”. He said:

Obviously, the best solution to these … problems would be to put everything in a next-generation DBMS — one capable of keeping track of data, metadata, and lineage. Supporting the latter would require all operations on the data to be done inside the DBMS with user-defined functions — Postgres-style.

Read more

January 24, 2008

A passionate defense of MapReduce

Mark Chu-Carroll has weighed in with a passionate defense of MapReduce. I only see one thing he got wrong, which was to overlook the great shared-nothing parallelism of today’s data warehouse appliances and specialty data warehouse DBMS. But that doesn’t detract from his overall point, which is that MapReduce is designed to help with parallel computing in general, not database querying in particular.

He also has the best version I know of an old observation, namely:

… [relational database] people have found the most beautiful, wonderful, perfect hammer in the whole world. It’s perfectly balanced - not too heavy, not too light, and swings just right to pound in a nail just right every time. The grip is custom-made, fitted to the shape of the owners hand, so that they can use it all day without getting any blisters. It’s also beautifully decorated - encrusted with gemstones and gold filigree - but only in places that won’t detract from how well it works as a hammer. It really is the greatest hammer ever. Relational database guys love their hammer. It’s just such a wonderful tool! And when they make something with it, it really comes out great. In fact, they like it so much that they think it’s the only tool they need. If you give them a screw, they’ll just pound it in like it’s a nail. And when you point out to them that dammit, it’s a screw, not a nail, they’ll say “I know that. But you can’t expect me to use a crappy little screwdriver when I have a magnificent hammer like this!”


January 19, 2008

MapReduce for data mining? Maybe for variable-schema analytics.

Rich Skrenta is quite a successful entrepreneur, so it’s likely that he doesn’t really mean the more ridiculous parts of this rant on the MapReduce debate. E.g., he cheerfully disregards the fact that the data warehouse appliance vendors have ALREADY disrupted the market he’s focusing on. Index-light row-based and columnar systems are both super fast at data mining extracts.

But let’s go straight to the one interesting thing he said, Read more

January 18, 2008

The Great MapReduce Debate

Google’s highly parallel file manipulator MapReduce has gotten great attention recently, after a research paper revealed:

(Niall Kennedy popularized the paper and surveyed its results.)

David DeWitt and Mike Stonebraker then launched a blistering attack on MapReduce, accusing it of disregarding almost all the lessons of database management system theory and practice. A vigorous comment thread has ensued, pointing out that MapReduce is not a DBMS and asserting it therefore shouldn’t be judged as one.

While correct, that defense begs the question – what is MapReduce good for? Proponents of MapReduce highlight two advantages:

  1. MapReduce makes it very easy to program data transformations, including ones to which relational structures are of little relevance.
  2. MapReduce runs in massively parallel mode “for free,” without extra programming.

Based on those advantages, MapReduce would indeed seem to have significant uses, including: Read more

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