MapReduce — a Major Step Backwards?

The Database Column has an interesting, if negative, look at MapReduce and what it means for the database community. MapReduce is a software framework developed by Google to handle parallel computations over large data sets on cheap or unreliable clusters of computers. "As both educators and researchers, we are amazed at the hype that the MapReduce proponents have spread about how it represents a paradigm shift in the development of scalable, data-intensive applications. MapReduce may be a good idea for writing certain types of general-purpose computations, but to the database community, it is: a giant step backward in the programming paradigm for large-scale data intensive applications; a sub-optimal implementation, in that it uses brute force instead of indexing; not novel at all -- it represents a specific implementation of well known techniques developed nearly 25 years ago; missing most of the features that are routinely included in current DBMS; incompatible with all of the tools DBMS users have come to depend on."

may be missing the (data)points

[yagu]

I don't know why this article is so harshly critical of MapReduce. They base their critique and criticism on the following five tenets, which they further elaborate in detail in the article:

1. A giant step backward in the programming paradigm for large-scale data intensive applications
2. A sub-optimal implementation, in that it uses brute force instead of indexing
3. Not novel at all -- it represents a specific implementation of well known techniques developed nearly 25 years ago
4. Missing most of the features that are routinely included in current DBMS
5. Incompatible with all of the tools DBMS users have come to depend on

If you take the time to read the article you'll find they use axiomatic arguments with lemmas like: "schemas are good", and "Separation of the schema from the application is good, etc. First, they make the assumption that these points are relevant and germaine to MapReduce. But, they mostly aren't.

Also taking the five tenets listed, here are my observations:

1. A giant step backward in the programming paradigm for large-scale data intensive applications

   they don't offer any proof, merely their view... However, the fact that Google used this technique to re-generate their entire internet index leads me to believe that is this were indeed a giant step backward, we must have been pretty darned evolved to step "back" into such a backwards approach

2. A sub-optimal implementation, in that it uses brute force instead of indexing

   Not sure why brute force is such a poor choice, especially given what this technique is used for. From wikipedia:

   MapReduce is useful in a wide range of applications, including: "distributed grep, distributed sort, web link-graph reversal, term-vector per host, web access log stats, inverted index construction, document clustering, machine learning, statistical machine translation..." Most significantly, when MapReduce was finished, it was used to completely regenerate Google's index of the World Wide Web, and replaced the old ad hoc programs that updated the index and ran the various analyses.

3. Not novel at all -- it represents a specific implementation of well known techniques developed nearly 25 years ago

   Again, not sure why something "old" represents something "bad". The most reliable rockets for getting our space satellites into orbit are the oldest ones.

   I would also argue their bold approach to applying these techniques in such a massively aggregated architecture is at least a little novel, and based on results of how Google has used it, effective.

4. Missing most of the features that are routinely included in current DBMS

   They're mistakenly assuming this is for database programming

5. Incompatible with all of the tools DBMS users have come to depend on

   See previous bullet

Are these guys just trying to stake a reputation based on being critical of Google?

Re:may be missing the (data)points

[CajunArson]

Are these guys just trying to stake a reputation based on being critical of Google? I tend to agree, I could probably write a nice article about how map-reduce would be a terrible system to use in making a 3D game. Could an article like that be technically true? Sure. Would it be in anything more than a logical non-sequiter? Not unless Google all of the sudden came out and claimed mapreduce is the new platform for all 3D game development (not likely).

Re:may be missing the (data)points

[abscondment]

It's also terrible for painting.

1. Since the bucket doesn't enforce any schema, you never know what color paint the bucket might hold. Heck, it could even be full of honey. You just can't know, and not being able to know is, well, like programming assembly.
2. Buckets aren't indexed, so you're not able to find that one ounce of paint that you really want to use next. You've got to split up all of the paint into ounce cups each time and examine very cup. It's very intensive, and really slows down your painting. If you stored the paint in a B-tree of ounce cups, your search for the right ounce of paint would be much more efficient.
3. Painting is so old. I mean, get with the program. Gold plate your house, or something newer (since newer is always better!). In fact, decades of research into titanium has determined that it'll hold up better to the elements, anyway, so you should just get titanium siding instead of painting.
4. Painting is an incomplete process. What if you want a window? Yeah, you can't paint a window for yourself, now can you? Did you need a jacuzzi? A fireplace? A new car? Sorry! Painting doesn't support those features yet. You'd better not paint at all if you want those things.
5. Painting, believe it or not, is incompatible with tennis. There's no racket, there's no court, and there's no ball. There's not even a net (unless you're working from a really tall building, in which case you might fall and so a net is often used). I mean, you don't even need to paint with another person. It's so... incompatible.

Re:

[jd]

MapReduce is a clustered database system, which implies you have a cluster, which shuld be very good for games. Alternatively, if you wanted a VERY large game of Empire or NetPanzer, a distributed world would actually be quite helpful.

I am a little surprised by the parallelization methods, though. Informix developed some parallel database methods some time back, which is partly why IBM bought them. I'm sure that the cutting-edge parallel database techniques that exist today have advanced beyond herustics

Re:may be missing the (data)points

[starwed]

I thought that this blog post [typicalprogrammer.com] was a pretty good sounding critique of the article in question. (Of course, I don't know a damn thing about DB, relational or otherwise. . )

Re:may be missing the (data)points

[merreborn]

His conclusion really hits the nail on the head:

What the authors really want to gripe about is distributed "cloud" data management systems like Amazon's SimpleDB; in fact if you change "MapReduce" to "SimpleDB" the original article almost makes sense.

Re:

[dezert_fox]

>If you take the time to read the article you'll find they use axiomatic arguments with lemmas like: "schemas >are good", and "Separation of the schema from the application is good, etc. Actually, it says: "The database community has learned the following three lessons from the 40 years that have unfolded since IBM first released IMS in 1968. Schemas are good. Separation of the schema from the application is good. High-level access languages are good." Way to conveniently drop important contextual

Re:

[MagikSlinger]

Way to conveniently drop important contextual information. Axioms like these, derived from 40 years of experience, carry a lot of weight for me.

While a good point, it's still irrelevant to Google and Map-Reduce because Google's search engine is NOT a RDBMS. It's almost pure indexing, and what they are doing is comparing, say , Oracle to a specific B+-Tree implementation. They are seeing the Map-Reduce algorithm purely from a RDBMS perspective--not a "let's solve this specific problem" perspective.

I'

Re:may be missing the (data)points

[Anonymous Coward]

You missed points 6 through 9:

6. New things are scary.
7. Google is on their lawn.
8. Matlock is the best television show ever.

Re:

[MorpheousMarty]

I am still missing point 9.

Re:

[MightyYar]

9 minus 6 is 3, and there are 3 points there. So what's your problem? :)

Re:

[Otter]

Are these guys just trying to stake a reputation based on being critical of Google?

I don't know much about database theory, but do know that Michael Stonebraker already has a reputation.

Re:may be missing the (data)points

[samkass]

Speaking as someone who works for a company whose product uses a database that is neither relational nor object-oriented, I can say from experience that folks who have devoted a significant amount of their lives to mastering that methodology see anything else as a threat. There are definitely use-cases for non-relational databases-- they're used at both Google and Amazon, as well as many other places. You can either burn significant effort defending your decision to go non-relational, or you can move on and ignore these folks and produce great products. The problem is that sometimes they make good points (especially about some aspects of indexing), but it's almost always lost in the "but... but... but... you're not relational!" argument.

Re:

[NewbieProgrammerMan]

Speaking as someone who works for a company whose product uses a database that is neither relational nor object-oriented, I can say from experience that folks who have devoted a significant amount of their lives to mastering that methodology see anything else as a threat.

I've bumped into this attitude in the little bit of time I spent as a developer: people who think that every last bit of configuration and data can (and must!) be crammed into a relational model, whether it belongs there or not. Performan

Re:

[samkass]

A colleague of mine replied anonymously to my post and said things much more eloquently than I could. No, it's nothing to do with table design or adjustments... it's a fundamental shortcoming of relational theory. Relational databases require transactional integrity and therefore a centralized locking system. There is no way to scale it beyond a certain point.

Re:

[Larry Lightbulb]

Pick?

Wait, are they "experts", though?

[SanityInAnarchy]

Note how their blog represents the post as having a single author, when, in fact, it has multiple authors?

That does not sound at all like a database expert to me. It's a simple many-to-many relationship!

Re:may be missing the (data)points

[DragonWriter]

I don't know why this article is so harshly critical of MapReduce.

The primary grounds for complaint seems to be "this isn't the way we do things in the database world". Each of the complaints (except #3) boils down to this (#1: The database community had arguments a few decades back and developed, at the time, a set of conventions; Map Reduce doesn't follow them and is, therefore, bad; #2: All databases use one of two kinds of indexes to accelerate data access; MapReduce doesn't and is, therefore, bad; #3: Databases do something like MapReduce, so MapReduce isn't necessary; #4: Modern databases tend to offer a variety of support utilities and features that MapReduce doesn't, so MapReduce is bad; #5: MapReduce isn't out-of-the-box compatible with existing tools designed to work with existing databases and is, therefore, bad.)

And its from The Database Column, a blog that from its own "About" page is comprised of experts from the database industry.

I suspect part of the reason they are harshly critical is that this is a technology whose adoption and use in large, data-centric tasks is (regardless of efficiency) a threat to the market value of the skills in which they've invested years and $$ developing expertise.

At the end, they note (as an afterthought) that they recognize that MapReduce is an underlying approach, and that there are projects ongoing to build DBMS's on top of MapReduce, a fact which, if considered for more than a second, explodes all of their criticism which is entirely premised on the idea that MapReduce is intended as a general purposes replacement for existing DBMSs, rather than a lower-level technology which is currently used stand-alone for applications for which current RDBMSs do not provide adequate performance (regardless of their other features), and on which DBMS implementations (with all the features they complain about MapReduce lacking) might, in the future, be built.

Re:

[ShakaUVM]

Map/Reduce is a very common operation in parallel processing. From my very quick look, it does seem as if the authors are right -- it looks like a quick and dirty implementation of a common operation, and not a "paradigm shift" in the slightest.

Re:

[Splab]

Did an assignment on map reduce some time ago, while I wasn't really impressed with it as a "Database" it was some really cool stuff they did with distributing the calculations - I did however note back then that it wasn't really useful for the general industry, but still was a very nice piece of software.

Indexing is useless here.

[SharpFang]

Indexing works by picking a small slice of the data you have (as a list of hashes), and changing it into a much smaller table mapping the data onto a group of records matching it. The index is smaller and conforms to a certain strict standard, so it's very fast to brute force. Then as you get the list of indices, you brute force them, and this way you get the record.

This works well if you can create such a slice - a piece of data you will match against. It becomes increasingly unwieldy if there are many ways to match a data - multiple columns mean multiple indices. And then if you remove columns entirely, making records just long strings, and start matching random words in the record, index becomes useless - hashes become bigger than chunks of data they match against, indexing all possible combinations of words you can match against results in index bigger than the database, and generally... bummer. Index doesn't work well against freestyle data searchable in random form.

Imagine a database with its main column being VARCHAR(255) and using about full length of it, then search using a lot of LIKE and AND, picking various short pieces out of that column, and the database being terabytes big. Try to invent a way to index it.

Re:Indexing is useless here.

[Timothy Brownawell]

Imagine a database with its main column being VARCHAR(255) and using about full length of it, then search using a lot of LIKE and AND, picking various short pieces out of that column, and the database being terabytes big. Try to invent a way to index it.

Convert it to an HTML table and put it where googlebot can see it.

Re:

[Allador]

Imagine a database with its main column being VARCHAR(255) and using about full length of it, then search using a lot of LIKE and AND, picking various short pieces out of that column, and the database being terabytes big. Try to invent a way to index it.

Turn on full-text indexing?

This is also an old problem, with well understood solutions. Nearly all modern RDMS systems ship with a solution to this built in and well documented.

Google = statistical database?

[gnuman99]

I thought Google search weren't exact. You know, they were more statistical in nature. The entire algorithm is not probably based on absolute number (guessing, but otherwise it would not make sense).

The thing is if Google uses this to create their index-like structure of the internet for their search engine, and it is not exactly like a RDBMS, well, so what? The MapReduce thing seems to be targeted at large sets of data and semi-accurate data mining, not exact results. No one really cares if there are 3,000

Re:may be missing the (data)points

[mishabear]

> I don't know why this article is so harshly critical of MapReduce.
> Are these guys just trying to stake a reputation based on being critical of Google?

Um... yes?

The Database Column is being coy about being a corporate blog for Vertica, a high performance database database product, but in fact it is. Vertica is a commercial implementation of C-Store and was founded by Michael Stonebraker, the most prominent proponent of column based databases (get it? the database column). So yes, they have a very good reason to be hostile to Google.

http://www.vertica.com/company/leadership [vertica.com]
http://en.wikipedia.org/wiki/C-Store [wikipedia.org]
http://en.wikipedia.org/wiki/Michael_Stonebraker [wikipedia.org]
http://www.databasecolumn.com/2007/09/contributors.html [databasecolumn.com]

Re:may be missing the (data)points

[einhverfr]

Hmmm.... ISTM that the basic critiques come down to:

1) No indexing.

Which means

2) Certain types of constraints probably don't work (such as UNIQUE constraints)

Which also means

3) Referential integrity checking and other things don't work.

This leads to the conclusion that the idea is good for certain types of data-intensive but not integrity-intensive applications (think Ruby on Rails-type apps) but *not* good for anything Edgar Codd had in mind....

Re:

[Blakey Rat]

What bothers me the most is how much hype it gets. I work for a company that has had a "MapReduce" implementation (used internally) for as long as Google has, and we're not getting drooled over by the tech press. I'm sure tons of companies that have had to solve similar problems have already made this tool, even though the languages and syntax involved might change between implementations, it's nothing all that great.

ObDilbert

[markov_chain]

"You'd be fools to ignore the Boolean anti-binary least-square approach!"

Re:

[rakslice]

I don't think that these guys are being deliberately antagonistic, it's just that, well,...

Relational databases are so routinely used in applications that they aren't a good fit for that I suspect many relational database "experts" aren't even acquainted with the practise of selecting network computing paradigms and technologies for an application that are a good fit for the application.

Re:

[Allador]

A giant step backward in the programming paradigm for large-scale data intensive applications
they don't offer any proof, merely their view...

Actually they do. Their proof is that this sort of approach was tried several decades ago, and has been found to be a fundamentally lacking approach for general purpose data processing. They provide several examples.

Not novel at all -- it represents a specific implementation of well known techniques developed nearly 25 years ago

Again, not sure why something "old" represents something "bad". The most reliable rockets for getting our space satellites into orbit are the oldest ones.

It's not bad because its 'old'. It's bad because it was rejected by the community consensus a long time ago as a general purpose solution. Their argument isnt that bad = old. Their argument is that ignoring the lessons of the past is bad.

Missing most of the features that are routinely included in current DBMS

They're mistakenly assuming this is for database programming

They never state that MapReduce is for database pr

Re:may be missing the (data)points

[eh2o]

MapReduce falls under the category of embarrassingly parallel algorithms. It isn't a step backwards, it just has a limited scope.

Google's contribution (and yes it does predate them by a long time) is to point out that MapReduce is a bit more than an algorithm -- it is a design pattern. Design patterns help us write clean code by establishing a consistent vocabulary (e.g. actors, containers, operators, etc), and furthermore are important insofar as they making algorithms accessible to programmers. Right now we badly need more well-defined design patterns in the area of parallel computing as this is essentially the future of programming.

Re:

[mini me]

CouchDB, ThruDB, RDDB, and SimpleDB, to name a few.

Re:

[jacquesm]

map-reduce has *many* applications outside search

Just watch.

[jonnythan]

It's a technical step backwards, they're doing it all wrong, experts say you should do it this other way....

And watch. It'll be massively successful because it works.

Blink blink

[Thelasko]

Once I saw the word paradigm in the summary I just glazed over like I do whenever our CEO gives a speech.

Re:Blink blink

[spun]

Ah, the old "eyes glazing over" paradigm. Definitely no synergy in that. Here's an action item: leverage your value added intellectual capital to architect a new scenario.

Re:

[putch]

[obligatory simpsons quote] Excuse me, but "proactive" and "paradigm"? Aren't these just buzzwords that dumb people use to sound important?

Not that I'm accusing you of anything like that.

I'm fired, aren't I?[/obligatory simpsons quote]

Fnord?

[SanityInAnarchy]

Paradigm.

Does that mean Paradigm is a Fnord? As in, I can now say stuff you won't be able to consciously read, because it has the Fnord Paradigm in it?

Databases? WTF?

[mrchaotica]

Since when did MapReduce have anything to do with databases? It's actually about parallel computations, which are entirely different.

Re:

[mini me]

Those newfangled [couchdb.org] document [google.com] databases [thefreedictionary.com] utilize MapReduce to gather records. I'm guessing that's what the article is about.

Re:

[DragonWriter]

Since when did MapReduce have anything to do with databases?

MapReduce is a tool, one of whose principal applications is conducting queries on large bodies of data consisting of records of similar structure. It, therefore, competes with traditional DBMSs to a degree.

Now, (largely because of the limitations the authors note), it generally is only used currently for the kind of applications where setting up a traditional RDBMS to handle them would be impractical: Google developed their implementation of MapRed

Re:

[DragonWriter]

MapReduce is a tool, one of whose principal applications is conducting queries on large bodies of data consisting of records of similar structure. It, therefore, competes with traditional DBMSs to a degree.

Responding to myself is bad form, but:

Obviously, this only makes sense taking "similar structure" extremely loosely; still, the point is that MapReduce was developed to fill a niche for which RDBMSs were being used previously, in the absence of a more specialized tool, so it clearly competes with them, to

Re:

[einhverfr]

Not really.

ANd no it is not really a step backwards for databases. It is actually something which offers a niche solution for large-scale single-purpose, semi-accurate databases.

This is almost but not entirely unlike what Codd had in mind when he wrote his seminal paper: "A Relational Model of Data for Large Shared Data Banks."

If it were a paradigm shift, it would be a step backward. However, as "one more tool in the toolbox" it is useful in some cases where RDBMS's are not.

I figured it out. Really.

[mypalmike]

I had a lightbulb moment after rereading this thing a few times. The authors of the paper think MapReduce is a distributed query processor, backed by a datastore of unstructured records. They picture this database where every query kicks off a MapReduce operation. Seriously, reread it from that perspective. It makes sense. Too bad for them, their fundamental assumption is wrong. It helps to have even a small amount of experience working with a technology before writing a critique of it.

Re:

[Temporal]

Um... Nope, sorry, the OP is right. MapReduce is a framework for batch processing of gigantic data sets where you intend to do something with every item in the set, or at least a large fraction of them. Relational databases are better for quickly looking up subsets of the items in a database based on query terms, and can be used for serving real-time queries.

Re:

[martin-boundary]

Um, nope. You're not thinking abstractly enough, that is, you're not thinking like a computer scientist. MapReduce is a (rather obvious) framework for processing large lists of (key,data) pairs in parallel, therefore it can be compared with other such systems. Both MapReduce and RDBMSes basically compute a function on a set of (key,data) pairs.

1) The fact that MapReduce is being used for specific low level applications does not make it intrinsically different or uncomparable to an RDBMS, although it may n

Re:

[Temporal]

I guess if you consider anything that involves (key, value) pairs to be basically an RDBMS, you might as well classify almost everything as an RDBMS, which seems to make the term pointless. Why write software anymore when we can just use a database? The reality is that I would use MapReduce and MySQL to solve very different problems.

I think TFA is being silly in trying to compare MapReduce to DBMSs. Yes, of course MapReduce compares unfavorably, because it isn't a DBMS. The comment that MapReduce is "A

Re:

[martin-boundary]

I guess if you consider anything that involves (key, value) pairs to be basically an RDBMS, you might as well classify almost everything as an RDBMS, which seems to make the term pointless.

I don't think it's pointless. It (ie database theory) actually explains why RDBMSes are so popular: they are flexible enough to solve many problems, and people find it easy to think in those terms, and apply that viewpoint.

I think TFA is being silly in trying to compare MapReduce to DBMSs. Yes, of co

Re:

[Temporal]

From reading the article, my impression is that the authors wrote it in response to some questions, and it's targeted at database people who have heard the MapReduce hype and are wondering if it will help them. Kind of like when people wondered if they should use Java for everything in the late 90s.

The article seems to assume that MapReduce is trying to compete with RDBMSs, and even attacks the authors of MapReduce, suggesting that they should read up on database theory. An article which simply argued that

Re:

[martin-boundary]

The article seems to assume that MapReduce is trying to compete with RDBMSs, and even attacks the authors of MapReduce, suggesting that they should read up on database theory.

Fair point.

Re:

[martin-boundary]

More and more systems use databases simply as a data archive, not for primary work.

I wouldn't count on even that being a long term trend. It takes time for people to come up with things to do with a database. Especially really big databases. Wait another ten years, and people will complain that their dumb data archives are not RDBMSes.

Money, meet mouth

[tietokone-olmi]

Perhaps the traditional RDBMS experts will return when they can scale their paradigms to datasets that are measured in the tens of terabytes and stored on thousands of computers. Following the airplane rule the solution needs to be able to withstand a crash in a bunch of those hosts without coming unglued.

Now, this is not to say that a more sophisticated approach wouldn't work. It's just that when you have thousands of boxes in a few ethernet segments, communication overhead becomes really quite large, so large in fact that whatever can be saved with brute-force computation it'll usually be worth it. Consider that from what I've heard, at Google these thousands of boxes are mostly containers for RAM modules so there's rather a lot of computation power per gigabyte available to throw away with a brute force system.

Also, I would like to point out that map/reduce is demonstrated to work. Apparently quite well too. Certainly better than any hypothetical "better" massively parallel RDBMS available in a production quality implementation today.

Re:

[StarfishOne]

Agreed.

I recently read somewhere (if only I could recall the link...) that on average Google's MapReduce jobs process something in the order of 100 GB/second, 24/7/365

I've got nothing against RDBMS... but how can you be critical about a tool that scales and performs so well? It's just a matter of selecting and using the right tool for the job.

Re:

[Chuck_McDevitt]

Parallel DBMS systems like Teradata, Greenplum, Netezza, etc. have already solved this in the relational DBMS world. Teradata was doing 10's of Terabytes across many computers 15 years ago, and there are Teradata and Greenplum systems that have many hundreds of terabytes.

Re:

[kpharmer]

> Perhaps the traditional RDBMS experts will return when they can scale
> their paradigms to datasets that are measured in the tens of terabytes
> and stored on thousands of computers.

I'm not defending the authors of the article, but...

1. this article wasn't written by rdbms people, but rather by column database people. There's nothing traditional or relational about their background.

2. Every solution is a result of a variety of compromises, there is no such thing as the perfect solution outside of

Re:

[Eivind Eklund]

I'm not defending the authors of the article, but...

1. this article wasn't written by rdbms people, but rather by column database people. There's nothing traditional or relational about their background.

A column database [wikipedia.org] is a particular way of implementing relational databases. David DeWitt (faculty homepage [wisc.edu], wikipedia entry [wikipedia.org]) is best known for object-relational work, which is in the traditional relational area. Michael Stonebraker [wikipedia.org], the other author, is probably the best known relational database person living today (though C.J. Date might be another candidate).

Eivind.

As one of the comments on the blog ...

[tcopeland]

...entry says;

"You seem to not have noticed that mapreduce is not a DBMS."

Exactly. These are the same sort of criticisms that you hear around memcached [danga.com] - the feature set is smaller, etc - and they make the same mistake. It's not a DBMS, and it's not supposed to be. But it does what it does quite well nonetheless!

distributed indexes?

[magarity]

in that it uses brute force instead of indexing
 
Isn't the overhead of a distributed index usually not worth the bother? This scheme sounds similar to the way Teradata handles its distribution and it manages to get a lot done with hardly any secondary indexes. I think the thinking in the article indicates standalone database server box thinking.

Ideas ahead of their time?

[dazedNconfuzed]

it represents a specific implementation of well known techniques developed nearly 25 years ago

There are many classic/old techniques which are only now being used - and very successfully - precisely because the hardware simply wasn't there. A recent /. post told of ray-tracing being soon used for real-time 3D gaming, and how it beats the socks off "rasterized" methods when a critical mass of polygons is involved; the techniques were well known and developed nearly 25 years ago, but only now do we have the CPU horsepower and vast fast memory capacities available for those "old" techniques to really shine. Likewise "old" "brute force" database techniques: they may not be clever and efficient like what we've been using for highly stable processing of relatively small-to-medium databases, but they work marvelously well when involving big unreliable networks of processors working on vast somewhat-incoherent databases - systems where modern shiny techniques just crumble and can't handle the scaling.

Sometimes the "old" methods are best - you just need the horsepower to pull it off. Clever improvements only scale so long.

Re:

[Duncan3]

Exactly, noone except Google claims the MapReduce methods are new in any way. And given their lots-of-junk-machines, it's the way to do it. Anyone in the distributed computing space over the last _35_ years would have done it exactly the same way, just in older programming languages :)

The rest of the article is just DB-centric whining.

Bad Perspective

[Evets]

This article was written from the perspective that map-reduce based architectures is in competition with common relational database architecture. It's not.

Certainly if you were to implement map-reduce within the confines of the relational database world, there are implementation methodologies that would need to be taken to make it easier for the RDBMS developer to work with the storage and querying mechanisms.

The article implies that map-reduce is bad because it doesn't place restrictions common to the dat

A completely uninformed analysis

[abes]

Well, INDBE, but MapReduce seems like a pretty cool idea (even it is old [which in my books does not equate bad]). A similar argument could be made against SQL -- it's not appropriate to all solutions. It's used for most nowadays, in part because it's the simplest to use, but that doesn't make it necessarily better. It (of course) depends on what data you want to represent.

Even more importantly, you can create schemas with MapReduce by how you write your Map/Reduce functions. This is a matter of the datafunction exchange (all data can be represented as a function, likewise all functions can be represented as data). I admit ignorance to how this MapReduce system works, but I would be surprised if you couldn't get a relational database back out.

The advantage is you get with MapReduce is that you aren't necessarily tied to a single representation of data. Especially for companies like Google, which may want to create dynamic groups of data, this could be a big win. Again, this is all speculative, as I have very little experience with these systems.

A Very Human Response

[Anonymous Coward]

The reaction seems straightforward enough. The MapReduce paradigm has proved to be very effective for a company that lives and breathes scalability, while it apparently ignores a whole bunch of database work that's been going on in academia. That fact that industry was able to produce something so effective without making use of all this knowledge base at least implicitly undercuts the importance of that work, and is thus threatening to the community which produced that work. Is it any surprise that the researchers whose work was completely side-stepped by this approach aren't happy with the current situation?

Re:

[DragonWriter]

The reaction seems straightforward enough. The MapReduce paradigm has proved to be very effective for a company that lives and breathes scalability, while it apparently ignores a whole bunch of database work that's been going on in academia.

That's not the "problem" (from the perspective of the authors of TFA), really.

The problem is that provides an alternative to work that has been going on in industry, and in particular that it provides a way to end-run some of the limitations of traditional databases that

belly acres

[rodentia]

A sub-optimal implementation, in that it uses brute force instead of indexing

As though these are the exclusive choices. TFA goes on to complain about implementing 25 year old ideas, though they are actually rather older than that--they just didn't strike the RDB types until the eighties. They proceed to insist that the system cannot scale. Arguing google's scalability is like arguing gravity.

FTFA

[smcdow]

Given the experimental evaluations to date, we have serious doubts about how well MapReduce applications can scale.

That's a joke, right?

I think Google's already taken care of all the experimental evaluations you'd need.

Re:

[sammy baby]

That's a joke, right?

I know, that's what I thought.

But then again... a few weeks ago I was involved in a phone call with one of our clients. They're a huge client for us, to a degree that they can significantly influence the future direction of our product by complaining loud enough, and our first client to use some new "high-availability" features we're gradually rolling out.

In the course of our conversation, one of the client's guys essentially pooped on a large part of our product roadmap, basically beca

Re:

[Bill, Shooter of Bul]

People have funny beliefs about things they don't completely understand, especially with technology. By "network based" he probably is referring to some asinine criteria that is most likely not related to any network thats ever existed or could ever exist, like routing the requested based on the evil bit of the packet.

A step from where?

[644bd346996]

If you are starting with a good database, MapReduce is definitely a step backwards. But that isn't what MapReduce is designed to replace. In reality, MapReduce replaces the for loop [joelonsoftware.com], and viewed from that perspective, it is a major step forward. Most languages (C, C++, Java, etc.) define the for loop and other iteration facilities in such a way that the compiler can seldom safely parallelize the loop. MapReduce gives the programmer an easy way to convert probably 90% of their for loops into highly scalable code.

Re:

[Duncan3]

Unless you're using one of the dozens of compilers can do just that, or FORTRAN, or OpenMP, or...

Re:

[Rakishi]

Your compiler will parallelize a for loop across 1000 machines AND split the input data across them before you even run the program?

Translation:

[Chris Mattern]

"We spent all these years making these complex, elegant algorithms--see how intricate this wonderful indexing algorithm is?--and then they solve things by simply throwing cheap hardware at it. It's not *fair!*"

Missing the forest for the trees...

[brundlefly]

The point of MapReduce is that It Works. Cheaply. Reliably. It's not a solution for the Cathedral, it's one for the Bazaar.

Comparing it to a DBMS on fanciness is pointless, because the DBMS solution fails where MapReduce succeeds.

Step backward?

[gmuslera]

The 1st that come to my mind when i read that was the evolution of a programmer [nus.edu.sg], when a "program" evolving started to get back thin in lines didnt meant that were a step backwards.

Huh?

[Black Parrot]

we are amazed at the hype that the MapReduce proponents have spread about how it represents a paradigm shift in the development of scalable, data-intensive applications.

So much hype that I never even heard of it before their complain hit Slasdot...

Vertica

[QuietLagoon]

The column was copyright by Vertica [vertica.com]. Wouldn't they be concerned about the type of competition that MapReduce presents?

Vertica launches database-focused blog

[QuietLagoon]

Vertica launches database-focused blog: The Database Column [vertica.com].

Re:

[SteeldrivingJon]

Maybe they're getting customers asking about mapreduce, and are tired of trying to convince customers that a conventional system is the way to go.

The are afraid...

[mini me]

I gather this is a publication for DBAs. It seems they are worried about their jobs more than anything. With the map-reduce-style databases there isn't a need for any kind of special database expert. The business logic all happens in the application. There is no need for tuning indexes. You don't even need to define a schema. When things get slow any monkey can drop in another computer and you're back up to speed and ready to go.

Traditional RDBMSes have their place, but we're going to see a lot more applica

like Spider Robinson sang..

[hmaon]

"...I taped twenty cents to my transmission
So I could shift my pair 'a dimes..."

Article really misses the point

[steveha]

I read through the whole article, and was just bemused. According to the article, MapReduce isn't as good as a real database at doing the sorts of things real databases do well. Um, okay, I guess, but MapReduce can do quite a lot of other things that they seem to have missed.

Also, I had a major WTF moment when I read this:

Given the experimental evaluations to date, we have serious doubts about how well MapReduce applications can scale.

Empirical evidence to date suggests that MapReduce scales insanely well. Exhibit A: Google, which uses MapReduce running on literally thousands of servers at a time to chew through literally hundreds of terabytes of data. (Google uses MapReduce to index the entire World Wide Web!)

This in turn suggests that the authors of TFA are firmly ensconced in the ivory tower.

They complained that brute-force is slower than indexed searches. Well, nothing about MapReduce rules out the use of indexes; and for common problems, Google can add indexes as desired. (Google uses MapReduce to build their index to the Web in the first place.) And because Google adds servers by the rackful, they have quite a lot of CPU power just waiting to be used. Brute force might not be slower if you split it across thousands of servers!

Likewise, they complain that one can't use standard database report-generating tools with MapReduce; but if the Reduce tasks insert their results into a standard database, one could then use any standard report-generating tools.

MapReduce lets Google folks do crazy one-off jobs like ask every single server they own to check through their system logs for a particular error, and if it's found, return a bunch of config files and log files. Even if you had some sort of distributed database that could run on thousands of machines, any of which might die at any moment, and if you planned ahead and set the machines to copy their system logs into the database, I don't see how a database would be better for that task. That's just a single task I just invented as an example; there are many others, and MapReduce can do them all.

And one of the coolest things about MapReduce is how well it copes with failure. Inevitably some servers will respond very slowly, or will die and not respond; the MapReduce scheduler detects this and sends the Map tasks out to other servers so the job still finishes quickly. And Google keeps statistics on how often a computer is slow. At a lecture, I heard a Google guy explain how there was a BIOS bug that made one server in 50 disable some cache memory, thus greatly slowing down server performance; the MapReduce statistics helped them notice they had a problem, and isolate which computers had the problem.

MapReduce lets you run arbitrary jobs across thousands of machines at once, and all the authors of the article seem to be able to see is that it's not as database-oriented as a real database.

steveha

Re:

[marcosdumay]

Also, I had a major WTF moment when I read this:

Given the experimental evaluations to date, we have serious doubts about how well MapReduce applications can scale.

Empirical evidence to date suggests that MapReduce scales insanely well.

Well, I didn't RTFA, but I also had a major WTF moment when I read that line. I don't understand what all that buzz is about, map reduce is an old approach that is known to work well. Also, it scales the best way any algorithm could scale, its only bottleneck is data

Article misses the point of MapReduce/RDBMS

[duffbeer703]

Sounds like the rumblings of grumpy DBAs.

The whole point of a relational DBMS is to store, link and maintain the integrity of data in tables based on the relationships among the data.

MapReduce is about processing data... it's not focused on maintaining integrity, and the kinds of datasets suitable for MapReduce probably don't have well defined relationships.

Re:

[DragonWriter]

Sounds like the rumblings of grumpy DBAs.

Or maybe talking the (free!) competition from a blog launched by a company trying to sell a different alternative to traditional databases (but one which outwardly looks more like a traditional RDBMS) for an overlapping problem domain (that is, column-oriented databases, which address some of the same distribution and parallelization issues that MapReduce addresses, and target some of the same areas [e.g., "big science"] where it has been suggested that MapReduce mig

Re:

[duffbeer703]

Thanks for pointing that out -- I hadn't realized that the article was part of a corporate blog!

Re:

[IvyKing]

On the other hand TFA may be more of a caution to those who think that Google has solved the "Database on Clusters" problem. The key point is integrity or lack there of (as you pointed out), Google doesn't need to maintain 100% integrity unlike the typical used of a DBMS.

In related news: Screwdrivers suck because...

[DragonWriter]

1) They don't look like hammers,
2) They don't work like hammers,
3) You can already drive in a screw with a hammer,
4) They aren't good at ripping out nails, and
5) They aren't good at driving nails.

Brought to you by The Hammer Column, a blog written by experts in the hammer industry, and launched by Hammertron, makers of a revolutionary new kind of hammer [vertica.com].

They have a point. And it matters

[Animats]

I understand what they're getting at. What makes modern SQL-driven databases so useful is that they optimize queries. If you're asking for every entry in A that's also in B, any modern database will check whether it's faster to look up every A in B, every B in A, or do a match where both databases are read through sequentially by the same key. The best choice depends on the database record counts, available indices, and key types and lengths. The database system figures that out; it's not in the SQL que

Crawl is concurrent database update, not batch

[Animats]

No, crawling the web isn't a map/reduce type problem. It's a large number of long-running processes feeding a database-like engine.

Map/reduce is for batch-like jobs. Long-running systems with intercommunication have to be organized differently.

This coming from the DB Community?

[Qbertino]

Seriously, the DB Community calling something 'backwards' is a joke. Before going after others the DB people maybe should get up to date with their technology and maybe just get rid of that ancient, crappy POS PL called SQL. They should spend their time migrating to some up-to-date LGPLd solution for connection and glue-code. 'Them' using an early 70s interactive terminal hack as cornerstone of their work and calling others 'backwards' is just plain silly.
When rotating HD disks will be replaced by SSDs and

Re:

[geekboy642]

Oh wise and mighty Qbertino,

What, pray tell would you replace SQL with? Bear in mind you have to make it capable of replacing BILLIONS of lines of stored procedures and db code. And whatever magnificent replacement you invent also has to be a great enough improvement over SQL to be worth the TRILLIONS of dollars necessary to retrain the entire SQL economy.

Go ahead, let's hear your awesome idea! Or were you just being an idiot?

Re:

[Qbertino]

I'll give you a counter example:
You now all these Frameworks out there that are gaining traction? Rails, Django, CakePHP, etc.? Well, there is this one, Symfony, that uses a powerfull PHP DB abstraction layer called 'Propel'. One of it's perks is that you don't write any SQL anymore. None. Meaning: You write your transactions and persistance layer interactions in the programming language in which you write everything else aswell. If SQL is so cool, then why don't we have a different PL for each task? We cou

The only thing wrong with map-reduce...

[frank_adrian314159]

... is that they misspelled xapping [homeunix.net].

Stream processing.

[mypalmike]

The whole point of MapReduce is to take an unindexed stream of data and shrink it down based on some criteria where numerous records can be associated (Map) and aggregated (Reduce). It is a process. The *result* of the process is an indexed database, which is often inserted into a relational or time-series database.

It's an apples and oranges comparison, and the author's never eaten an orange.

Index Every Column?

[Tablizer]

a sub-optimal implementation, in that it uses brute force instead of indexing;

For Query-by-Example-like tools, often you cannot predict which columns need indexing: they ALL do. At some point it just seems easier to split the data sets up onto dozens or hundreds of hard-drives and just do a sequencial search on each one in parellel. I cannot say whether it is clearly faster than indexing every column, but it is certainly simpler from a technical standpoint. And, it would possibly require less disk-space

Q: Implementation issues

[harmonica]

The authors raise two interesting questions on skew and data interchange wrt scaling in section 2. (poor impl), issues others supposedly have solved. Has anyone run into those problems with MapReduce? Are they not important when using MapReduce in the "real world"?

Re:

[e4g4]

a search routine that would attempt to pick 5 records at random from a database containing potentially a billion records

Yeah, I'd say an index wouldn't be much help in that situation. A monkey with a keyboard could probably handle it, though.

Also: How's a DBM supposed to profit off that?

[SteeldrivingJon]

And, if mapreduce doesn't generate vast license income for Oracle, it must suck. Imagine the per-processor charges Google would be paying!

What is MapReduce SPECIFICALLY useful for?

[CurtMonash]

At the risk of quoting myself [dbms2.com],

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:

* Specialized indexing of large quantities of data.

Re:

[ScrewMaster]

Now, had Google been around when John Cameron's first Terminator film came out, I'll bet that Skynet would have sprung forth from a Google AI project gone awry.