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> you cannot expect all of them to SSH into a single machine

Why not?

I can do exactly this (KDB warning). Building one or two very beefy machines is 1000x faster, and a lot cheaper than a Hadoop setup.

> The author of the blogpost/article, completely … The author completely … [Here's my opinions about what Hadoop really is]

This is a very real data-volume with two realistic solutions, and thinking that every problem is a nail because you're so invested in your hammer is one of the things that causes people to wait for 26 minutes instead of expecting the problem to take 12 seconds.

And it gets worse: Terrabyte-ram machines are accessible to the kinds of companies that have petabytes of data and the business case for it, and managing the infrastructure and network bandwidth is bigger time sink than you think (or are willing to admit).

If I see value in Hadoop, you might think I'm splitting hairs, so let me be clear: I think Hadoop is a cancerous tumour that has led many smart people to do very very stupid things. It's slow, it's expensive, it's difficult to use, and investing in tooling is just throwing good money after bad.

> Building one or two very beefy machines is 1000x faster, and a lot cheaper than a Hadoop setup.

You have a few petabytes of data and your working set is 50 TB. You put it on two machines. All your data is now on these SGI UV 3000s or whatever. You now need a bunch of experts because any machine failure is a critical data loss situation, and a throughput cliff situation. Took a fairly low-stakes situation (disk failure, let's say) and transformed it into the Mother of All Failures.

And then you've decided that next year your working set won't be over the max for the particular machine type you've decided on. What will you even do then? Sell this and get the new bigger machine? Hook them both up and run things in a distributed fashion?

And then there's the logistics of it all. You're going to use tooling to submit jobs on this machine, and there's got to be configurable process limits, job queues, easy scheduling and rescheduling, etc. etc.

I mean, I'm firmly in the camp that lots of problems are better solved on the giant 64 TB, 256 core machines, but you're selling an idea that has a lot of drawbacks.

Both disk and CPU failures are recoverable on expensive hardware.

And people with 64TB, 256 core machines don't have RAID arrays attached to their machine for this exact reason?

If it's "machines" plural, than you can do replication between the two. There's your fallover in case of complete failure.

> If it's "machines" plural, than you can do replication between the two.

This is the start of a scaling path that winds down Distributed Systems Avenue, and eventually leads to a place called Hadoop.

(Replication and consensus are remarkably difficult problems that Hadoop solves).

Fair 'nuff, but if you don't distribute compute, and you store the dataset locally on all the systems (not necessarily the results of mutations, just the datasets and the work that's being done), you'll still possibly reap massive perf gains over Hadoop in certain contexts.

> you'll still possibly reap massive perf gains over Hadoop in certain contexts.

Certainly, and unfortunately, the exact point at which Hadoop becomes the better option over big iron is generally an ongoing debate and shifting target. But there's no doubt that such a point actually exists.

...I'm not sure if it does. Bain's Law still stands.

But if it does, then it's a pretty big chunk of data, and a very fast network.

Couldn't find anything for Bain's Law, do you have a reference I could follow?

I believe it's a reference to this old (but insightful) comment: https://news.ycombinator.com/item?id=8902739

As such, it is actually "Bane's Rule" which states, "you don't understand a distributed computing problem until you can get it to fit on a single machine first."

(Thanks to nekopa, who also referenced it further down in this thread.)

"You have a few petabytes of data and your working set is 50 TB. You put it on two machines. All your data is now on these SGI UV 3000s or whatever. "

There's usually a combination of apps that work within the memory of the systems plus huge amount of external storage with a clustered filesystem, RAID, etc. Example supercomputer from SGI below since you brought them up that illustrates how they separate compute, storage, management and so on. Management software is available for most clusters to automate or make easy a lot of what you described in later paragraph. They use one. It was mostly a solved problem over a decade ago with sometimes one or two people running supercomputer centers at various universities.

http://www.nas.nasa.gov/hecc/resources/pleiades.html

Yes, but old-school MPI style supercomputer clusters are closer to Hadoop style clusters than standalone machines for the purpose of this discussion.

Both have mechanisms for doing distributed processing on data that is too big for a single machine.

The original argument was that command line tools etc are sufficient. In both these cases they aren't.

Well, this is actually covered in the accompanying blogpost (link in comments below), and he makes a salient point:

"At the same time, it is worth understanding which of these features are boons, and which are the tail wagging the dog. We go to EC2 because it is too expensive to meet the hardware requirements of these systems locally, and fault-tolerance is only important because we have involved so many machines."

Implicitly: the features you mention are only fixes introduced to solve problems that were caused by the chosen approach in the first place.

"The features you mention are only fixes introduced to solve problems that were caused by the chosen approach in the first place."

The chosen approach is the only choice! There is a reason why smart people at thousands of companies use Hadoop. Fault-tolerance and Multi-user support are not mere externalities of the chosen approach but fundamental to performing data science in any organization.

Before you further comment, I highly highly encourage you to get a "Real world" experience in data science by working at a large or even medium sized company. You will realize that outside of trading engines, "faster" is typically the third or fourth most important concern. For data and computed results to be used across organization, they need to stored centrally, similarly hadoop allows you to centralize not only data but also computations. When you take this into account, it does not matter how "Fast" command line tools are on your own laptop. Since now your speed, is determined by the slowest link, which is data transfer over the network.

"Gartner, Inc.'s 2015 Hadoop Adoption Study has found that investment remains tentative in the face of sizable challenges around business value and skills.

Despite considerable hype and reported successes for early adopters, 54 percent of survey respondents report no plans to invest at this time, while only 18 percent have plans to invest in Hadoop over the next two years. Furthermore, the early adopters don't appear to be championing for substantial Hadoop adoption over the next 24 months; in fact, there are fewer who plan to begin in the next two years than already have."

So lots of big businesses are doing just fine without Hadoop and have no plans for beginning to use it. This seems very much at odds with your statement that "The chosen approach is the only choice!"

In fact I would hazard a guess that for businesses that aren't primarily driven by internet pages, big data is generally not a good value proposition, simply because their "big data sets" are very diverse, specialised and mainly used by certain non-overlapping subgroups of the company. Take a car manufacturer, for instance. They will have really big data sets coming out of CFD and FEA analysis by the engineers. Then they will have a lot of complex data for assembly line documentation. Other data sets from quality assurance and testing. Then they will have data sets created by the sales people, other data sets created by accountants, etc. In all of these cases they will have bespoke data management and analysis tools, and the engineers won't want to look at the raw data from the sales team, etc.

My experience echos the OP of this thread; having data in one place backed by a compute engine that can be scaled is a huge boon. Enterprise structure, challenges and opportunities change really fast now, we have mergers new businesses, new products and the requirements to create evidence to support the business conversations that these generate is intense. A single data infrastructure cuts the time required to do this kind of thing from weeks to hours - I've had several engagements where the hadoop team has produced answers in an afternoon that were then later "confirmed" from the proprietary datawarehouses days or weeks later after query testing and firewall hell.

For us Hadoop "done right" is the only game in town for this usecase, because it's dirt cheap per TB and has a mass of tooling. It's true that we've underinvested, but mostly because we've been able to get away with it, but we are running 1000's of enterprise jobs a day through it and without it we would sink like a stone.

Or spend £50m.

Is there anything in your Hadoop that's not "business evidence", financials, user acquisition etc?

My point is that there are many many business decisions driven by analysing non-financial big data sets that physically cannot be done with data crunched out in five hours. These may even require physical testing or new data collection to validate your data analysis.

Like I mentioned, anyone doing proper Engineering (as in, professional liability) will have the same level of confidence in a number coming out of your Hadoop system as they would in a number their colleague Charlie calculated on a napkin at the bar after two beers. Same goes for people in the pharma/biomolecular/chemical industries, oil and gas, mining etc etc.

One of the developments that we're after is radical improvements in data quality and standards of belief (provenance, verification, completeness).

A huge malady that has sometimes effected business is decisions made on the basis of spreadsheets of data that are from unknown sources, contradicted left, right and centre and full of holes.

A single infrastructure helps us do this because we can establish KPI's on the data and control those (as it's coming to the centre rather than a unit providing summaries or updates with delays) we know when data has gone missing and have often been able to do something about it. In the past it was gone, and by the time that was known there was no chance of recovery.

Additionally we are able to cross reference data sources and do our own sanity checks. We have found several huge issues by doing this, systems reporting garbage, systems introducing systematic errors.

I totally agree, if you need to take new readings then you have to wait for the readings to come in before making a decision. This is the same no matter what data infrastructure you are using.

On the other hand there is no reason to view data coming out of Hadoop as any less good than data coming from any other system, apart from the assertion that Hadoop system X is not being well run, which is more of a diagnosis of something that needs fixing than anything else I think.

There are several reasons (outlined above) to believe that a well run data lake can produce high quality data. If an Engineer ignored (for example) a calculation that showed that a bridge was going to fail because the data that hydrated it came out of my system and instead waited for a couple of days for data to arrive from the stress analysis group, metallurgy group and traffic analysis group would they be acting professionally?

Having said all that I do believe that there are issues with running Hadoop data lakes that are not well addressed and stand in the way of delivering value in many domains. Data audit, the ethical challenges of recombination and inference and security challenges generated by super empowered analysts all need to be sorted. Additionally we are only scratching the surface of processes and approaches to managing data quality and detecting data issues.

Like I mentioned, anyone doing proper Engineering (as in, professional liability) will have the same level of confidence in a number coming out of your Hadoop system as they would in a number their colleague Charlie calculated on a napkin at the bar after two beers. Same goes for people in the pharma/biomolecular/chemical industries, oil and gas, mining etc etc.

What are you talking about?

I personally know people working in mining, oil/gas as well as automotive engineering (which you mentioned previously). All rely on Hadoop. I'm sure I could find you some in the other fields too.

Are you seriously thinking Hadoop isn't used outside web companies or something?

Terradata sells Hadoop now, because people are migrating datawarehouses off their older systems. This isn't web stuff, it is everything the business owns.

Yeah, that sounds fun, dozens of undocumented data silos without supervision that some poor bastard will have to troubleshoot as soon as the inevitable showstopper bug crops up.

Most medium and big enterprises have a working set of data around 1-2TB. Enough to fit in memory on a single machine these days.

> .. cannot expect all of them to SSH into a single machine ..

That's pretty much how the Cray supercomputer worked at my old university. SSH to a single server containing compilers and tooling. Make sure any data you need is on the cluster's SAN. Run a few cli tools via SSH to schedule job, and bam - a few moments later your program is crunching data on several tens of thousands of cores.

But, as I pointed out in another comment, what about systems like Manta, which make transitioning from this sort of script to a full-on mapreduce cluster trivial?

Mind, I don't know the performance metrics for Manta vs Hadoop, but it's something to consider...

From my experience organizations have adopted, Hive/Presto/Spark on top of Hadoop. Which actually solves a whole bunch of problems that "script" approach would not. With several added benefits. Executing scripts (cat, grep, uniq, sort) do not provide similar, benefits, while they might be faster. A dedicated solution such as Presto by Facebook will provide similar if not even faster results.

https://prestodb.io/

Ah, so it doesn't solve data storage, and runs SQL queries, which are less capable than UNIX commmands. If your data's stuck inside 15 SQL DBs, than that'd make sense, but a lot of data is just stored in flat files. And you know what's really good at analyzing flat files? Unix commands.

Did you even read it? Presto reads directly from HDFS, which is as close to distributed "flat files" as you can get. As far as "SQL being less capable than UNIX commands", you have got to be kidding me. SQL allows type checking, conversion, joins all of which are difficult if not impossible with grep | uniq | sort etc.

I read it.

>Presto allows querying data where it lives, including Hive, Cassandra, relational databases or even proprietary data stores. A single Presto query can combine data from multiple sources, allowing for analytics across your entire organization.

That doesn't sound like HDFS to me. I mean, I assume it can read from HDFS, but Presto is backend agnostic. You could probably write code to run it on Manta. That would be neat for people who like Presto, I guess.

Type checking and conversions, no, and table joins only matter when you're handling relational data.

Also, how many formats can Presto handle? Unix utilities can handle just about any tabular data, and you can run them against non-tabular data in a pinch (although nobody reccomends it). I doubt Presto is that versitile.

Hive operates on top of HDFS.

Presto absolutley runs directly on HDFS.

Huh. Well then, I don't understand HDFS, or Facebook needs to fix Presto's front page. Both are reasonably likely.

Totally agree. It'd be relatively trivial to automate converting this script into a distributed application. Haven't checked Manta out, but I will. For ultimate performance though right now you could go for something like OpenMP + MPI which gets you scalability/fault-tolerance. In a few months you'll also be able to use something like RaftLib as a dataflow/stream processing API for distributed computation (almost ready to roll out the distributed back-end). MPI though has decades of research in HPC to make it the most robust distributed compute platform in existence (though not the most easy to use). You think your big data problems are big...nah, supercomputers were doing todays big data back in the late 90's. Just a totally different crowd with slightly different solutions. MPI is hard to use, Spark/Storm is much easier...but much slower.

> For any non-trivial organization ( > 5 users), you cannot expect all of them to SSH into a single machine.

That's exactly the use case we built Userify for (https://userify.com)