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From Sudhir.Kumar <Sudhir.Ku...@target.com>
Subject Re: Reliability of Hadoop
Date Sat, 28 May 2016 18:57:26 GMT
Hi Deepak,

Your assumption that folks would write bad algorithms because a platform is efficient is wrong
and misguided. Wrong algorithm on efficient platform would bite you.

From: Deepak Goel <deicool@gmail.com<mailto:deicool@gmail.com>>
Date: Saturday, May 28, 2016 at 1:47 PM
To: Dejan Menges <dejan.menges@gmail.com<mailto:dejan.menges@gmail.com>>
Cc: "J. Rottinghuis" <jrottinghuis@gmail.com<mailto:jrottinghuis@gmail.com>>,
Arun Natva <arun.natva@gmail.com<mailto:arun.natva@gmail.com>>, user <user@hadoop.apache.org<mailto:user@hadoop.apache.org>>
Subject: Re: Reliability of Hadoop

My thoughts inline (Sorry for my poor english earlier, I will try to explain what I mean again)


On Sat, May 28, 2016 at 1:17 AM, Dejan Menges <dejan.menges@gmail.com<mailto:dejan.menges@gmail.com>>
wrote:
Hi Deepak,

Hadoop is just platform (Hadoop and all around it). Toolset to do what you want to do.

If you are writing bad code you can't blame programming language. It's you not being able
to write good code. There's also nothing bad in using commodity hardware (and not sure I understand
whats' commodity software). In this very moment, while we are exchanging this - how much do
we know or care on which hardware mail servers are running? We don't, neither we care.

**********************Deepak**********************
I am not saying Hadoop is bad at all. Infact I am saying it is wonderful. However the algorithms
written in the past decades in (OS, JVM, our applications) are perhaps not the best in terms
of performance. Infact they are governed by "Inverse Moore's law" which is something like
"The quality of software in terms of performance reduces by half every year". Now with coming
of Hadoop, the algorithms are run in parallel on many small computers, and they don't have
to be inefficient at all. So in all likelihood, the quality of our algorithms in OS, JVM,
application (not Hadoop!) will decrease further. As we are all from the software industry,
we must guard ourselves against this pitfall.

As to your question, whether we care about the hardware mail servers? Well it is subjective
and person dependent. From my perspective I do care (and I might be writing this to satisfy
my ego!). For example, I do keep thinking on what servers do our software run on. Which CPU?
What is the technology inside the CPU? How much heat is the CPU generating? How smaller and
faster the CPU can get? And so on...
********************** Deepak**********************


For whitepapers and use cases internet is full of them.
**********************Deepak**********************
I tried googling (Assuming Google is good at its job of finding what I need) and could not
find any whitepapers on the "cost v/s performance benefit between the two technology". Can
you please provide a link if you have?

**********************Deepak**********************

My company is keeping majority of the really important data in Hadoop ecosystem. Some of the
best software developers I met so far are writing different types of code from it, from analytics
to development of in house software and plugins for different things.

**********************Deepak**********************
I will draw inspiration from your example above and improve my software skills (Actually I
am almost fresh at software development and am starting from scratch). Thank You. Appreciate
it. :)

**********************Deepak**********************

However, I'm not sure that anyone on any mailing list can give you answers than you need.
I would start with official documentation and understanding how specific component works in
depth and why it works the way it works.

My 2c

Cheers,
Dejan

On Fri, May 27, 2016 at 9:41 PM Deepak Goel <deicool@gmail.com<mailto:deicool@gmail.com>>
wrote:
Sorry once again if I am wrong, or my comments are without significance

I am not saying Hadoop is bad or good...It is just that Hadoop might be indirectly encouraging
commodity hardware and software to be developed which is convenient but might not be very
good (also the cost factor is unproven with no proper case studies or whitepaper)

It is like the fast food industry, which is very convenient (a commodity) but causing obesity
all over the world (And hence also causing many illness, poor health, social trauma therefore
the cost of a burger to anyone is actually far more than what a company charges when you eat
it)

In effect what Hadoop (and all the other commercial software around it) is saying that its
ok if you have bad software (Application, JVM, OS), I will provide another software which
will hide all the problems of yours... We might all just go the obesity way in the software
industry too



Hey

Namaskara~Nalama~Guten Tag~Bonjour



   --
Keigu

Deepak
73500 12833
www.simtree.net<http://www.simtree.net>, deepak@simtree.net<mailto:deepak@simtree.net>
deicool@gmail.com<mailto:deicool@gmail.com>

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On Sat, May 28, 2016 at 12:51 AM, J. Rottinghuis <jrottinghuis@gmail.com<mailto:jrottinghuis@gmail.com>>
wrote:
We run several clusters of thousands of nodes (as do many companies), our largest one has
over 10K nodes. Disks, machines, memory, and network fail all the time. The larger the scale,
the higher the odds that some machine is bad in a given day. On the other hand, scale helps.
If a single node our of 10K fails, 9,999 others participate in re-distributing state. Even
a rack failure isn't a big deal most of the time (plus typically a rack fails due to a TOR
issue, so the data is offline, but typically not lost permanently).

Hadoop is designed to deal with this, and by-and-large it does. Critical components (such
as Namenodes) can be configured to run in an HA pair with automatic failover. There is quite
a bit of work going on by many in the Hadoop community to keep pushing the boundaries of scale.

A node or a rack failing in a large cluster actually has less impact than at smaller scale.
With a 5-node cluster, if 1 machine crashes you've taken 20% capacity (disk and compute) offline.
1 out of 1K barely registers. Ditto with a 3 rack cluster. Loose a rack and 1/3rd of your
capacity is offline.

It is large-scale coordinated failure you should worry about. Think several rows of racks
coming offline due to power failure, a DC going offline due to fire in the building etc. Those
are hard to deal with in software within a single DC. They should also be more rare, but as
many companies have experienced, large scale coordinated failures do occasionally happen.

As to your question in the other email thread, it is a well-established pattern that scaling
horizontally with commodity hardware (and letting software such as Hadoop deal with failures)
help with both scale and reducing cost.

Cheers,

Joep


On Fri, May 27, 2016 at 11:02 AM, Arun Natva <arun.natva@gmail.com<mailto:arun.natva@gmail.com>>
wrote:
Deepak,
I have managed clusters where worker nodes crashed, disks failed..
HDFS takes care of the data replication unless you loose too many of the nodes where there
is not enough space to fit the replicas.



Sent from my iPhone

On May 27, 2016, at 11:54 AM, Deepak Goel <deicool@gmail.com<mailto:deicool@gmail.com>>
wrote:


Hey

Namaskara~Nalama~Guten Tag~Bonjour

We are yet to see any server go down in our cluster nodes in the production environment? Has
anyone seen reliability problems in their production environment? How many times?

Thanks
Deepak
   --
Keigu

Deepak
73500 12833
www.simtree.net<http://www.simtree.net>, deepak@simtree.net<mailto:deepak@simtree.net>
deicool@gmail.com<mailto:deicool@gmail.com>

LinkedIn: www.linkedin.com/in/deicool<http://www.linkedin.com/in/deicool>
Skype: thumsupdeicool
Google talk: deicool
Blog: http://loveandfearless.wordpress.com
Facebook: http://www.facebook.com/deicool

"Contribute to the world, environment and more : http://www.gridrepublic.org
"



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