| Andrew Cooke | Contents | Latest | RSS | Twitter | Previous | Next

C[omp]ute

Welcome to my blog, which was once a mailing list of the same name and is still generated by mail. Please reply via the "comment" links.

Always interested in offers/projects/new ideas. Eclectic experience in fields like: numerical computing; Python web; Java enterprise; functional languages; GPGPU; SQL databases; etc. Based in Santiago, Chile; telecommute worldwide. CV; email.

Personal Projects

Lepl parser for Python.

Colorless Green.

Photography around Santiago.

SVG experiment.

Professional Portfolio

Calibration of seismometers.

Data access via web services.

Cache rewrite.

Extending OpenSSH.

C-ORM: docs, API.

Last 100 entries

Credit Card Processing for Cat Soft LLC; Discover new movies on demand in our online cinema; Tasting; Credit Card Processing for Cat Soft LLC; Apple + Kiwi Jam; Hit Me; Increase Efficiency with GPS Vehicle Tracking for Cat Soft LLC; Sudoku - CSP + Chaos; Recycling Electronics In Santiago; Vector Displays in OpenGL; Call Center Services for Cat Soft LLC; And Anti-Aliased; OpenGL - Render via Intermediate Texture; And Garmin Connect; Using Garmin Forerunner 230 With Linux; Payroll Service Quotes for Cat Soft LLC; (Beating Dead Horse) StackOverflow; Current State of Justice in China; Now Is Cat Soft LLC's Chance To Save Up To 32% On Mail; Axiom of Determinacy; Ewww; Fee Chaos Book; Course on Differential Geometry; Increase Efficiency with GPS Vehicle Tracking for Cat Soft LLC; Okay, but...; Sparse Matrices, Deep Learning; Sounds Bad; Applebaum Rape; Tomato Chutney v4; Have to add...; Culturally Liberal and Nothing More; Weird Finite / Infinite Result; Your diamond is a beaten up mess; Maths Books; Good Bike Route from Providencia / Las Condes to Panul\; Iain Pears (Author of Complex Plots); Plum Jam; Excellent; More Recently; For a moment I forgot StackOverflow sucked; A Few Weeks On...; Chilean Book Recommendations; How To Write Shared Libraries; Jenny Erpenbeck (Author); Dijkstra, Coins, Tables; Python libraries error on OpenSuse; Deserving Trump; And Smugness; McCloskey Economics Trilogy; cmocka - Mocks for C; Concept Creep (Americans); Futhark - OpenCL Language; Moved / Gone; Fan and USB issues; Burgers in Santiago; The Origin of Icosahedral Symmetry in Viruses; autoenum on PyPI; Jars Explains; Tomato Chutney v3; REST; US Elections and Gender: 24 Point Swing; PPPoE on OpenSuse Leap 42.1; SuperMicro X10SDV-TLN4F/F with Opensuse Leap 42.1; Big Data AI Could Be Very Bad Indeed....; Cornering; Postcapitalism (Paul Mason); Black Science Fiction; Git is not a CDN; Mining of Massive Data Sets; Rachel Kaadzi Ghansah; How great republics meet their end; Raspberry, Strawberry and Banana Jam; Interesting Dead Areas of Math; Later Taste; For Sale; Death By Bean; It's Good!; Tomato Chutney v2; Time ATAC MX 2 Pedals - First Impressions; Online Chilean Crafts; Intellectual Variety; Taste + Texture; Time Invariance and Gauge Symmetry; Jodorowsky; Tomato Chutney; Analysis of Support for Trump; Indian SF; TP-Link TL-WR841N DNS TCP Bug; TP-Link TL-WR841N as Wireless Bridge; Sending Email On Time; Maybe run a command; Sterile Neutrinos; Strawberry and Banana Jam; The Best Of All Possible Worlds; Kenzaburo Oe: The Changeling; Peach Jam; Taste Test; Strawberry and Raspberry Jam; flac to mp3 on OpenSuse 42.1; Also, Sebald; Kenzaburo Oe Interview

© 2006-2015 Andrew Cooke (site) / post authors (content).

Cloud Computing

From: andrew@...

Date: Fri, 9 Jul 2010 18:13:57 -0400

This is a short summary of Cloud Computing and various related ("trendy")
technologies.

The driving force for much of these ideas seems to have come from Amazon and
Google as they have dealt with scaling their businesses (it's strange how much
Amazon is involved in this, rather than Microsoft, for example). The simple
idea behind Cloud Computing is that computers, and the basic functionality
they provide, are becoming "comoditized". The usual comparison is with an
Electric Company, that supplies electricity. Just as you don't care exactly
what generator is used, so you shouldn't need to care what hardware is used to
run your software.

There are two main approaches to this, one taken by Amazon, and the other by
Google.

Amazon's approach is "Infrastructure as a Service" (Amazon Web Services,
AWS). By that, they mean that they provide basic infrastructure as the
"commodity". So you can configure a "machine" and then run N instances, where
N varies depending on the current load. Similarly, you can access "databases"
and "permanent stores". So this is very like "normal" computing, except that
you have services that are "elastic" - they scale as required, typically by
adding instances.

Google's approach is "Platform as a Service". By that, they mean that they
offer a set of APIs that you can use to write a webservice, and then they will
run that service for you, scaling as necessary. So you work at a higher level
of abstraction: you write code that handles a single thread; Google will make
sure that as many threads as necessary run. For data storage, you save data to
a kind-of database (more like a hash table) and you don't care about the lower
level details.

So Google's "App Engine" approach is easier to use (although you need to learn
a whole new API in either Python or Java), but more limiting. In very simple
terms, Amazon's approach is like owning an expandable set of virtual machines
while Google's approach is more like having an infinitely powerful single
machine. One cool detail about Google's approach is that, for small services,
it's free.

And of course there are many more providers, generally doing things like
Amazon (partly because no-one apart from Google is big enough that it's worth
learning a new API just to use their service).


Now, tangentially related to that, are the spin-off technologies. Two are
particularly important, and are related: MapReduce and NoSQL.

To understand both, and how they are related, it's probably easiest to start
with Google. As you can imagine, Google's web search is a huge database spread
across many machines. And to run a search they need to run tasks on each
machine and then collect the results. This process is called MapReduce: "map"
is related to dividing the work out, doing the same thing on various machines;
"reduce" is the process of collecting the results together.

Now MapReduce doesn't make much sense without data. So the flip side of
MapReduce is the distributed data. And that implies some kind of distributed
database. It turns out that databases can do 2 out of 3 things well, where the
3 things are: availability; consistency; partitions. SQL databases are "AC",
but aren't so good at P. Spreading data across various machines requires
partitioning (P), so new technologies have appeared that are either "AP"
(don't always give consistent results) or "CP" (are not guaranteed
available). These tend not to use SQL and are collectively called "NoSQL".

Curiously, Google, who invented MapReduce, don't actually let the public use
it. However, Amazon do support it as a service, and there are many 3rd party
solutions that you can also run yourself (most famously, CouchDB, MongoDB,
HBase and Cassandra). All of these support MapReduce implementations. And
Google's App Engine (its PaaS - see above) does have support for something
similar, but it's not the same MapReduce that Google use internally. 

Andrew

More on CAP

From: andrew cooke <andrew@...>

Date: Mon, 12 Jul 2010 05:23:39 -0400

This clarifies CAP -
http://dbmsmusings.blogspot.com/2010/04/problems-with-cap-and-yahoos-little.html

Andrew

Comment on this post