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  • What is the best practice with KML files when adding geositemap?

    - by Floran
    Im not sure how to deal with kml files. Now important particularly in reference to the Google Venice update. My site basically is a guide of many company listings (sort of Yellow Pages). I want each company listing to have a geolocation associated with it. Which of the options I present below is the way to go? OPTION 1: all locations in a single KML file with a reference to that KML file from a geositemap.xml MYGEOSITEMAP.xml <?xml version="1.0" encoding="UTF-8"?> <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:geo="http://www.google.com/geo/schemas/sitemap/1.0"> <url><loc>http://www.mysite.com/locations.kml</loc> <geo:geo> <geo:format>kml</geo:format></geo:geo></url> </urlset> ALLLOCATIONS.kml <kml xmlns="http://www.opengis.net/kml/2.2" xmlns:atom="http://www.w3.org/2005/Atom"> <Document> <name>MyCompany</name> <atom:author> <atom:name>MyCompany</atom:name> </atom:author> <atom:link href="http://www.mysite.com/locations/3454/MyCompany" rel="related" /> <Placemark> <name>MyCompany, Kalverstraat 26 Amsterdam 1000AG</name> <description><![CDATA[<address><a href="http://www.mysite.com/locations/3454/MyCompany">MyCompany</a><br />Address: Kalverstraat 26, Amsterdam 1000AG <br />Phone: 0646598787</address><p>hello there, im MyCompany</p>]]> </description><Point><coordinates>5.420686499999965,51.6298808,0</coordinates> </Point> </Placemark> </Document> </kml> <kml xmlns="http://www.opengis.net/kml/2.2" xmlns:atom="http://www.w3.org/2005/Atom"> <Document> <name>MyCompany</name><atom:author><atom:name>MyCompany</atom:name></atom:author><atom:link href="http://www.mysite.com/locations/22/companyX" rel="related" /><Placemark><name>MyCompany, Rosestreet 45 Amsterdam 1001XF </name><description><![CDATA[<address><a href="http://www.mysite.com/locations/22/companyX">companyX</a><br />Address: Rosestreet 45, Amsterdam 1001XF <br />Phone: 0642195493</address><p>some text about companyX</p>]]></description><Point><coordinates>5.520686499889632,51.6197705,0</coordinates></Point></Placemark> </Document> </kml> OPTION 2: a separate KML file for each location and a reference to each KML file from a geositemap.xml (kml files placed in a \kmlfiles folder) MYGEOSITEMAP.xml <?xml version="1.0" encoding="UTF-8"?> <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:geo="http://www.google.com/geo/schemas/sitemap/1.0"> <url><loc>http://www.mysite.com/kmlfiles/3454_MyCompany.kml</loc> <geo:geo> <geo:format>kml</geo:format></geo:geo></url> <url><loc>http://www.mysite.com/kmlfiles/22_companyX.kml</loc> <geo:geo> <geo:format>kml</geo:format></geo:geo></url> </urlset> *3454_MyCompany.kml* <kml xmlns="http://www.opengis.net/kml/2.2" xmlns:atom="http://www.w3.org/2005/Atom"> <Document><name>MyCompany</name><atom:author><atom:name>MyCompany</atom:name></atom:author><atom:link href="http://www.mysite.com/locations/3454/MyCompany" rel="related" /><Placemark><name>MyCompany, Kalverstraat 26 Amsterdam 1000AG</name><description><![CDATA[<address><a href="http://www.mysite.com/locations/3454/MyCompany">MyCompany</a><br />Address: Kalverstraat 26, Amsterdam 1000AG <br />Phone: 0646598787</address><p>hello there, im MyCompany</p>]]></description><Point><coordinates>5.420686499999965,51.6298808,0</coordinates></Point></Placemark> </Document> </kml> *22_companyX.kml* <kml xmlns="http://www.opengis.net/kml/2.2" xmlns:atom="http://www.w3.org/2005/Atom"> <Document><name>companyX</name><atom:author><atom:name>companyX</atom:name></atom:author><atom:link href="http://www.mysite.com/locations/22/companyX" rel="related" /><Placemark><name>companyX, Rosestreet 45 Amsterdam 1001XF </name><description><![CDATA[<address><a href="http://www.mysite.com/locations/22/companyX">companyX</a><br />Address: Rosestreet 45, Amsterdam 1001XF <br />Phone: 0642195493</address><p>some text about companyX</p>]]></description><Point><coordinates>5.520686499889632,51.6197705,0</coordinates></Point></Placemark> </Document> </kml> OPTION 3?

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  • Correctly parsing an ATOM feed

    - by Joseph
    I currently have setup a Python script that uses feedparser to read a feed and parse it. However, I have recently come across a problem with the date parsing. The feed I am reading contains <modified>2010-05-05T24:17:54Z</modified> - which comes up in Python as a datetime object - 2010-05-06 00:17:54. Notice the discrepancy: the feed entry was modified on the 5th of may, while python reads it as the 6th. So the question is why this is happening. Is the ATOM feed (that is, the one who created the feed) wrong by putting the time as 24:17:54, or is my python script wrong in the way it treats it. And can I solve this?

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  • Link to RSS/Atom feed, relative, doesn't work in Firefox

    - by Adrian Smith
    I have a weird problem. I generate a HTML page, hosted let's say at http://www.x.com/stuff which contains <head> <link type="application/atom+xml" rel="alternate" href="/stuff/feed"/> .. </head> The result is: In IE7 all works well - you can click on the feed icon in the browser and the feed is displayed In Firefox, view source, click on the linked /stuff/feed and you see the source of the feed, so that works as expected In Firefox, view the page (not source), then click on the feed icon in the address bar, I get an error that it can't retrieve the URL feed://http//www.x.com/stuff/feed So the problem is, that it's appending feed:// to the front of the URL and then taking out the colon : after the http. I understand that feed: is HTTP anyway so perhaps the adding of that isn't a big problem. But anyway, the fact is, that URL Firefox generates out of my <link> tag doesn't work. I have considered making the URL absolute, but I haven't found any evidence that those URLs have to be absolute, nor can I understand why that would be the case. And for various reasons it would be inconvenient in my code to generate an absolute URL. I can do it if necessary but I would prefer to see proof (e.g. specification, or Mozilla bug report) that it's necessary before making my code messy What do you think? Does anyone know of any evidence that the URL should be absolute? Or am I doing something else wrong? It seems such a simple/obvious tag, where nothing could go wrong, but I can't get it to work.

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  • How low-power can a home server get?

    - by Halik
    I've got quite simple question actually. How green, low-power and efficient x86 home server can I build using consumer parts with rather constrained budget. After looking through some Google hits I've found out that system based on dual-core atom, some modest mITX board (gigabit lan, integrated audio and gfx etc), one RAM module and one 'green' WD HDD, powered by picoITX PSU uses about 30W at idle up to 40 at load. Can you get lower (or how much lower) then that? Maybe some VIA nano chips, or single core atom? My home server would take care of some back-upping mixed with little ftp/http traffic.

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  • intel atom getting too hot

    - by user59565
    I own an Asus 1215N which is getting very hot Intel Atom 525D dual core ION 2 (geforce 220 + GMA 3150) 4 GB RAM Ubuntu 12.04 it hits 86 C at idle. Some times (at load or turned on 1 hour) it shuts down due to heat, in Windows 7 it runs idle at 49 C. I tried an acpi call to shut down the nVidia chip which is cooled together with the Atom chip. That didn't solve the problem. To check up to see if it really turned off I checked how much power the laptop consumed, it only went from using 1400 mW to 860 mW, no changes in heat. I also tried reapply the standard heat adhesive, the old heat adhesive made it run at 97 (it couldn't even put up a useful install of Ubuntu). This really annoys me, as Ubuntu is the OS of choice to me. Should I try compile the kernel? Is it true that compile for a P4 is the better choice to the atom, when compiling the kernel for this processor architecture? Now I tried compiling the kernel for atom. Now temperature is 83 C (think the drop has more to do with ambient temp than the customized kernel) help appreciated

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  • Open Grid Engine or Akka/Something more fault tolerant?

    - by Mike Lyons
    My use case is that I have a pipeline of independent, stand alone programs, that I want to execute in a certain order on specific pieces of data that our output from previous pipeline stages. The pipeline is entirely linear and doesn't do anything in terms of alternate paths through the pipe. I'm currently using SGE to do this and it works OK, however occasionally a job will overstep it's memory bounds, fail, and all jobs that require that output data will fail. The pipe needs to be restarted in that case, and it seems that whatever is providing the fault tolerance in akka might solve that for me?

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  • How to detect if a page is an RSS or ATOM feed

    - by Pepper
    Hello, I'm currently building a new online Feed Reader in PHP. One of the features i'm working on is feed auto-discovery. If a user enters a website URL, the script will detect that its not a feed and look for the real feed URL by parsing the HTML for the proper tag. The problem is, the way im currently detecting if the URL is a feed or a website only works part of the time, and I know it can't be the best solution. Right now im taking the CURL response and running it through simplexml_load_string, if it can't parse it I treat it as a website. Here is the code. $xml = @simplexml_load_string( $site_found['content'] ); if( !$xml ) // this is a website, not a feed { // handle website } else { // parse feed } Obviously, this isn't ideal. Also, when it runs into an HTML website that it can parse, it thinks its a feed. Any suggestions on a good way of detecting the difference between a feed or non-feed in PHP? Thanks, Pepper http://feedingo.com

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  • Atom feed validator keeps showing Self reference doesn't match document location

    - by Dino
    I am creating an atom feed, but when I validate it I keep getting: Self reference doesn't match document location and the specific line that is causing the error is: <link rel="self" type="application/atom+xml" href="http://www.example.com/test.rss"/> Please can anyone advise what the error is? Ps. I noticed an up arrow just at the end of that line. (presumably something to do with that bbut not sure)

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  • Dans le Cloud computing, un tutoriel pour débutant, traduit par Nicolas vieux et Vincent Viale

    Qu'est-ce que le Cloud computing ? Le Cloud computing est devenu le nouveau mot à la mode tirée en grande partie par le marketing et les offres de services de grands groupes comme Google, IBM et Amazon. Cloud computing est la prochaine étape dans l'évolution d'Internet. Cloud computing fournit le moyen par lequel tout - de la puissance de calcul de l'infrastructure informatique, des applications, des processus d'affaires pour une autoentreprise - peut être livré comme un service où et quand vous en avez besoin.

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  • Intel to Unleash Atom-ic Power at Computex

    <b>Hardware Central:</b> "Intel plans to introduce a series of new Atom processors at the opening of the giant Computex show in Taipei this week, as well as offer a preview a number of other offerings. But Atom will be the star of the show."

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  • Parallel computing in .net

    - by HotTester
    Since the launch of .net 4.0 a new term that has got into lime light is parallel computing. Does parallel computing provide us some benefits or its just another concept or feature. Further is .net really going to utilize it in applications ? Further is parallel computing different from parallel programming ? Kindly throw some light on the issue in perspective of .net and some examples would be helpful. Thanks...

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  • The future of cloud computing? [closed]

    - by Vimvq1987
    As far as I know, cloud computing is growing rapidly. Amazon EC2, Google App Engine, Microsoft Windows Azure...But I can't imagine how cloud computing will change the world. Will cloud computing will play the main role in software industry? Will our data be stored at one place and then can be accessed from any where? Shall we need powerful PCs no more because everything will be processed at "cloud"? Thank you so much

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  • Cloud computing?

    - by Suraj
    I'm writing a report advising on future technologies that a manufacturing company could use. I've highlighted a number of advanced manufacturing technologies such as CAD etc. However, I want to bring cloud computing into the report just to score some extra points. I am not sure how one would bring together cloud computing with the advanced technologies though. Basically what would be the process of integrating these technologies into a cloud computing "environment"? Say the organisation buys a CAD package, how could they make use of cloud computing here?

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  • Open source Distributed computing tool

    - by Prasenjit Chatterjee
    I want to set up distributed computing on my Local Area Network consisting a bunch of PCs. Say for the time being each one has the same OS - Windows 7. Is there any opensource tool available so that I can share the resources of these PCs over the LAN and increase the speed of my applications and the memory space. I know that if its a graphics intensive application then, it is not very practical, because the speed of LAN is much slower than Graphics processors. But I only want to share general applications, some basic softwares, Programming language IDEs etc. Can anyone shed some light on it? Thanks in Advance..

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  • Cloud Computing - Multiple Physical Computers, One Logical Computer

    - by Koobz
    I know that you can set up multiple virtual machines per physical computer. I'm wondering if it's possible to make multiple physical computers behave as one logical unit? Fundamentally the way I imagine it working is that you can throw 10 computers into a facility one day. You've got one client that requires the equivalent of two computers worth, and 100 others that eat up the remaining 8. As demands change you're just reallocating logical resources, maybe the 2 computer client now requires a third physical system. You just add it to the cloud, and don't worry about sharding the database, or migrating data over to a new server. Can it work this way? If yes, why would anyone ever do things like partition their database servers anymore? Just add more computing resources. You scale horizontally with the hardware, but your server appears to scale vertically. There's no need to modify your application's infrastructure to support multiple databases etc.

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  • Cloud Computing - Multiple Physical Computers, One Logical Computer

    - by bundini
    I know that you can set up multiple virtual machines per physical computer. I'm wondering if it's possible to make multiple physical computers behave as one logical unit? Fundamentally the way I imagine it working is that you can throw 10 computers into a facility one day. You've got one client that requires the equivalent of two computers worth, and 100 others that eat up the remaining 8. As demands change you're just reallocating logical resources, maybe the 2 computer client now requires a third physical system. You just add it to the cloud, and don't worry about sharding the database, or migrating data over to a new server. Can it work this way? If yes, why would anyone ever do things like hand partition their database servers anymore? Just add more computing resources. You scale horizontally with the hardware, but your server appears to scale vertically. There's no need to modify your application's supporting infrastructure to support multiple databases etc.

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  • cloud computing ? Eucalyptus

    - by neolix
    Hi Greeting!! I want to setup small cloud computing using our old 2 core server system? we are new to cloud system we have google for the same. We are looking host VM's on top any one has done pls share me doc or how to ? we have 50 plus server which we are not using. 2 core each 4GB RAM, 1TB HDD centos is my base os we looking host windows. Right now we can use this server only paravirtualization ignore my english Thanks

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  • Configuration management in support of scientific computing

    - by Sharpie
    For the past few years I have been involved with developing and maintaining a system for forecasting near-shore waves. Our team has just received a significant grant for further development and as a result we are taking the opportunity to refactor many components of the old system. We will also be receiving a new server to run the model and so I am taking this opportunity to consider how we set up the system. Basically, the steps that need to happen are: Some standard packages and libraries such as compilers and databases need to be downloaded and installed. Some custom scientific models need to be downloaded and compiled from source as they are not commonly provided as packages. New users need to be created to manage the databases and run the models. A suite of scripts that manage model-database interaction needs to be checked out from source code control and installed. Crontabs need to be set up to run the scripts at regular intervals in order to generate forecasts. I have been pondering applying tools such as Puppet, Capistrano or Fabric to automate the above steps. It seems perfectly possible to implement most of the above functionality except there are a couple usage cases that I am wondering about: During my preliminary research, I have found few examples and little discussion on how to use these systems to abstract and automate the process of building custom components from source. We may have to deploy on machines that are isolated from the Internet- i.e. all configuration and set up files will have to come in on a USB key that can be inserted into a terminal that can connect to the server that will run the models. I see this as an opportunity to learn a new tool that will help me automate my workflow, but I am unsure which tool I should start with. If any member of the community could suggest a tool that would support the above workflow and the issues specific to scientific computing, I would be very grateful. Our production server will be running Linux, but support for OS X would be a bonus as it would allow the development team to setup test installations outside of VirtualBox.

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  • Configuration management in support of scientific computing

    - by Sharpie
    For the past few years I have been involved with developing and maintaining a system for forecasting near-shore waves. Our team has just received a significant grant for further development and as a result we are taking the opportunity to refactor many components of the old system. We will also be receiving a new server to run the model and so I am taking this opportunity to consider how we set up the system. Basically, the steps that need to happen are: Some standard packages and libraries such as compilers and databases need to be downloaded and installed. Some custom scientific models need to be downloaded and compiled from source as they are not commonly provided as packages. New users need to be created to manage the databases and run the models. A suite of scripts that manage model-database interaction needs to be checked out from source code control and installed. Crontabs need to be set up to run the scripts at regular intervals in order to generate forecasts. I have been pondering applying tools such as Puppet, Capistrano or Fabric to automate the above steps. It seems perfectly possible to implement most of the above functionality except there are a couple usage cases that I am wondering about: During my preliminary research, I have found few examples and little discussion on how to use these systems to abstract and automate the process of building custom components from source. We may have to deploy on machines that are isolated from the Internet- i.e. all configuration and set up files will have to come in on a USB key that can be inserted into a terminal that can connect to the server that will run the models. I see this as an opportunity to learn a new tool that will help me automate my workflow, but I am unsure which tool I should start with. If any member of the community could suggest a tool that would support the above workflow and the issues specific to scientific computing, I would be very grateful. Our production server will be running Linux, but support for OS X would be a bonus as it would allow the development team to setup test installations outside of VirtualBox.

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  • Distributed storage and computing

    - by Tim van Elteren
    Dear Serverfault community, After researching a number of distributed file systems for deployment in a production environment with the main purpose of performing both batch and real-time distributed computing I've identified the following list as potential candidates, mainly on maturity, license and support: Ceph Lustre GlusterFS HDFS FhGFS MooseFS XtreemFS The key properties that our system should exhibit: an open source, liberally licensed, yet production ready, e.g. a mature, reliable, community and commercially supported solution; ability to run on commodity hardware, preferably be designed for it; provide high availability of the data with the most focus on reads; high scalability, so operation over multiple data centres, possibly on a global scale; removal of single points of failure with the use of replication and distribution of (meta-)data, e.g. provide fault-tolerance. The sensitivity points that were identified, and resulted in the following questions, are: transparency to the processing layer / application with respect to data locality, e.g. know where data is physically located on a server level, mainly for resource allocation and fast processing, high performance, how can this be accomplished? Do you from experience know what solutions provide this transparency and to what extent? posix compliance, or conformance, is mentioned on the wiki pages of most of the above listed solutions. The question here mainly is, how relevant is support for the posix standard? Hadoop for example isn't posix compliant by design, what are the pro's and con's? what about the difference between synchronous and asynchronous opeartion of a distributed file system. Though a synchronous distributed file system has the preference because of reliability it also imposes certain limitations with respect to scalability. What would be, from your expertise, the way to go on this? I'm looking forward to your replies. Thanks in advance! :) With kind regards, Tim van Elteren

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  • Big Data – Role of Cloud Computing in Big Data – Day 11 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the NewSQL. In this article we will understand the role of Cloud in Big Data Story What is Cloud? Cloud is the biggest buzzword around from last few years. Everyone knows about the Cloud and it is extremely well defined online. In this article we will discuss cloud in the context of the Big Data. Cloud computing is a method of providing a shared computing resources to the application which requires dynamic resources. These resources include applications, computing, storage, networking, development and various deployment platforms. The fundamentals of the cloud computing are that it shares pretty much share all the resources and deliver to end users as a service.  Examples of the Cloud Computing and Big Data are Google and Amazon.com. Both have fantastic Big Data offering with the help of the cloud. We will discuss this later in this blog post. There are two different Cloud Deployment Models: 1) The Public Cloud and 2) The Private Cloud Public Cloud Public Cloud is the cloud infrastructure build by commercial providers (Amazon, Rackspace etc.) creates a highly scalable data center that hides the complex infrastructure from the consumer and provides various services. Private Cloud Private Cloud is the cloud infrastructure build by a single organization where they are managing highly scalable data center internally. Here is the quick comparison between Public Cloud and Private Cloud from Wikipedia:   Public Cloud Private Cloud Initial cost Typically zero Typically high Running cost Unpredictable Unpredictable Customization Impossible Possible Privacy No (Host has access to the data Yes Single sign-on Impossible Possible Scaling up Easy while within defined limits Laborious but no limits Hybrid Cloud Hybrid Cloud is the cloud infrastructure build with the composition of two or more clouds like public and private cloud. Hybrid cloud gives best of the both the world as it combines multiple cloud deployment models together. Cloud and Big Data – Common Characteristics There are many characteristics of the Cloud Architecture and Cloud Computing which are also essentially important for Big Data as well. They highly overlap and at many places it just makes sense to use the power of both the architecture and build a highly scalable framework. Here is the list of all the characteristics of cloud computing important in Big Data Scalability Elasticity Ad-hoc Resource Pooling Low Cost to Setup Infastructure Pay on Use or Pay as you Go Highly Available Leading Big Data Cloud Providers There are many players in Big Data Cloud but we will list a few of the known players in this list. Amazon Amazon is arguably the most popular Infrastructure as a Service (IaaS) provider. The history of how Amazon started in this business is very interesting. They started out with a massive infrastructure to support their own business. Gradually they figured out that their own resources are underutilized most of the time. They decided to get the maximum out of the resources they have and hence  they launched their Amazon Elastic Compute Cloud (Amazon EC2) service in 2006. Their products have evolved a lot recently and now it is one of their primary business besides their retail selling. Amazon also offers Big Data services understand Amazon Web Services. Here is the list of the included services: Amazon Elastic MapReduce – It processes very high volumes of data Amazon DynammoDB – It is fully managed NoSQL (Not Only SQL) database service Amazon Simple Storage Services (S3) – A web-scale service designed to store and accommodate any amount of data Amazon High Performance Computing – It provides low-tenancy tuned high performance computing cluster Amazon RedShift – It is petabyte scale data warehousing service Google Though Google is known for Search Engine, we all know that it is much more than that. Google Compute Engine – It offers secure, flexible computing from energy efficient data centers Google Big Query – It allows SQL-like queries to run against large datasets Google Prediction API – It is a cloud based machine learning tool Other Players Besides Amazon and Google we also have other players in the Big Data market as well. Microsoft is also attempting Big Data with the Cloud with Microsoft Azure. Additionally Rackspace and NASA together have initiated OpenStack. The goal of Openstack is to provide a massively scaled, multitenant cloud that can run on any hardware. Thing to Watch The cloud based solutions provides a great integration with the Big Data’s story as well it is very economical to implement as well. However, there are few things one should be very careful when deploying Big Data on cloud solutions. Here is a list of a few things to watch: Data Integrity Initial Cost Recurring Cost Performance Data Access Security Location Compliance Every company have different approaches to Big Data and have different rules and regulations. Based on various factors, one can implement their own custom Big Data solution on a cloud. Tomorrow In tomorrow’s blog post we will discuss about various Operational Databases supporting Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Cloud Computing Forces Better Design Practices

    - by Herve Roggero
    Is cloud computing simply different than on premise development, or is cloud computing actually forcing you to create better applications than you normally would? In other words, is cloud computing merely imposing different design principles, or forcing better design principles?  A little while back I got into a discussion with a developer in which I was arguing that cloud computing, and specifically Windows Azure in his case, was forcing developers to adopt better design principles. His opinion was that cloud computing was not yielding better systems; just different systems. In this blog, I will argue that cloud computing does force developers to use better design practices, and hence better applications. So the first thing to define, of course, is the word “better”, in the context of application development. Looking at a few definitions online, better means “superior quality”. As it relates to this discussion then, I stipulate that cloud computing can yield higher quality applications in terms of scalability, everything else being equal. Before going further I need to also outline the difference between performance and scalability. Performance and scalability are two related concepts, but they don’t mean the same thing. Scalability is the measure of system performance given various loads. So when developers design for performance, they usually give higher priority to a given load and tend to optimize for the given load. When developers design for scalability, the actual performance at a given load is not as important; the ability to ensure reasonable performance regardless of the load becomes the objective. This can lead to very different design choices. For example, if your objective is to obtains the fastest response time possible for a service you are building, you may choose the implement a TCP connection that never closes until the client chooses to close the connection (in other words, a tightly coupled service from a connectivity standpoint), and on which a connection session is established for faster processing on the next request (like SQL Server or other database systems for example). If you objective is to scale, you may implement a service that answers to requests without keeping session state, so that server resources are released as quickly as possible, like a REST service for example. This alternate design would likely have a slower response time than the TCP service for any given load, but would continue to function at very large loads because of its inherently loosely coupled design. An example of a REST service is the NO-SQL implementation in the Microsoft cloud called Azure Tables. Now, back to cloud computing… Cloud computing is designed to help you scale your applications, specifically when you use Platform as a Service (PaaS) offerings. However it’s not automatic. You can design a tightly-coupled TCP service as discussed above, and as you can imagine, it probably won’t scale even if you place the service in the cloud because it isn’t using a connection pattern that will allow it to scale [note: I am not implying that all TCP systems do not scale; I am just illustrating the scalability concepts with an imaginary TCP service that isn’t designed to scale for the purpose of this discussion]. The other service, using REST, will have a better chance to scale because, by design, it minimizes resource consumption for individual requests and doesn’t tie a client connection to a specific endpoint (which means you can easily deploy this service to hundreds of machines without much trouble, as long as your pockets are deep enough). The TCP and REST services discussed above are both valid designs; the TCP service is faster and the REST service scales better. So is it fair to say that one service is fundamentally better than the other? No; not unless you need to scale. And if you don’t need to scale, then you don’t need the cloud in the first place. However, it is interesting to note that if you do need to scale, then a loosely coupled system becomes a better design because it can almost always scale better than a tightly-coupled system. And because most applications grow overtime, with an increasing user base, new functional requirements, increased data and so forth, most applications eventually do need to scale. So in my humble opinion, I conclude that a loosely coupled system is not just different than a tightly coupled system; it is a better design, because it will stand the test of time. And in my book, if a system stands the test of time better than another, it is of superior quality. Because cloud computing demands loosely coupled systems so that its underlying service architecture can be leveraged, developers ultimately have no choice but to design loosely coupled systems for the cloud. And because loosely coupled systems are better… … the cloud forces better design practices. My 2 cents.

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  • Cloud Computing = Elasticity * Availability

    - by Herve Roggero
    What is cloud computing? Is hosting the same thing as cloud computing? Are you running a cloud if you already use virtual machines? What is the difference between Infrastructure as a Service (IaaS) and a cloud provider? And the list goes on… these questions keep coming up and all try to fundamentally explain what “cloud” means relative to other concepts. At the risk of over simplification, answering these questions becomes simpler once you understand the primary foundations of cloud computing: Elasticity and Availability.   Elasticity The basic value proposition of cloud computing is to pay as you go, and to pay for what you use. This implies that an application can expand and contract on demand, across all its tiers (presentation layer, services, database, security…).  This also implies that application components can grow independently from each other. So if you need more storage for your database, you should be able to grow that tier without affecting, reconfiguring or changing the other tiers. Basically, cloud applications behave like a sponge; when you add water to a sponge, it grows in size; in the application world, the more customers you add, the more it grows. Pure IaaS providers will provide certain benefits, specifically in terms of operating costs, but an IaaS provider will not help you in making your applications elastic; neither will Virtual Machines. The smallest elasticity unit of an IaaS provider and a Virtual Machine environment is a server (physical or virtual). While adding servers in a datacenter helps in achieving scale, it is hardly enough. The application has yet to use this hardware.  If the process of adding computing resources is not transparent to the application, the application is not elastic.   As you can see from the above description, designing for the cloud is not about more servers; it is about designing an application for elasticity regardless of the underlying server farm.   Availability The fact of the matter is that making applications highly available is hard. It requires highly specialized tools and trained staff. On top of it, it's expensive. Many companies are required to run multiple data centers due to high availability requirements. In some organizations, some data centers are simply on standby, waiting to be used in a case of a failover. Other organizations are able to achieve a certain level of success with active/active data centers, in which all available data centers serve incoming user requests. While achieving high availability for services is relatively simple, establishing a highly available database farm is far more complex. In fact it is so complex that many companies establish yearly tests to validate failover procedures.   To a certain degree certain IaaS provides can assist with complex disaster recovery planning and setting up data centers that can achieve successful failover. However the burden is still on the corporation to manage and maintain such an environment, including regular hardware and software upgrades. Cloud computing on the other hand removes most of the disaster recovery requirements by hiding many of the underlying complexities.   Cloud Providers A cloud provider is an infrastructure provider offering additional tools to achieve application elasticity and availability that are not usually available on-premise. For example Microsoft Azure provides a simple configuration screen that makes it possible to run 1 or 100 web sites by clicking a button or two on a screen (simplifying provisioning), and soon SQL Azure will offer Data Federation to allow database sharding (which allows you to scale the database tier seamlessly and automatically). Other cloud providers offer certain features that are not available on-premise as well, such as the Amazon SC3 (Simple Storage Service) which gives you virtually unlimited storage capabilities for simple data stores, which is somewhat equivalent to the Microsoft Azure Table offering (offering a server-independent data storage model). Unlike IaaS providers, cloud providers give you the necessary tools to adopt elasticity as part of your application architecture.    Some cloud providers offer built-in high availability that get you out of the business of configuring clustered solutions, or running multiple data centers. Some cloud providers will give you more control (which puts some of that burden back on the customers' shoulder) and others will tend to make high availability totally transparent. For example, SQL Azure provides high availability automatically which would be very difficult to achieve (and very costly) on premise.   Keep in mind that each cloud provider has its strengths and weaknesses; some are better at achieving transparent scalability and server independence than others.    Not for Everyone Note however that it is up to you to leverage the elasticity capabilities of a cloud provider, as discussed previously; if you build a website that does not need to scale, for which elasticity is not important, then you can use a traditional host provider unless you also need high availability. Leveraging the technologies of cloud providers can be difficult and can become a journey for companies that build their solutions in a scale up fashion. Cloud computing promises to address cost containment and scalability of applications with built-in high availability. If your application does not need to scale or you do not need high availability, then cloud computing may not be for you. In fact, you may pay a premium to run your applications with cloud providers due to the underlying technologies built specifically for scalability and availability requirements. And as such, the cloud is not for everyone.   Consistent Customer Experience, Predictable Cost With all its complexities, buzz and foggy definition, cloud computing boils down to a simple objective: consistent customer experience at a predictable cost.  The objective of a cloud solution is to provide the same user experience to your last customer than the first, while keeping your operating costs directly proportional to the number of customers you have. Making your applications elastic and highly available across all its tiers, with as much automation as possible, achieves the first objective of a consistent customer experience. And the ability to expand and contract the infrastructure footprint of your application dynamically achieves the cost containment objectives.     Herve Roggero is a SQL Azure MVP and co-author of Pro SQL Azure (APress).  He is the co-founder of Blue Syntax Consulting (www.bluesyntax.net), a company focusing on cloud computing technologies helping customers understand and adopt cloud computing technologies. For more information contact herve at hroggero @ bluesyntax.net .

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