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Wednesday, October 20, 2010
apple mac air 2010 oct 20
http://www.engadget.com/2010/10/20/apples-new-macbook-air/
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Monday, October 4, 2010
Tuesday, August 10, 2010
Tuesday, July 20, 2010
Tuesday, May 18, 2010
Friday, May 14, 2010
Wednesday, May 5, 2010
Thursday, April 29, 2010
Sunday, April 18, 2010
Saturday, April 17, 2010
Thursday, April 1, 2010
Monday, March 22, 2010
Monday, March 15, 2010
Sunday, February 14, 2010
Here is a list of my favorite Google advanced search operators, operator combinations, and related uses:
- link:URL = lists other pages that link to the URL.
- related:URL = lists other pages that are related to the URL.
- site:domain.com “search term = restricts search results to the given domain.
- allinurl:WORDS = shows only pages with all search terms in the url.
- inurl:WORD = like allinurl: but filters the URL based on the first term only.
- allintitle:WORD = shows only results with terms in title.
- intitle:WORD = similar to allintitle, but only for the next word.
- cache:URL = will show the Google cached version of the URL.
- info:URL = will show a page containing links to related searches, backlinks, and pages containing the url. This is the same as typing the url into the search box.
- filetype:SOMEFILETYPE = will restrict searches to that filetype
- -filetype:SOMEFILETYPE = will remove that file type from the search.
- site:www.somesite.net “+www.somesite.net” = shows you how many pages of your site are indexed by google
- allintext: = searches only within text of pages, but not in the links or page title
- allinlinks: = searches only within links, not text or title
- WordA OR WordB = search for either the word A or B
- “Word” OR “Phrase” = search exact word or phrase
- WordA -WordB = find word A but filter results that include word B
- WordA +WordB = results much contain both Word A and Word B
- ~WORD = looks up the word and its synonyms
- ~WORD -WORD = looks up only the synonyms to the word
So here’s a list of some things I do online when my mind is set on avoiding ‘real work.’
- Check delicious popular tags like ‘useful,’ ‘tutorials,’ ‘tips,’ ‘howto,’ ‘advice,’ ‘entrepreneurship,’ etc. for interesting, educational articles to read.
- Watch one of the thousands of educational videos streaming at TED.com,Academic Earth and Teacher Tube.
- Read an online book list and find a new book to grab next time I’m at the library. Here’s another list. And another. And another.
- Read a classic book online for free at Project Gutenberg, Planet eBook, or the E-books Directory.
- Research a new Do It Yourself project at DIY Network, Instructables,eHow, or WikiHow.
- Add to, delete from, or just generally sort my ongoing to-do list atRemember The Milk.
- Create a cool graphical mind map of some of my recent ideas at bubbl.us.
- Email a close friend or family member I haven’t spoken to in awhile.
- Backup my recent photos, documents, and other important files online using Microsoft’s free 25 gig SkyDrive.
- Use Wikipedia’s random article function to pick a random article to read.
- Touch up on my math and science skills over a the Khan Academy, MIT OpenCourseWare, or LearningScience.org.
- Send a paper greeting card directly to a friend or relative at enGreet.
- Start learning a new language online for free at BBC Languages orLivemocha.
- Watch one of the insightful 6 minute and 40 second presentations atIgnite Show.
- Use Memorize Now to memorize a cool joke, or poem, or whatever.
- Use Media Convert to convert video files I have on my computer into a format I can view on my iPhone or iPod later on.
- Listen to an educational podcast over at Odeo or via iTunes on iTunes U.
- Read one of the academic journals at the Directory of Open Access Journals.
- Share my favorite mp3s, photos, videos, etc. with friends and family usingdrop.io.
- Get a free college education online using this guide from Lifehacker (or read one of the other useful articles on Lifehacker).
- Inspire and spark my creative mind by looking at a rolling slideshow of the highest rated photos on Flickr for the last 7 days.
- Catch up on a short history lesson at HyperHistory or The Internet History Sourcebooks Project. Or find out what happened today in history.
- Take a fun, educational online quiz at Quizlet.
- Play an educational online game at Lumosity, Sporcle, Games for the Brain, or Math Run.
- Add a little gentle rain to my environment using RainyMood.com and then simply meditate and relax in my computer chair for 10 minutes.
- Sell old stuff I no longer need on eBay and make a little extra cash.
- Find a new musical artist to listen to based on music I like at Grooveshark,Pandora, last.fm, or Deezer.
- Find out what’s happening in our world from quality international news sources like BBC News and Reuters.
- Write a blog post like this one.
What kind of semi-productive things do you do online in your off-time? Please share them with me in the comments section below.
Friday, February 12, 2010
http://en.wikipedia.org/wiki/The_Mythical_Man-Month
The Mythical Man-Month: Essays on Software Engineering is a book on software engineering and project management by Fred Brooks, whose central theme is that "adding manpower to a late software project makes it later". This idea is known as Brooks's law, and is presented along with the second-system effectand advocacy of prototyping.
Brooks's observations are based on his experiences at IBM while managing the development of OS/360. He had mistakenly added more workers to a project falling behind schedule. He also made the mistake of asserting that one project — writing an Algol compiler — would require six months, regardless of the number of workers involved (It required longer). The tendency for managers to repeat such errors in project development led Brooks to quip that his book is called "The Bible of Software Engineering", because, "everybody quotes it, some people read it, and a few people go by it."[1] The book is widely regarded as a classic on the human elements of software engineering.[2]
The work was first published in 1975 (0-201-00650-2), reprinted with corrections in 1982, and republished as an anniversary edition with four extra chapters in 1995 (ISBN 0-201-83595-9) with the essay No Silver Bullet and commentary by the author.
Tuesday, February 2, 2010
Sunday, January 24, 2010
Sharding
What is sharding...?
While working for an auction website, somebody got the idea to solve the site’s scaling problems by creating a database server for a group of users and running those servers on cheap Linux boxes. In this scheme the data for User A is stored on one server and the data for User B is stored on another server. It's a federated model. Groups of 500K users are stored together in what are calledshards.
The advantages are:
- High availability. If one box goes down the others still operate.
- Faster queries. Smaller amounts of data in each user group mean faster querying.
- More write bandwidth. With no master database serializing writes you can write in parallel which increases your write throughput. Writing is major bottleneck for many websites.
- You can do more work. A parallel backend means you can do more work simultaneously. You can handle higher user loads, especially when writing data, because there are parallel paths through your system. You can load balance web servers, which access shards over different network paths, which are processed by separate CPUs, which use separate caches of RAM and separate disk IO paths to process work. Very few bottlenecks limit your work.
How is Sharding different from traditional architectures...?
Sharding is different than traditional database architecture in several important ways; following are the key factors -
Data is denormalized. Traditionally we normalize data. Data are splayed out into anomaly-less tables and then joined back together again when they need to be used. In sharding the data are denormalized. You store together data that are used together.
Data is across many physical instances. Historically database servers are scaled up. You buy bigger machines to get more power. With sharding the data are parallelized and you scale by scaling out. Using this approach you can get massively more work done because it can be done in parallel.
Data is small. The larger a set of data a server handles the harder it is to cash intelligently because you have such a wide diversity of data being accessed. You need huge gobs of RAM that may not even be enough to cache the data when you need it. By isolating data into smaller shards the data you are accessing is more likely to stay in cache.
Smaller sets of data are also easier to backup, restore, and manage.
Data are more highly available. Since the shards are independent a failure in one doesn't cause a failure in another. And if you make each shard operate at 50% capacity it's much easier to upgrade a shard in place. Keeping multiple data copies within a shard also helps with redundancy and making the data more parallelized so more work can be done on the data. You can also setup a shard to have a master-slave or dual master relationship within the shard to avoid a single point of failure within the shard. If one server goes down the other can take over.
It doesn't use replication. Replicating data from a master server to slave servers is a traditional approach to scaling. Data is written to a master server and then replicated to one or more slave servers. At that point read operations can be handled by the slaves, but all writes happen on the master.
Obviously the master becomes the write bottleneck and a single point of failure; and as the load increases the cost of replication increases. Replication costs in CPU, network bandwidth, and disk IO. The slaves fall behind and have stale data. The folks at YouTube had a big problem with replication overhead as they scaled.
Sharding cleanly and elegantly solves the problems with replication.
The most recommended approach to implement database shards is using the Hibernate Shards framework. The said framework offers critical data clustering and support for horizontal partitioning along with standard Hibernate services. Which enable the businesses to keep data in more than one relational database without any add-on complexity whilst building the applications.
Other than Hibernate; shards can also be implemented with any of the following toolkits –
- Apache Slice (supports distributed XA transactions)
- Websphere ObjectGrid
Well, thats all for the starters folks. Hope this was an useful read and has provided enough thoughts for your brains to work quite sometime now...
Tuesday, January 5, 2010
Google wins floating data center patent
- Date: May 3rd, 2009
- Author: Larry Dignan
- Category: data center
- Tags: Google Inc., Patent, Data Center, Data Centers, Storage, Hardware, Data Management
This is a guest post from Larry Dignan of TechRepublic’s sister site ZDNet. You can follow Larry on his ZDNet blog Between the Lines, or subscribe to the RSS feed.
Google has been awarded a U.S. patent for its floating data centers that are powered by waves and cooled by sea water.
The patent award was spotted first by SEO by the Sea. As noted previously, the floating data center idea is quite novel and makes a ton of sense. For Google these floating data centers could be a boon because there are no real estate costs or property taxes.
The offshore data centers would site 3 to 7 miles offshore and float in about 50 to 70 meters of water.
According to the abstract Google was awarded a patent (7,525,207) for:
A system includes a floating platform-mounted computer data center comprising a plurality of computing units, a sea-based electrical generator in electrical connection with the plurality of computing units, and one or more sea-water cooling units for providing cooling to the plurality of computing units.
Inventors were listed as Jimmy Clidaras, David Stiver and William Hamburgen.
The general idea is to move computing power closer to users. The larger question is whether Google will actually deploy these data center barges. Rich Miller at Data Center Knowledge writes:
Does Google have any intention of actually building these floating data centers? Many in the data center community are deeply skeptical about the concept, and find it difficult to believe that Google would ever pursue such a project.
So here’s the interesting precedent: In December 2003 Google applied for a patent for a portable data center in a shipping container, which was awarded in Oct. 2007. At last month’s Efficient Data Center Summit, we learned that Google deployed its first container data center in the fall of 2005, less than two years after filing its patent application.
Hmmm......................... :)
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