Focus: Programming
Programming has become as much a part of our everyday lives as breathing. Some of us do it; some of us use it; all of us are affected by it. We encounter it when we use an ATM machine, drive a car, check out at the grocery store, and sit in front of the TV or computer. It's interesting and fun to do. Nothing beats the feeling of writing a good application that gets used by many others—it's a source of pride. Nothing feels worse than writing a good application that gets scrapped and never used. Actually, this brings up one of the good points of free software: you write it, put it out on the Web, and people who need it use it; nothing gets put in the bit bucket. Only in the commercial environment can a boss say, “We've decided to abort that project you've been working on for the last six months”--and your work disappears forever.
This month, we let programmers tell us how they work, from programming on clusters to building GUIs with X and Motif—it's all here. Multi-threaded programs are hot and so is Python. Learn how to use the two together; then use LCLint to debug all that new code you've been writing. We also have articles on Palm Pilot development tools and writing a simple plotting program (see “Strictly On-line”). We also talk again to Darryl Strauss to find out what is happening in the world of 3-D graphics.
Some people collect spoons, others collect languages. Eric Raymond collects languages, and his latest find is Python. We've liked Python for a long time and so decided not only to include a feature article on Python in this issue, but also publish an entire supplement devoted to it. That supplement comes to you with this issue. We hope you enjoy it.
Marjorie Richardson, Editor in Chief
Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.
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| Speed Up Your Web Site with Varnish | Jun 19, 2013 |
| Non-Linux FOSS: libnotify, OS X Style | Jun 18, 2013 |
| Containers—Not Virtual Machines—Are the Future Cloud | Jun 17, 2013 |
| Lock-Free Multi-Producer Multi-Consumer Queue on Ring Buffer | Jun 12, 2013 |
| Weechat, Irssi's Little Brother | Jun 11, 2013 |
| One Tail Just Isn't Enough | Jun 07, 2013 |
- Speed Up Your Web Site with Varnish
- Containers—Not Virtual Machines—Are the Future Cloud
- Linux Systems Administrator
- Non-Linux FOSS: libnotify, OS X Style
- Lock-Free Multi-Producer Multi-Consumer Queue on Ring Buffer
- Senior Perl Developer
- Technical Support Rep
- UX Designer
- Web & UI Developer (JavaScript & j Query)
- RSS Feeds
Featured Jobs
| Linux Systems Administrator | Houston and Austin, Texas | Host Gator |
| Senior Perl Developer | Austin, Texas | Host Gator |
| Technical Support Rep | Houston and Austin, Texas | Host Gator |
| UX Designer | Austin, Texas | Host Gator |
| Web & UI Developer (JavaScript & j Query) | Austin, Texas | Host Gator |
Free Webinar: Hadoop
How to Build an Optimal Hadoop Cluster to Store and Maintain Unlimited Amounts of Data Using Microservers
Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.
Some of key questions to be discussed are:
- What is the “typical” Hadoop cluster and what should be installed on the different machine types?
- Why should you consider the typical workload patterns when making your hardware decisions?
- Are all microservers created equal for Hadoop deployments?
- How do I plan for expansion if I require more compute, memory, storage or networking?




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