autoSql and autoXml: Code Generators from the Genome Project
autoXml extends the type field of ATTLIST to include INT or FLOAT for numerical rather than string values. Similarly you can use #INT or #FLOAT in place of #PCDATA to put a numerical type in the text field. If you include these extensions, please use the .dtdx rather than .dtd suffix on your DTD file.
Currently autoXml only copes with DTD comments if they start on a line by themselves. autoXml expects all ELEMENTS and ATTLIST declarations to fit on a single line. It doesn't handle reference data types beyond saving the reference ID as a string.
Refer to Listing 3 for a complete example of the source code autoXml generates. In addition to the .h file shown in Listing 3, autoXml generates a corresponding .c file as well. Each XML file has to have a root object. In this case the root object is POLYGON (our DTD as is won't let us have more than one polygon per file). You can read an XML file that respects this DTD using the polyPolygonLoad() function, and save it back out using the polyPolygonSave.
autoSql and autoXml work well on a range of data, as you've seen, anywhere from an address book to gene tracks. We hope you'll find these tools useful on your own projects.
Practical Task Scheduling Deployment
July 20, 2016 12:00 pm CDT
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
Join Linux Journal's Mike Diehl and Pat Cameron of Help Systems.
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With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide