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Excellent legal piece explaining the options and methods of how software programs use licensed and copyrighted third party libraries through an API. Finally, some clear thinking about Google Android and the Oracle Java Law Suit.
excerpt: Another option for a developer is to do what Google did when it created Android, and create replacement code libraries that are compatible with the existing code libraries, but which are new copyrighted works. Being "compatible" in this context means that the new libraries are called in the same way that the old libraries are--that is, using the same APIs. But the actual copyrighted code that is being called is a new work. As long as the new developer didn't actually copy code from the original libraries, the new libraries are not infringing. It does not infringe on the copyright of a piece of software to create a new piece of software that works the same way; copyright protects the actual expression (lines of code) but not the functionality of a program. The functionality of a program is protected by patent, or not at all.
In the Oracle/Google case, no one is arguing that code libraries themselves are not copyrightable. Of course they are and this is why the Google/Oracle dispute has no bearing on the enforceability of the GPL. Instead, the argument is about whether the method of using a code library, the APIs, is subject to a copyright that is independent of the copyright of the code itself. If the argument that APIs are not copyrightable prevails, programs that are created by statically-linking GPL'd code libraries will still be considered derivative works of the code libraries and will still have to be released under the GPL.
Though irrelevant to the enforceability of the GPL, the Oracle/Google dispute is still interesting. Oracle is claiming that Google, by creating compatible, replacement code libraries that are "called" in the same way as Oracle's code libraries (that is, using the same APIs), infringed

A video with slide presentation featuring Google's Joshua Bloch. The topic is Java API Summary: A well-written API can be a great asset to the organization that wrote it and to all that use it. Given the importance of good API design, surprisingly little has been written on the subject. In this talk (recorded at Javapolis), Java library designer Joshua Bloch teaches how to design good APIs, with many examples of what good and bad APIs look like.

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Excellent Hadoop/Hive explanation.  Hat tip to Matt Asay for the link.  I eft a comment on Matt's blog questioning the consequences of the Oracle vs. Google Android lawsuit, and the possible enforcement of the Java API copyright claim against Hadoop/Hive.  Based on this explanation of Hadoop/Hive, i'm wondering if Oracle is making a move to claim the entire era of Big Data Cloud Computing?  To understand why, it's first necessary to read Matt the Hadoople's explanation.   kill shot excerpt: "You've built your Hadoop job, and have successfully processed the data. You've generated some structured output, and that resides on HDFS. Naturally you want to run some reports, so you load your data into a MySQL or an Oracle database. Problem is, the data is large. In fact it's so large that when you try to run a query against the table you've just created, your database begins to cry. If you listen to its sobs, you'll probably hear "I was built to process Megabytes, maybe Gigabytes of data. Not Terabytes. Not Perabytes. That's not my job. I was built in the 80's and 90's, back when floppy drives were used. Just leave me alone". "This is where Hive comes to the rescue. Hive lets you run an SQL statement against structured data stored on HDFS. When you issue an SQL query, it parses it, and translates it into a Java Map/Reduce job, which is then executed on your data. Although Hive does some optimizations, in general it just goes record by record against all your data. This means that it's relatively slow - a typical Hive query takes 5 or 10 minutes to complete, depending on how much data you have. However, that's what makes it effective. Unlike a relational database, you don't waste time on query optimization, adding indexes, etc. Instead, what keeps the processing time down is the fact that the query is run on all machines in your Hadoop cluster, and the scalability is taken care of for you." "Hive is extremely useful in data-warehousing kind of scenarios. You would