Social networking company MySpace has announced that it is releasing an internally developed framework for data analysis as an open-source tool.
In a blog on the company's site, chief operating officer Mike Jones says the tool, called Qizmt, can be used for many operations that require processing large volumes of data such as collaborative filtering for recommendations and analytics.
"Qizmt is a powerful MapReduce-based environment that enables MySpace user recommendation engines to become smarter, faster and more reliable," Jones said. "Qizmt is being used in the 'People You May Know' feature, and will soon enable us to expand user recommendations to new areas."
Jones said Qizmt was developed using Microsoft technology, in particular C#.NET for Windows. "This extends the rapid development nature of the .NET environment to the world of large-scale data crunching and enables .NET developers to easily leverage their skill set to write MapReduce functions," Jones added.
MapReduce environments are increasingly being used by sites with large amounts of data, such as Google and Amazon. MySpace notes that its millions of users consume and produce video, music and content every minute, constantly generating large sets of new data.
Qizmt was designed to process active data generated by users and passive data in the analytics system, with the goal of creating meaningful recommendations in real time. The technology is currently being used to offer friend recommendations, but increasingly it will be leveraged to offer recommendations on such things as music, video, books and products to buy.
Jones said MySpace wanted to make Qizmt code available to developers to foster its continued development and innovation. "Supporting the open-source community is important not only because of the amazing interaction, testing and contributions that result, but also because we believe that at the end of the day, the community makes us better," he said.
In June, MySpace released another custom-built tool called MSFast, which enables developers to track page load performance and user experience data.
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