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Online auction site eBay has open sourced a database technology called Kylin that the company says enables fast queries over even petabytes of data stored in Hadoop. eBay isn’t a big data user on par with companies like Google and Facebook, but it does run technologies such as Hadoop at a fairly large scale and Kylin seems a good example of the type of innovation it’s doing on top of them.

eBay details Kylin in a blog post on Wednesday, citing among other features its REST APIs, ANSI-SQL compatibility, connections to analysis tools Tableau and Excel, and sub-second latency on some queries. However, the most unique features of Kylin involve how it deals with scale. eBay says it can query billions of rows of data — on datasets more that 14 terabytes in size — at speeds much faster than using the traditional Apache Hive tool.

The way Kylin works, at a high level, is to take data from Hive; pre-process large queries using MapReduce; and then store those results as key-value “cuboids” in HBase. When a user runs a Kylin query using a particular set of variables, the values are ready to go without requiring them to be processed again. It’s not entirely dissimilar from the cubes than analytic databases have been utilizing for years, but Kylin’s cuboids are designed with HBase’s preferred data structure in mind.

Here’s how eBay says Kylin has is used within the company:

At the time of open-sourcing Kylin, we already had several eBay business units using it in production. Our largest use case is the analysis of 12+ billion source records generating 14+ TB cubes. Its 90% query latency is less than 5 seconds. Now, our use cases target analysts and business users, who can access analytics and get results through the Tableau dashboard very easily – no more Hive query, shell command, and so on.

It would be interesting to know how Kylin stacks up against next-generation versions of Hive, Spark SQL and other options for SQL analysis in Hadoop that have emerged as a result of the YARN resource manager available in the latest versions of Apache Hadoop. My guess is it’s slower but more scalable than in-memory options or those not requiring MapReduce processing, but that it might be a solid option for the large percentage of Hadoop users still running earlier versions of the software.