As XML usage grows for both data-centric and document-centric applications, introducing native support for XML data in relational databases brings significant benefits. It provides a more mature platform for the XML data model and serves as the basis for interoperability between relational and XML data. Whereas query processing on XML data shredded into one or more relational tables is well understood, it provides limited support for the XML data model. XML data can be persisted as a byte sequence (BLOB) in columns of tables to support the XML model more faithfully. This introduces new challenges for query processing such as the ability to index the XML blob for good query performance. This paper reports novel techniques for indexing XML data in the upcoming version of Microsoft® SQL Server™, and how it ties into the relational framework for query processing.

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Espresso is a document-oriented distributed data serving platform that has been built to address LinkedIn's requirements for a scalable, performant, source-of-truth primary store. It provides a hierarchical document model, transactional support for modifications to related documents, realtime secondary indexing, on-the-fly schema evolution and provides a timeline consistent change capture stream. This paper describes the motivation and design principles involved in building Espresso, the data model and capabilities exposed to clients, details of the replication and secondary indexing implementation and presents a set of experimental results that characterize the performance of the system along various dimensions.When we set out to build Espresso, we chose to apply best practices in industry, already published works in research and our own internal experience with different consistency models. Along the way, we built a novel generic distributed cluster management framework, a partition-aware changecapture pipeline and a high-performance inverted index implementation.