Over the last two releases SQL Server has integrated two specialized engines into the core system: the Apollo column store engine for analytical workloads and the Hekaton in-memory engine for high-performance OLTP workloads. There is an increasing demand for real-time analytics, that is, for running analytical queries and reporting on the same system as transaction processing so as to have access to the freshest data. SQL Server 2016 will include enhancements to column store indexes and in-memory tables that significantly improve performance on such hybrid workloads. This paper describes four such enhancements: column store indexes on in-memory tables, making secondary column store indexes on disk-based tables updatable, allowing B-tree indexes on primary column store indexes, and further speeding up the column store scan oper ator.

Recent OLTP support exploits new techniques, running on modern hardware, to achieve unprecedented performance compared with prior approaches. In SQL Server, the Hekaton main-memory database engine embodies this new OLTP support. Hekaton uses the Bw-tree to achieve its great indexing performance. The Bw-Tree is a latch-free B-tree index that also exploits log-structured storage when used "beyond" Hekaton as a separate key value store. It is designed from the ground up to address two hardware trends: (1) Multi-core and main memory hierarchy: the Bw-tree is completely latch-free, using an atomic compare-and-swap instruction to install state changes on a "page address" mapping table; it performs updates as "deltas" to avoid update-in-place. These improve performance by eliminating thread blocking while improving cache hit ratios. (2) Flash storage: the Bw-tree organizes secondary storage in a log-structured manner, using large sequential writes to avoid entirely the adverse performance impact of random writes. We demonstrate the architectural versatility and performance of the Bw-tree in two scenarios: (a) running live within Hekaton and (2) running as a standalone key value store compared to both BerkeleyDB and a state-of-the-art in-memory range index (latch-free skiplists). Using workloads from real-world applications (Microsoft XBox Live Primetime and enterprise deduplication), we show the Bw-tree is 19x faster than BerkeleyDB and 3x faster than skiplists.

This paper describes a type system that is capable of expressing and enforcing immutability constraints. The specific constraint expressed is that the abstract state of the object to which an immutable reference refers cannot be modified using that reference. The abstract state is (part of) the transitively reachable state: that is, the state of the object and all state reachable from it by following references. The type system permits explicitly excluding fields or objects from the abstract state of an object. For a statically type-safe language, the type system guarantees reference immutability. If the language is extended with immutability downcasts, then run-time checks enforce the reference immutability constraints.