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With the growing DRAM capacity and core count in modern servers, database systems are becoming increasingly multi-engine to feature a heterogeneous set of engines. In particular, a memory-optimized engine and a conventional storage-centric engine may coexist for various application needs. However, handling cross-engine transactions that access more than one engine remains challenging in terms of correctness, performance and programmability. This paper describes Skeena, a holistic approach to cross-engine transactions. We propose a lightweight transaction tracking structure and an atomic commit protocol to ensure correctness and support various isolation levels in multi-engine systems. Evaluation on a 40-core server shows that Skeena (1) does not penalize single-engine transactions and (2) enables the use of cross-engine transactions to improve throughput by up to 30x and/or reduce storage cost by judiciously placing tables in different engines.
Existing data storage systems offer a wide range of functionalities to accommodate an equally diverse range of applications. However, new classes of applications have emerged, e.g., blockchain and collaborative analytics, featuring data versioning, f
Many scientific data-intensive applications perform iterative computations on array data. There exist multiple engines specialized for array processing. These engines efficiently support various types of operations, but none includes native support f
Debugging transactions and understanding their execution are of immense importance for developing OLAP applications, to trace causes of errors in production systems, and to audit the operations of a database. However, debugging transactions is hard f
Efficient execution of SPARQL queries over large RDF datasets is a topic of considerable interest due to increased use of RDF to encode data. Most of this work has followed either relational or graph-based approaches. In this paper, we propose an alt
Current main memory database system architectures are still challenged by high contention workloads and this challenge will continue to grow as the number of cores in processors continues to increase. These systems schedule transactions randomly acro