Abstract

This survey compares four papers that propose solutions for optimizing the storage model, data layout, and system catalogs in data management systems for hybrid workloads, which consist of both analytical and transactional queries. These solutions include a main memory hybrid storage engine that separates the storage of analytical and transactional data and uses a sophisticated query optimizer, a hands-free adaptive store that adjusts the storage layout based on access patterns, a hybrid storage engine that combines the strengths of row- and column- store systems and a column layout optimization method that considers both analytical and transactional access patterns and uses ghost values to support updates. These papers highlight the importance of designing storage systems specifically for hybrid workloads and the need for further research in this area.
Keywords: Adaptive storage, dynamic operators, adaptive hybrids, main memory, workloads, hybrid
storage, workloads
INTRODUCTION
In recent years, there has been a growing interest in the design of storage engines that can effectively support hybrid workloads, which are characterized by a mix of long-running analytical queries and short-lived transactions. These hybrid workloads are increasingly common in modern systems and pose unique challenges for storage engine design due to the need to balance the conflicting requirements of high-throughput analytical processing and low-latency transactional processing.

Keyworde: Adaptive storage, dynamic operators, adaptive hybrids, main memory, workloads, hybrid storage, workloads

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