Pay One, Get Hundreds for Free: Reducing Cloud Costs through Shared Query Execution

Cloud-based data analysis is nowadays common practice because of the lower system management overhead as well as the pay-as-you-go pricing model. The pricing model, however, is not always suitable for query processing as heavy use results in high costs. For example, in query-as-a-service systems, where users are charged per processed byte, collections of queries accessing the same data frequently can become expensive. The problem is compounded by the limited options for the user to optimize query execution when using declarative interfaces such as SQL. In this paper, we show how, without modifying existing systems and without the involvement of the cloud provider, it is possible to significantly reduce the overhead, and hence the cost, of query-as-a-service systems. Our approach is based on query rewriting so that multiple concurrent queries are combined into a single query. Our experiments show the aggregated amount of work done by the shared execution is smaller than in a query-at-a-time approach. Since queries are charged per byte processed, the cost of executing a group of queries is often the same as executing a single one of them. As an example, we demonstrate how the shared execution of the TPC-H benchmark is up to 100x and 16x cheaper in Amazon Athena and Google BigQuery than using a query-at-a-time approach while achieving a higher throughput

Dark Silicon Accelerators for Database Indexing

The growing explosion of digital data motivates renewed emphasis on new architectures for future data-centric workloads. At the same time, the inability of power to scale with increasing transistor counts has led to a recent focus on “dark silicon” designs where transistors are used to design specialized accelerators to improve energy efficiency and performance. Combining these trends, in this paper, we examine the applicability of accelerators in future data-centric system architectures. Specifically, we focus on indexing, a fundamental and time consuming component of databases, and propose a new “indexing widget” to improve energy efficiency and performance. Through preliminary characterization of a representative scale-out database, VoltDB, in conjunction with performance, power and area models, we show that our proposed approach can achieve 1.24X improvement in energy efficiency relative to non-accelerated designs

A Case for Staged Database Systems

Traditional database system architectures face a rapidly evolving operating environment, where millions of users store and access terabytes of data. In order to cope with increasing demands for performance, high-end DBMS employ parallel processing techniques coupled with a plethora of sophisticated features. However, the widely adopted, work-centric, thread-parallel execution model entails several shortcomings that limit server performance when executing workloads with changing requirements. Moreover, the monolithic approach in DBMS software has lead to complexanddifficulttoextenddesigns. This paper introduces a staged design for high-performance, evolvable DBMS that are easy to tune and maintain. We propose to break the database system into modules and to encapsulate them into self-contained stages connected to each other through queues. The staged, data-centric design remedies the weaknesses of modern DBMS by providing solutions at both a hardware and a software engineering level.

Improving instruction cache performance in OLTP

Instruction-cache misses account for up to 40 % of execution time in online transaction processing (OLTP) database workloads. In contrast to data cache misses, instruction misses cannot be overlapped with out-of-order execution. Chip design limitations do not allow increases in the size or associativity of instruction caches that would help reduce misses. On the contrary, the effective instruction cache size is expected to further decrease with the adoption of multicore and multithreading chip designs (multiple on-chip processor cores and multiple simultaneous threads per core). Different concurrent database threads, however, execute similar instruction sequences over their lifetime, too long to be captured and exploited in hardware. The challenge, from a software designer’s point of view, is to identify and exploit common code paths across threads executing arbitrary operations, thereby eliminating extraneous instruction misses. In this article, we describe Synchronized Threads through Explicit Processor Scheduling (STEPS), a methodology and tool to increase instruction locality in database servers executing transaction processing workloads. STEPS works at two levels to increase reusability of instructions brought in the cache. At a higher level, synchronization barriers form teams of threads that execute the same system component. Within a team, STEPS schedules special fast context-switche