Code Generation for Efficient Query Processing in Managed Runtimes

In this paper we examine opportunities arising from the conver-gence of two trends in data management: in-memory database sys-tems (IMDBs), which have received renewed attention following the availability of affordable, very large main memory systems; and language-integrated query, which transparently integrates database queries with programming languages (thus addressing the famous ‘impedance mismatch ’ problem). Language-integrated query not only gives application developers a more convenient way to query external data sources like IMDBs, but also to use the same querying language to query an application’s in-memory collections. The lat-ter offers further transparency to developers as the query language and all data is represented in the data model of the host program-ming language. However, compared to IMDBs, this additional free-dom comes at a higher cost for query evaluation. Our vision is to improve in-memory query processing of application objects by introducing database technologies to managed runtimes. We focus on querying and we leverage query compilation to im-prove query processing on application objects. We explore dif-ferent query compilation strategies and study how they improve the performance of query processing over application data. We take C] as the host programming language as it supports language-integrated query through the LINQ framework. Our techniques de-liver significant performance improvements over the default LINQ implementation. Our work makes important first steps towards a future where data processing applications will commonly run on machines that can store their entire datasets in-memory, and will be written in a single programming language employing language-integrated query and IMDB-inspired runtimes to provide transparent and highly efficient querying. 1

Generating code for holistic query evaluation

Abstract — We present the application of customized code generation to database query evaluation. The idea is to use a collection of highly efficient code templates and dynamically instantiate them to create query- and hardware-specific source code. The source code is compiled and dynamically linked to the database server for processing. Code generation diminishes the bloat of higher-level programming abstractions necessary for implementing generic, interpreted, SQL query engines. At the same time, the generated code is customized for the hardware it will run on. We term this approach holistic query evaluation. We present the design and development of a prototype system called HIQUE, the Holistic Integrated Query Engine, which incorporates our proposals. We undertake a detailed experimental study of the system’s performance. The results show that HIQUE satisfies its design objectives, while its efficiency surpasses that of both wellestablished and currently-emerging query processing techniques. I

Recycling in pipelined query evaluation

htmlabstractDatabase systems typically execute queries in isolation. Sharing recurring intermediate and final results between successive query invocations is ignored or only exploited by caching final query results. The DBA is kept in the loop to make explicit sharing decisions by identifying and/or defining materialized views. Thus decisions are made only after a long time and sharing opportunities may be missed. Recycling intermediate results has been proposed as a method to make database query engines profit from opportunities to reuse fine-grained partial query results, that is fully autonomous and is able to continuously adapt to changes in the workload. The technique was recently revisited in the context of MonetDB, a system that by default materializes all intermediate results. Materializing intermediate results can consume significant system resources, therefore most other database systems avoid this where possible, following a pipelined query architecture instead. The novelty of this paper is to show how recycling can successfully be applied in pipelined query executors, by tracking the benefit of materializing possible intermediate results and then choosing the ones making best use of a limited intermediate result cache. We present ways to maximize the potential of recycling by leveraging subsumption and proactive query rewriting. We have implemented our approach in the Vectorwise database engine and have experimentally evaluated its potential using both synthetic and real-world datasets. Our results show that intermediate result recycling significantly improves performance