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ion without regret refers to the vision of using high-levelprogramming languages for systems development without experiencing a negativeimpact on performance. A database system designed according to this visionoffers both increased productivity and high performance, instead of sacrificingthe former for the latter as is the case with existing, monolithicimplementations that are hard to maintain and extend. In this article, werealize this vision in the domain of analytical query processing. We presentLegoBase, a query engine written in the high-level language Scala. The keytechnique to regain efficiency is to apply generative programming: LegoBaseperforms source-to-source compilation and optimizes the entire query engine byconverting the high-level Scala code to specialized, low-level C code. We showhow generative programming allows to easily implement a wide spectrum ofoptimizations, such as introducing data partitioning or switching from a row toa column data layout, which are difficult to achieve with existing low-levelquery compilers that handle only queries. We demonstrate that sufficientlypowerful abstractions are essential for dealing with the complexity of theoptimization effort, shielding developers from compiler internals anddecoupling individual optimizations from each other. We evaluate our approachwith the TPC-H benchmark and show that: (a) With all optimizations enabled,LegoBase significantly outperforms a commercial database and an existing querycompiler. (b) Programmers need to provide just a few hundred lines ofhigh-level code for implementing the optimizations, instead of complicatedlow-level code that is required by existing query compilation approaches. (c)The compilation overhead is low compared to the overall execution time, thusmaking our approach usable in practice for compiling query engines.

Building Efficient Query Engines in a High-Level Language