It generates code even faster than HyPer’s bytecode interpreter and the resulting execution speed is on par with HyPer’s LLVM-generated code. With the Flying Start backend we show a solution for the low-latency spectrum, i.e., short-running queries. Ultimately, the query engine is stuck in a performance gap between interpretation and compilation, with no great choice for low query latency. Unfortunately, in cases like this, both options have significant shortcomings: Compilation time with LLVM is not amortized and the bytecode interpretation is so slow that it diminishes the gains from its fast compilation. It can either prioritize fast execution, but spend more time in compilation with the LLVM backend, or use the bytecode interpreter for fast compilation at the cost of slower execution. In the example of TPC-H query 2, HyPer’s low-latency choices are the top two in Fig. For low latency, HyPer can either use a bytecode interpreter or the optimizing compiler LLVM with most optimizations turned off (turning optimizations on takes too much compilation time for short-running queries). For query execution, it has a choice between using intensively optimized code for high-speed execution and two low-latency compilation backends. This allows Umbra to switch dynamically between low-latency compilation with Flying Start and highest-speed query execution by optimizing compilation with the LLVM compiler framework.Īdaptive execution was introduced first to the HyPer query engine. The Flying Start backend is integrated into Umbra through the adaptive execution technique. Further, it reduces the time spent for execution as the speed of the created machine-code is close to that of thoroughly optimized code. Flying Start reduces query latency in two ways: It minimizes time spent for compilation as it generates machine-code very quickly. We introduce \(\) the novel Flying Start compilation backend which transforms Umbra IR directly into machine-code. Compile time must be addressed in the whole compilation pipeline, thus we address every component (c.f., Fig.
This paper presents multiple components for compiling query engines to achieve low query latency that is, to minimize the total time spent for query compilation and execution. They observed that compilation “has an up-front cost, which can quickly add up” and thus severely deteriorates the interactive user experience. The Northstar project also encountered the issue.
Vogelsgesang et al. reported that for the interactive data exploration tool Tableau some queries, even after careful tuning, still take multiple seconds just in compilation step of the underlying database system Hyper. Any overhead from compilation delays the query response and, especially with a large number of queries per interaction, becomes noticeable to the user and causes them to idly wait. For example, interactive data exploration tools send many queries to the underlying database system often even multiple queries for a single user interaction.
However, for some use-cases the extra time spent on compilation-the latency overhead of compilation-can be a problem. At the same time, on large data sets, its throughput is on par with the state-of-the-art compiling system HyPer.Ĭompilation works well for large analytical workloads. On small data sets, it is even faster than interpreter engines like DuckDB and PostgreSQL. Indeed, Umbra achieves unprecedentedly low query latencies. We implemented these optimizations in our database system Umbra to show that it is possible to unite fast compilation and fast execution. Third, we introduce a new compiler backend that is optimized for minimal compile time, and simultaneously, yields superior execution performance to competing approaches, e.g., Volcano-style or bytecode interpretation. Second, we present a program representation whose data structures are tuned to support fast code generation and compilation. First, we introduce a code generation framework that establishes abstractions to manage complexity, yet generates code in a single fast pass. We incorporate the lessons learned from a decade of generating code in HyPer into a design that manages complexity and yields high speed.
#Compiling java code cdg how to#
In this paper, we examine all stages of compiling query execution engines and show how to reduce compilation overhead. Also, a major barrier for adoption, especially for interactive ad hoc queries, is long compilation time. It is sometimes claimed that the intricacies of code generation make compilation-based engines too complex. Although compiling queries to efficient machine code has become a common approach for query execution, a number of newly created database system projects still refrain from using compilation.