On the Impact of Memory Allocation on High-Performance Query Processing

Somewhat surprisingly, the behavior of analytical query engines is crucially affected by the dynamic memory allocator used. Memory allocators highly influence performance, scalability, memory efficiency and memory fairness to other processes. In this work, we provide the first comprehensive experimental analysis on the impact of memory allocation for high-performance query engines. We test five state-of-the-art dynamic memory allocators and discuss their strengths and weaknesses within our DBMS. The right allocator can increase the performance of TPC-DS (SF 100) by 2.7x on a 4-socket Intel Xeon server

Learned Cardinalities: Estimating Correlated Joins with Deep Learning

We describe a new deep learning approach to cardinality estimation. MSCN is a multi-set convolutional network, tailored to representing relational query plans, that employs set semantics to capture query features and true cardinalities. MSCN builds on sampling-based estimation, addressing its weaknesses when no sampled tuples qualify a predicate, and in capturing join-crossing correlations. Our evaluation of MSCN using a real-world dataset shows that deep learning significantly enhances the quality of cardinality estimation, which is the core problem in query optimization.Comment: CIDR 2019. https://github.com/andreaskipf/learnedcardinalitie

Estimating Cardinalities with Deep Sketches

We introduce Deep Sketches, which are compact models of databases that allow us to estimate the result sizes of SQL queries. Deep Sketches are powered by a new deep learning approach to cardinality estimation that can capture correlations between columns, even across tables. Our demonstration allows users to define such sketches on the TPC-H and IMDb datasets, monitor the training process, and run ad-hoc queries against trained sketches. We also estimate query cardinalities with HyPer and PostgreSQL to visualize the gains over traditional cardinality estimators.Comment: To appear in SIGMOD'1

Adaptive Execution of Compiled Queries

Compiling queries to machine code is arguably the most efficient way for executing queries. One often overlooked problem with compilation, however, is the time it takes to generate machine code. Even with fast compilation frameworks like LLVM, Generating machine code for complex queries routinely takes hundreds of milliseconds. Such compilation times can be a major disadvantage for workloads that execute many complex, but quick queries. To solve this problem, we propose an adaptive execution framework, which dynamically and transparently switches from interpretation to compilation. We also propose a fast bytecode interpreter for LLVM, which can execute queries without costly translation to machine code and thereby dramatically reduces query latency. Adaptive execution is dynamic, fine-grained, and can execute different code paths of the same query using different execution modes. Our extensive evaluation shows that this approach achieves optimal performance in a wide variety from settings---low latency for small data sets and maximum throughput for large data sizes

Nonlinear fracture mechanics-based analysis of thin wall cylinders

This paper presents a simple analysis technique to predict the crack initiation, growth, and rupture of large-radius, R, to thickness, t, ratio (thin wall) cylinders. The method is formulated to deal both with stable tearing as well as fatigue mechanisms in applications to both surface and through-wall axial cracks, including interacting surface cracks. The method can also account for time-dependent effects. Validation of the model is provided by comparisons of predictions to more than forty full scale experiments of thin wall cylinders pressurized to failure

Chest physiotherapy using passive expiratory techniques does not reduce bronchiolitis severity: a randomised controlled trial

Chest physiotherapy (CP) using passive expiratory manoeuvres is widely used in Western Europe for the treatment of bronchiolitis, despite lacking evidence for its efficacy. We undertook an open randomised trial to evaluate the effectiveness of CP in infants hospitalised for bronchiolitis by comparing the time to clinical stability, the daily improvement of a severity score and the occurrence of complications between patients with and without CP. Children <1year admitted for bronchiolitis in a tertiary hospital during two consecutive respiratory syncytial virus seasons were randomised to group 1 with CP (prolonged slow expiratory technique, slow accelerated expiratory flow, rarely induced cough) or group 2 without CP. All children received standard care (rhinopharyngeal suctioning, minimal handling, oxygen for saturation ≥92%, fractionated meals). Ninety-nine eligible children (mean age, 3.9months), 50 in group 1 and 49 in group 2, with similar baseline variables and clinical severity at admission. Time to clinical stability, assessed as primary outcome, was similar for both groups (2.9 ± 2.1 vs. 3.2 ± 2.8days, P = 0.45). The rate of improvement of a clinical and respiratory score, defined as secondary outcome, only showed a slightly faster improvement of the respiratory score in the intervention group when including stethoacoustic properties (P = 0.044). Complications were rare but occurred more frequently, although not significantly (P = 0.21), in the control arm. In conclusion, this study shows the absence of effectiveness of CP using passive expiratory techniques in infants hospitalised for bronchiolitis. It seems justified to recommend against the routine use of CP in these patient

Learned cardinalities: Estimating correlated joins with deep learning

We describe a new deep learning approach to cardinality estimation. MSCN is a multi-set convolutional network, tailored to representing relational query plans, that employs set semantics to capture query features and true cardinalities. MSCN builds on sampling-based estimation, addressing its weaknesses when no sampled tuples qualify a predicate, and in capturing join-crossing correlations. Our evaluation of MSCN using a real-world dataset shows that deep learning signiicantly enhances the quality of cardinality estimation, which is the core problem in query optimization

How Good Are Query Optimizers, Really?

Finding a good join order is crucial for query performance. In this paper, we introduce the Join Order Benchmark (JOB) and experimentally revisi