Data management systems enable various influential applications from high-performance online services (e.g., social networks like Twitter and Facebook or financial markets) to big data analytics (e.g., scientific exploration, sensor networks, business intelligence). As a result, data management systems have been one of the main drivers for innovations in the database and computer architecture communities for several decades. Recent hardware trends require software to take advantage of the abundant parallelism existing in modern and future hardware. The traditional design of the data management systems, however, faces inherent scalability problems due to its tightly coupled components. In addition, it cannot exploit the full capability of the aggressive micro-architectural features of modern processors. As a result, today's most commonly used server types remain largely underutilized leading to a huge waste of hardware resources and energy.

In this book, we shed light on the challenges present while running DBMS on modern multicore hardware. We divide the material into two dimensions of scalability: implicit/vertical and explicit/horizontal.

The first part of the book focuses on the vertical dimension: it describes the instruction- and data-level parallelism opportunities in a core coming from the hardware and software side. In addition, it examines the sources of under-utilization in a modern processor and presents insights and hardware/software techniques to better exploit the microarchitectural resources of a processor by improving cache locality at the right level of the memory hierarchy.

The second part focuses on the horizontal dimension, i.e., scalability bottlenecks of database applications at the level of multicore and multisocket multicore architectures. It first presents a systematic way of eliminating such bottlenecks in online transaction processing workloads, which is based on minimizing unbounded communication, and shows several techniques that minimize bottlenecks in major components of database management systems. Then, it demonstrates the data and work sharing opportunities for analytical workloads, and reviews advanced scheduling mechanisms that are aware of nonuniform memory accesses and alleviate bandwidth saturation.

Anastasia Ailamaki, Ecole Polytechnique Federale de Lausanne (EPFL) - Switzerland
Anastasia Ailamaki is a Professor of Computer and Communication Sciences at the Ecole Polytechnique Federale de Lausanne (EPFL) in Switzerland. Her research interests are in data-intensive systems and applications, and in particular (a) in strengthening the interaction between the database software and emerging hardware and I/O devices, and (b) in automating data management to support computationally-demanding, data-intensive scientific applications. She has received an ERC Consolidator Award (2013), a Finmeccanica endowed chair from the Computer Science Department at Carnegie Mellon (2007), a European Young Investigator Award from the European Science Foundation (2007), an Alfred P. Sloan Research Fellowship (2005), eight bestpaper awards in database, storage, and computer architecture conferences (2001-2012), and an NSF CAREER award (2002). She holds a Ph.D. in Computer Science from the University of Wisconsin-Madison in 2000. She is an ACM fellow and the vice chair of the ACM SIGMOD community, as well as a senior member of the IEEE. She has served as a CRA-W mentor and is a member of the Expert Network of the World Economic Forum.

Erietta Liarou, Ecole Polytechnique Federale de Lausanne (EPFL) - Switzerland
Erietta Liarou is currently a co-founder in a data analytics startup. She received her Ph.D. in Computer Science from University of Amsterdam in 2013. In her thesis she worked on the first column-store stream processing system, MonetDB/DataCell, that leverages analytical systems technology for scalable stream processing. Her research interests include database architectures, transaction processing on modern hardware, data-stream processing and distributed query processing. In the past she has been with the Data-Intensive Applications and Systems Laboratory (DIAS) in EPFL, the Dutch National Research Center for Mathematics and Computer Science (CWI) in Amsterdam, The Netherlands, the Intelligence Systems Laboratory in Technical University of Crete, Greece, and with the System S group in IBM T.J.Watson Research Center, Hawthorne, NY, USA. In 2011, she received the Best Paper Award in Challenges and Visions at the Very Large Database Conference.

Table of Contents
Introduction
Exploiting Resources of a Processor Core
Minimizing Memory Stalls
Scaling-up OLTP
Scaling-up OLAP Workloads
Outlook
Summary
Bibliography
Authors' Biographies