As data sets grow in size, analytics applications struggle to get instant insight into large datasets. Modern applications involve heavy batch processing jobs over large volumes of data and at the same time require efficient ad-hoc interactive analytics on temporary data. Existing solutions, however, typically focus on one of these two aspects, largely ignoring the need for synergy between the two. Consequently, interactive queries need to re-iterate costly passes through the entire dataset (e.g., data loading) that may provide meaningful return on investment only when data is queried over a long period of time. In this paper, we propose DiNoDB, an interactive-speed query engine for ad-hoc queries on temporary data. DiNoDB avoids the expensive loading and transformation phase that characterizes both traditional RDBMSs and current interactive analytics solutions. It is tailored to modern workflows found in machine learning and data exploration use cases, which often involve iterations of cycles of batch and interactive analytics on data that is typically useful for a narrow processing window. The key innovation of DiNoDB is to piggyback on the batch processing phase the creation of metadata that DiNoDB exploits to expedite the interactive queries. Our experimental analysis demonstrates that DiNoDB achieves very good performance for a wide range of ad-hoc queries compared to alternatives.

Hardware trends oblige software to overcome three major challenges against systems scalability: (1) taking advantage of the implicit/vertical parallelism within a core that is enabled through the aggressive micro-architectural features, (2) exploiting the explicit/horizontal parallelism provided by multicores, and (3) achieving predictively efficient execution despite the variability in communication latencies among cores on multisocket multicores. In this three hour tutorial, we shed light on the above three challenges and survey recent proposals to alleviate them. The first part of the tutorial 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 micro-architectural resources of a processor by improving cache locality at the right level of the memory hierarchy. The second part focuses on the 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 non-uniform memory accesses and alleviate bandwidth saturation.

Power and cooling costs are some of the highest costs in data centers today, which make improvement in energy efficiency crucial. Energy efficiency is also a major design point for chips that power whole ranges of computing devices. One important goal in this area is energy proportionality, arguing that the system's power consumption should be proportional to its performance. Currently, a major trend among server processors, which stems from the design of chips for mobile devices, is the inclusion of advanced power management techniques, such as dynamic voltage-frequency scaling, clock gating, and turbo modes.

A fundamental need in the era of data deluge is data exploration through interactive tools, i.e., being able to quickly determine data and patterns of interest. dbTouch is a new research direction towards a next generation of data management systems that inherently support data exploration by allowing touch-based interaction. Data is represented in a visual format, while users can touch those shapes and interact/query with gestures. In a dbTouch system, the whole database kernel is geared towards quick responses in touch input; the user drives query processing (not just query construction) via touch gestures, dictating how fast or slow data flows through query plans and which data parts are processed at any time. dbTouch translates the gestures into interactive database operators, reacting continuously to the touch input and analytics tasks given by the user in real-time such as sliding a finger over a column to scan it progressively; zoom in with two fingers over a column to progressively get sample data; rotate a table to change the physical design from row-store to column-store, etc. This demo presents the first dbTouch prototype over iOS for iPad.

Nowadays, high-performance transaction processing applications increasingly run on multisocket multicore servers. Such architectures exhibit non-uniform memory access latency as well as non-uniform thread communication costs. Unfortunately, traditional shared-everything database management systems are designed for uniform inter-core communication speeds. This causes unpredictable access latencies in the critical path. While lack of data locality may be a minor nuisance on systems with fewer than 4 processors, it becomes a serious scalability limitation on larger systems due to accesses to centralized data structures. In this paper, we propose ATraPos, a storage manager design that is aware of the non-uniform access latencies of multisocket systems. ATraPos achieves good data locality by carefully partitioning the data as well as internal data structures (e.g., state information) to the available processors and by assigning threads to specific partitions. Furthermore, ATraPos dynamically adapts to the workload characteristics, i.e., when the workload changes, ATraPos detects the change and automatically revises the data partitioning and thread placement to fit the current access patterns and hardware topology. We prototype ATraPos on top of an open-source storage manager Shore-MT and we present a detailed experimental analysis with both synthetic and standard (TPC-C and TATP) benchmarks. We show that ATraPos exhibits performance improvements of a factor ranging from 1.4 to 6.7x for a wide collection of transactional workloads. In addition, we show that the adaptive monitoring and partitioning scheme of ATraPos poses a negligible cost, while it allows the system to dynamically and gracefully adapt when the workload changes.

Designing scalable database management systems on modern hardware has been a challenge for almost a decade. Hardware trends oblige software to overcome three major challenges against systems scalability: (1) Exploiting the abundant thread-level parallelism provided by multicores, (2) Achieving predictively efficient execution despite the variability in communication latencies among cores on multisocket multicores, and (3) Taking advantage of the aggressive micro-architectural features. In this tutorial, we shed light on the above three challenges and survey recent proposals to alleviate them. First, we present a systematic way of eliminating scalability bottlenecks based on minimizing unbounded communication and show several techniques that minimize bottlenecks in major components of database management systems. In addition, we demonstrate methods to parallelize major database operations. Then, we analyze the problems that arise from the non-uniform nature of communication latencies on modern multisockets and ways to address them for systems that already scale well on multicores. Finally, we examine the sources of under-utilization within a modern processor and present insights and techniques to better exploit the micro-architectural resources of a processor by improving cache locality at the right level of the memory hierarchy.

Continuous query processing has emerged as a promising query processing paradigm with numerous applications. A recent development is the need to handle both streaming queries and typical one-time queries in the same application. For example, data warehousing can greatly benefit from the integration of stream semantics, i.e., online analysis of incoming data and combination with existing data. This is especially useful to provide low latency in data-intensive analysis in big data warehouses that are augmented with new data on a daily basis.

In DataCell, we design streaming functionalities in a modern relational database kernel which targets big data analytics. This includes exploitation of both its storage/execution engine and its optimizer infrastructure. We investigate the opportunities and challenges that arise with such a direction and we show that it carries significant advantages for modern applications in need for online analytics such as web logs, network monitoring and scientific data management. The major challenge then becomes the efficient support for specialized stream features, e.g., multi-query processing and incremental window-based processing as well as exploiting standard DBMS functionalities in a streaming environment such as indexing. This demo presents DataCell, an extension of the MonetDB open-source column-store for online analytics. The demo gives users the opportunity to experience the features of DataCell such as processing both stream and persistent data and performing window based processing. The demo provides a visual interface to monitor the critical system components, e.g., how query plans transform from typical DBMS query plans to online query plans, how data flows through the query plans as the streams evolve, how DataCell maintains intermediate results in columnar form to avoid repeated evaluation of the same stream portions, etc. The demo also provides the ability to interactively set the test scenarios and various DataCell knobs.

Stream applications gained significant popularity over the last years that lead to the development of specialized stream engines. These systems are designed from scratch with a different philosophy than nowadays database engines in order to cope with the stream applications requirements. However, this means that they lack the power and sophisticated techniques of a full fledged database system that exploits techniques and algorithms accumulated over many years of database research.

This paper studies the problem of evaluating continuous multi-way joins on top of Distributed Hash Tables (DHTs). We present a novel algorithm, called recursive join (RJoin), that takes into account various parameters crucial in a distributed setting i.e., network traffic, query processing load distribution, storage load distribution etc. The key idea of RJoin is incremental evaluation: as relevant tuples arrive continuously, a given multi-way join is rewritten continuously into a join with fewer join operators, and is assigned continuously to different nodes of the network. In this way, RJoin distributes the responsibility of evaluating a continuous multi-way join to many network nodes by assigning parts of the evaluation of each binary join to a different node depending on the values of the join attributes. The actual nodes to be involved are decided by RJoin dynamically after taking into account the rate of incoming tuples with values equal to the values of the joined attributes. RJoin also supports sliding window joins which is a crucial feature, especially for long join paths, since it provides a mechanism to reduce the query processing state and thus keep the cost of handling incoming tuples stable. In addition, RJoin is able to handle message delays due to heavy network traffic. We present a detailed mathematical and experimental analysis of RJoin and study the performance tradeoffs that occur.