Scaling Up Concurrent Main-Memory Column-Store Scans: Towards Adaptive NUMA-aware Data and Task Placement

Main-memory column-stores are called to efficiently use modern non-uniform memory access (NUMA) architectures to service concurrent clients on big data. The efficient usage of NUMA architectures depends on the data placement and scheduling strategy of the column-store. Most column-stores choose a static strategy that involves partitioning all data across the NUMA architecture, and employing a stealing-based task scheduler. In this paper, we implement different strategies for data placement and task scheduling for the case of concurrent scans. We compare these strategies with an extensive sensitivity analysis. Our most significant findings include that unnecessary partitioning can hurt throughput by up to 70%, and that stealing memory-intensive tasks can hurt throughput by up to 58%. Based on our analysis, we envision a design that adapts the data placement and task scheduling strategy to the workload.

Scaling Up Concurrent Analytical Workloads on Multi-Core Servers

Iraklis Psaroudakis

Today, an ever-increasing number of researchers, businesses, and data scientists collect and analyze massive amounts of data in database systems. The database system needs to process the resulting highly concurrent analytical workloads by exploiting modern multi-socket multi-core processor systems with non-uniform memory access (NUMA) architectures and increasing memory sizes. Conventional execution engines, however, are not designed for many cores, and neither scale nor perform efficiently on modern multi-core NUMA architectures. Firstly, their query-centric approach, where each query is optimized and evaluated independently, can result in unnecessary contention for hardware resources due to redundant work found across queries in highly concurrent workloads. Secondly, they are unaware of the non-uniform memory access costs and the underlying hardware topology, incurring unnecessarily expensive memory accesses and bandwidth saturation. In this thesis, we show how these scalability and performance impediments can be solved by exploiting sharing among concurrent queries and incorporating NUMA-aware adaptive task scheduling and data placement strategies in the execution engine. Regarding sharing, we identify and categorize state-of-the-art techniques for sharing data and work across concurrent queries at run-time into two categories: reactive sharing, which shares intermediate results across common query sub-plans, and proactive sharing, which builds a global query plan with shared operators to evaluate queries. We integrate the original research prototypes that introduce reactive and proactive sharing, perform a sensitivity analysis, and show how and when each technique benefits performance. Our most significant finding is that reactive and proactive sharing can be combined to exploit the advantages of both sharing techniques for highly concurrent analytical workloads. Regarding NUMA-awareness, we identify, implement, and compare various combinations of task scheduling and data placement strategies under a diverse set of highly concurrent analytical workloads. We develop a prototype based on a commercial main-memory column-store database system. Our most significant finding is that there is no single strategy for task scheduling and data placement that is best for all workloads. In specific, inter-socket stealing of memory-intensive tasks can hurt overall performance, and unnecessary partitioning of data across sockets involves an overhead. For this reason, we implement algorithms that adapt task scheduling and data placement to the workload at run-time. Our experiments show that both sharing and NUMA-awareness can significantly improve the performance and scalability of highly concurrent analytical workloads on modern multi-core servers. Thus, we argue that sharing and NUMA-awareness are key factors for supporting faster processing of big data analytical applications, fully exploiting the hardware resources of modern multi-core servers, and for more responsive user experience.

EPFL2016

Toward timely, predictable and cost-effective data analytics

Renata Borovica-Gajic

Modern industrial, government, and academic organizations are collecting massive amounts of data at an unprecedented scale and pace. The ability to perform timely, predictable and cost-effective analytical processing of such large data sets in order to extract deep insights is now a key ingredient for success. Traditional database systems (DBMS) are, however, not the first choice for servicing these modern applications, despite 40 years of database research. This is due to the fact that modern applications exhibit different behavior from the one assumed by DBMS: a) timely data exploration as a new trend is characterized by ad-hoc queries and a short user interaction period, leaving little time for DBMS to do good performance tuning, b) accurate statistics representing relevant summary information about distributions of ever increasing data are frequently missing, resulting in suboptimal plan decisions and consequently poor and unpredictable query execution performance, and c) cloud service providers - a major winner in the data analytics game due to the low cost of (shared) storage - have shifted the control over data storage from DBMS to the cloud providers, making it harder for DBMS to optimize data access. This thesis demonstrates that database systems can still provide timely, predictable and cost-effective analytical processing, if they use an agile and adaptive approach. In particular, DBMS need to adapt at three levels (to workload, data and hardware characteristics) in order to stabilize and optimize performance and cost when faced with requirements posed by modern data analytics applications. Workload-driven data ingestion is introduced with NoDB as a means to enable efficient data exploration and reduce the data-to-insight time (i.e., the time to load the data and tune the system) by doing these steps lazily and incrementally as a side-effect of posed queries as opposed to mandatory first steps. Data-driven runtime access path decision making introduced with Smooth Scan alleviates suboptimal query execution, postponing the decision on access paths from query optimization, where statistics are heavily exploited, to query execution, where the system can obtain more details about data distributions. Smooth Scan uses access path morphing from one physical alternative to another to fit the observed data distributions, which removes the need for a priori access path decisions and substantially improves the predictability of DBMS. Hardware-driven query execution introduced with Skipper enables the usage of cold storage devices (CSD) as a cost-effective solution for storing the ever increasing customer data. Skipper uses an out-of-order CSD-driven query execution model based on multi-way joins coupled with efficient cache and I/O scheduling policies to hide the non-uniform access latencies of CSD. This thesis advocates runtime adaptivity as a key to dealing with raising uncertainty about workload characteristics that modern data analytics applications exhibit. Overall, the techniques introduced in this thesis through the three levels of adaptivity (workload, data and hardware-driven adaptivity) increase the usability of database systems and the user satisfaction in the case of big data exploration, making low-cost data analytics reality.

EPFL2016

Just-In-Time Data Virtualization: Lightweight Data Management with ViDa

Anastasia Ailamaki, Ioannis Alagiannis, Miguel Sérgio De Oliveira Branco, Thomas Heinis, Manolis Karpathiotakis

As the size of data and its heterogeneity increase, traditional database system architecture becomes an obstacle to data analysis. Integrating and ingesting (loading) data into databases is quickly becoming a bottleneck in face of massive data as well as increasingly heterogeneous data formats. Still, state-of-the-art approaches typically rely on copying and transforming data into one (or few) repositories. Queries, on the other hand, are often ad-hoc and supported by pre-cooked operators which are not adaptive enough to optimize access to data. As data formats and queries increasingly vary, there is a need to depart from the current status quo of static query processing primitives and build dynamic, fully adaptive architectures. We build ViDa, a system which reads data in its raw format and processes queries using adaptive, just-in-time operators. Our key insight is use of virtualization, i.e., abstracting data and manipulating it regardless of its original format, and dynamic generation of operators. ViDa's query engine is generated just-in-time; its caches and its query operators adapt to the current query and the workload, while also treating raw datasets as its native storage structures. Finally, ViDa features a language expressive enough to support heterogeneous data models, and to which existing languages can be translated. Users therefore have the power to choose the language best suited for an analysis.

2015

Scaling Up Concurrent Analytical Workloads on Multi-Core Servers

Iraklis Psaroudakis

EPFL2016

Toward timely, predictable and cost-effective data analytics

Renata Borovica-Gajic

EPFL2016