Taster: Self-Tuning, Elastic and Online Approximate Query Processing

Current Approximate Query Processing (AQP) engines are far from silver-bullet solutions, as they adopt several static design decisions that target specific workloads and deployment scenarios. Offline AQP engines target deployments with large storage budget, and offer substantial performance improvement for predictable workloads, but fail when new query types appear, i.e., due to shifting user interests. To the other extreme, online AQP engines assume that query workloads are unpredictable, and therefore build all samples at query time, without reusing samples (or parts of them) across queries. Clearly, both extremes miss out on different opportunities for optimizing performance and cost. In this paper, we present Taster, a self-tuning, elastic, online AQP engine that synergistically combines the benefits of online and offline AQP. Taster performs online approximation by injecting synopses (samples and sketches) into the query plan, while at the same time it strategically materializes and reuses synopses across queries, and continuously adapts them to changes in the workload and to the available storage resources. Our experimental evaluation shows that Taster adapts to shifting workload and to varying storage budgets, and always matches or significantly outperforms the state-of-the-art performing AQP approaches (online or offline).

From Massive Parallelization to Quantum Computing: Seven Novel Approaches to Query Optimization

Immanuel Trummer

The goal of query optimization is to map a declarative query (describing data to generate) to a query plan (describing how to generate the data) with optimal execution cost. Query optimization is required to support declarative query interfaces. It is a core problem in the area of database systems and has received tremendous attention in the research community, starting with an initial publication in 1979. In this thesis, we revisit the query optimization problem. This visit is motivated by several developments that change the context of query optimization. That change is not reflected in prior literature. First, advances in query execution platforms and processing techniques have changed the context of query optimization. Novel provisioning models and processing techniques such as Cloud computing, crowdsourcing, or approximate processing allow to trade between different execution cost metrics (e.g., execution time versus monetary execution fees in case of Cloud computing). This makes it necessary to compare alternative execution plans according to multiple cost metrics in query optimization. While this is a common scenario nowadays, the literature on query optimization with multiple cost metrics (a generalization of the classical problem variant with one execution cost metric) is surprisingly sparse. While prior methods take hours to optimize even moderately sized queries when considering multiple cost metrics, we propose a multitude of approaches to make query optimization in such scenarios practical. A second development that we address in this thesis is the availability of novel software and hardware platforms that can be exploited for optimization. We will show that integer programming solvers, massively parallel clusters (which nowadays are commonly used for query execution), and adiabatic quantum annealers enable us to solve query optimization problem instances that are far beyond the capabilities of prior approaches. In summary, we propose seven novel approaches to query optimization that significantly increase the size of the problem instances that can be addressed (measured by the query size and by the number of considered execution cost metrics). Those novel approaches can be classified into three broad categories: moving query optimization before run time to relax constraints on optimization time, trading optimization time for relaxed optimality guarantees (leading to approximation schemes, incremental algorithms, and randomized algorithms for query optimization with multiple cost metrics), and reducing optimization time by leveraging novel software and hardware platforms (integer programming solvers, massively parallel clusters, and adiabatic quantum annealers). Those approaches are novel since they address novel problem variants of query optimization, introduced in this thesis, since they are novel for their respective problem variant (e.g., we propose the first randomized algorithm for query optimization with multiple cost metrics), or because they have never been used for optimization problems in the database domain (e.g., this is the first time that quantum computing is used to solve a database-specific optimization problem).

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

Toward timely, predictable and cost-effective data analytics

Renata Borovica-Gajic

EPFL2016

From Massive Parallelization to Quantum Computing: Seven Novel Approaches to Query Optimization

Immanuel Trummer

EPFL2016