Big data processing systems (e.g., Hadoop, Spark, Storm) contain a vast number of configuration parameters controlling parallelism, I/O behavior, memory settings, and compression. Improper parameter settings can cause significant performance degradation and stability issues. However, regular users and even expert administrators grapple with understanding and tuning them to achieve good performance. We investigate existing approaches on parameter tuning for both batch and stream data processing systems and classify them into six categories: rule-based, cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. We summarize the pros and cons of each approach and raise some open research problems for automatic parameter tuning.
Unlike traditional database management systems which are organized around a single data model, a multi-model database (MMDB) utilizes a single, integrated back-end to support multiple data models, such as document, graph, relational, and key-value. As more and more platforms are proposed to deal with multi-model data, it becomes crucial to establish a benchmark for evaluating the performance and usability of MMDBs. Previous benchmarks, however, are inadequate for such scenario because they lack a comprehensive consideration for multiple models of data. In this paper, we present a benchmark, called UniBench, with the goal of facilitating a holistic and rigorous evaluation of MMDBs. UniBench consists of a mixed data model, a synthetic multi-model data generator, and a set of core workloads. Specifically, the data model simulates an emerging application: Social Commerce, a Web-based application combining E-commerce and social media. The data generator provides diverse data format including JSON, XML, key-value, tabular, and graph. The workloads are comprised of a set of multi-model queries and transactions, aiming to cover essential aspects of multi-model data management. We implemented all workloads on ArangoDB and OrientDB to illustrate the feasibility of our proposed benchmarking system and show the learned lessons through the evaluation of these two multi-model databases. The source code and data of this benchmark can be downloaded at http://udbms.cs.helsinki.fi/bench/.
Big Data processing systems (e.g., Spark) have a number of resource configuration parameters, such as memory size, CPU allocation, and the number of running nodes. Regular users and even expert administrators struggle to understand the mutual relation between different parameter configurations and the overall performance of the system. In this paper, we address this challenge by proposing a performance prediction framework, called d-Simplexed, to build performance models with varied configurable parameters on Spark. We take inspiration from the field of Computational Geometry to construct a d-dimensional mesh using Delaunay Triangulation over a selected set of features. From this mesh, we predict execution time for various feature configurations. To minimize the time and resources in building a bootstrap model with a large number of configuration values, we propose an adaptive sampling technique to allow us to collect as few training points as required. Our evaluation on a cluster of computers using WordCount, PageRank, Kmeans, and Join workloads in HiBench benchmarking suites shows that we can achieve less than 5% error rate for estimation accuracy by sampling less than 1% of data.
Database and big data analytics systems such as Hadoop and Spark have a large number of configuration parameters that control memory distribution, I/O optimization, parallelism, and compression. Improper parameter settings can cause significant performance degradation and stability issues. However, regular users and even expert administrators struggle to understand and tune them to achieve good performance. In this tutorial, we review existing approaches on automatic parameter tuning for databases, Hadoop, and Spark, which we classify into six categories: rule-based, cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. We describe the foundations of different automatic parameter tuning algorithms and present pros and cons of each approach. We also highlight real-world applications and systems, and identify research challenges for handling cloud services, resource heterogeneity, and real-time analytics.
In recent data management ecosystem, one of the greatest challenges is the data variety. Data varies in multiple formats such as relational and (semi-)structured data. Traditional database handles a single type of data format and thus its ability to deal with di erent types of data formats is limited. To overcome such limitation, we propose a multi-model processing framework for relational and semi-structured data (i.e. XML), and design a worst-case optimal join algorithm. e salient feature of our algorithm is that it can guarantee that the intermediate results are no larger than the worstcase join results. Preliminary results show that our multi-model algorithm signi cantly outperforms the baseline join methods in terms of running time and intermediate result size. 1 MOTIVATION As more businesses realized that data, in all forms and sizes, is critical to making the best possible decisions, we see the continued growth of demands to manage and process massive volume of di erent types of data [4]. e data presents in various types and formats: structured, semi-structured and unstructured. A traditional DB typically handles only one kind of data format. Relational DB, for example, can only deal with relational tables. It is promising to develop a multi-model database to manage and process multiple data models against a single model, while meeting the increasing requirements for scalability and performance [4, 6]. In this paper, we investigate the case of the join between relational database and XML database. We show a simple example in Figure 1. Our multi-model framework computes XML twigs into a relational-like structures without losing size bound so that the worst-case size bound of join between Relational and XML can be calculated. Based on the worst-case size bound, we design a worst-case optimal join algorithm for relational and XML data.
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