Recurring Query Processing on Big Data

Lei, Chuan

Etd

Recurring Query Processing on Big Data

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The advances in hardware, software, and networks have enabled applications from business enterprises, scientific and engineering disciplines, to social networks, to generate data at unprecedented volume, variety, velocity, and varsity not possible before. Innovation in these domains is thus now hindered by their ability to analyze and discover knowledge from the collected data in a timely and scalable fashion. To facilitate such large-scale big data analytics, the MapReduce computing paradigm and its open-source implementation Hadoop is one of the most popular and widely used technologies. Hadoop's success as a competitor to traditional parallel database systems lies in its simplicity, ease-of-use, flexibility, automatic fault tolerance, superior scalability, and cost effectiveness due to its use of inexpensive commodity hardware that can scale petabytes of data over thousands of machines. Recurring queries, repeatedly being executed for long periods of time on rapidly evolving high-volume data, have become a bedrock component in most of these analytic applications. Efficient execution and optimization techniques must be designed to assure the responsiveness and scalability of these recurring queries. In this dissertation, we thoroughly investigate topics in the area of recurring query processing on big data. In this dissertation, we first propose a novel scalable infrastructure called Redoop that treats recurring query over big evolving data as first class citizens during query processing. This is in contrast to state-of-the-art MapReduce/Hadoop system experiencing significant challenges when faced with recurring queries including redundant computations, significant latencies, and huge application development efforts. Redoop offers innovative window-aware optimization techniques for recurring query execution including adaptive window-aware data partitioning, window-aware task scheduling, and inter-window caching mechanisms. Redoop retains the fault-tolerance of MapReduce via automatic cache recovery and task re-execution support as well. Second, we address the crucial need to accommodate hundreds or even thousands of recurring analytics queries that periodically execute over frequently updated data sets, e.g., latest stock transactions, new log files, or recent news feeds. For many applications, such recurring queries come with user-specified service-level agreements (SLAs), commonly expressed as the maximum allowed latency for producing results before their merits decay. On top of Redoop, we built a scalable multi-query sharing engine tailored for recurring workloads in the MapReduce infrastructure, called Helix. Helix deploys new sliced window-alignment techniques to create sharing opportunities among recurring queries without introducing additional I/O overheads or unnecessary data scans. Furthermore, Helix introduces a cost/benefit model for creating a sharing plan among the recurring queries, and a scheduling strategy for executing them to maximize the SLA satisfaction. Third, recurring analytics queries tend to be expensive, especially when query processing consumes data sets in the hundreds of terabytes or more. Time sensitive recurring queries, such as fraud detection, often come with tight response time constraints as query deadlines. Data sampling is a popular technique for computing approximate results with an acceptable error bound while reducing high-demand resource consumption and thus improving query turnaround times. In this dissertation, we propose the first fast approximate query engine for recurring workloads in the MapReduce infrastructure, called Faro. Faro introduces two key innovations: (1) a deadline-aware sampling strategy that builds samples from the original data with reduced sample sizes compared to uniform sampling, and (2) adaptive resource allocation strategies that maximally improve the approximate results while assuring to still meet the response time requirements specified in recurring queries. In our comprehensive experimental study of each part of this dissertation, we demonstrate the superiority of the proposed strategies over state-of-the-art techniques in scalability, effectiveness, as well as robustness.

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