关键词：集群技术；数据处理；集群计算系统
摘 要：This dissertation proposes an architecture for cluster computing systems that can tackle emerging data processing workloads while coping with larger and larger scales. Whereas early cluster computing systems, like MapReduce, handled batch processing, our architecture also enables streaming and interactive queries, while keeping the scalability and fault tolerance of previous systems. And whereas most deployed systems only support simple one-pass computations (e.g. aggregation or SQL queries), ours also extends to the multi-pass algorithms required for more complex analytics (e.g. iterative algorithms for machine learning). Finally, unlike the specialized systems proposed for some of these workloads, our architecture allows these computations to be combined, enabling rich new applications that intermix, for example, streaming and batch processing, or SQL and complex analytics.