REX：递归、基于增量的数据中心计算
REX: Recursive, Delta-Based Data-Centric Computation
摘要：
在当今的Web和社交网络环境中，查询工作负载包括ad hoc和OLAP查询，以及分析数据关系的迭代算法（例如，链接分析、聚类、学习）。现代DBMS支持即席和OLAP查询，但大多数都不够健壮，无法扩展到大型集群。相反，像MapReduce这样的“云”平台以容错的方式跨集群执行批处理任务链，但有太多的开销来支持ad hoc查询。此外，这两类平台在执行迭代数据分析算法时会产生显著的开销。大多数这样的迭代算法重复地改进它们的答案的部分，直到达到某种收敛标准。然而，一般的云平台通常必须在每个步骤中重新处理所有数据。支持递归SQL的数据库管理系统更有效，因为它们只传播每个步骤中的更改--但它们仍然会累积每个迭代的状态，即使它不再有用。用户定义的函数在DBMS中的编写通常也比在云平台中更难。我们寻求统一两种风格的平台的优势，重点是支持迭代计算，其中的变化，在三角洲的形式，从迭代传播到迭代，状态是有效地更新在一个可扩展的方式。我们提出了一个面向三角洲的编程模型，描述了我们如何在REX运行时系统中执行和优化这些程序，并验证了我们的平台也可以优雅地处理故障。我们通过实验验证了我们的技术，并显示了从2.5到近100倍的竞争方法的加速比。
英文摘要：
In today's Web and social network environments, query workloads include ad hoc and OLAP queries, as well as iterative algorithms that analyze data relationships (e.g., link analysis, clustering, learning). Modern DBMSs support ad hoc and OLAP queries, but most are not robust enough to scale to large clusters. Conversely, "cloud" platforms like MapReduce execute chains of batch tasks across clusters in a fault tolerant way, but have too much overhead to support ad hoc queries. Moreover, both classes of platform incur significant overhead in executing iterative data analysis algorithms. Most such iterative algorithms repeatedly refine portions of their answers, until some convergence criterion is reached. However, general cloud platforms typically must reprocess all data in each step. DBMSs that support recursive SQL are more efficient in that they propagate only the changes in each step -- but they still accumulate each iteration's state, even if it is no longer useful. User-defined functions are also typically harder to write for DBMSs than for cloud platforms. We seek to unify the strengths of both styles of platforms, with a focus on supporting iterative computations in which changes, in the form of deltas, are propagated from iteration to iteration, and state is efficiently updated in an extensible way. We present a programming model oriented around deltas, describe how we execute and optimize such programs in our REX runtime system, and validate that our platform also handles failures gracefully. We experimentally validate our techniques, and show speedups over the competing methods ranging from 2.5 to nearly 100 times.
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