LaraDB：一个用于线性和关系代数计算的极小核
LaraDB: A Minimalist Kernel for Linear and Relational Algebra Computation
摘要：
分析任务使用关系代数（RA）的变体操纵结构化数据，使用线性代数（LA）的变体操纵定量数据。这两个计算模型具有重叠的表达性，从而激发了提供统一推理和算法设计的公共编程模型。在逻辑层面上，我们提出了Lara，一个精益代数的三个运营商，表示RA和LA以及相关的优化规则。我们展示了一系列的证据，位置劳拉%正式和非正式的表达能力的中间件代数的正确水平：更明确的比MapReduce，但比RA或LA更一般。在物理层面上，我们发现，Lara操作符提供了有效的实现，使用一个单一的原语，可在各种后端引擎：范围扫描分区排序的地图。为了评估这些想法，我们在Apache Accumulo中实现了Lara运算符作为范围迭代器，这是Google的BigTable的流行实现。首先，我们展示了劳拉如何表达一个传感器质量控制任务，我们衡量的性能影响的优化劳拉承认这项任务。其次，我们表明LaraDB实现在涉及矩阵乘法形式的连接和聚合的核心任务上优于Accumulo的原生MapReduce集成，特别是在通常不适合横向扩展方法的较小尺度上。我们发现LaraDB提供了一个概念上的精益框架，用于优化混合抽象分析任务，而不会放弃快速的记录级更新和扫描。
英文摘要：
Analytics tasks manipulate structured data with variants of relational algebra (RA) and quantitative data with variants of linear algebra (LA). The two computational models have overlapping expressiveness, motivating a common programming model that affords unified reasoning and algorithm design. At the logical level we propose Lara, a lean algebra of three operators, that expresses RA and LA as well as relevant optimization rules. We show a series of proofs that position Lara %formal and informal at just the right level of expressiveness for a middleware algebra: more explicit than MapReduce but more general than RA or LA. At the physical level we find that the Lara operators afford efficient implementations using a single primitive that is available in a variety of backend engines: range scans over partitioned sorted maps. To evaluate these ideas, we implemented the Lara operators as range iterators in Apache Accumulo, a popular implementation of Google's BigTable. First we show how Lara expresses a sensor quality control task, and we measure the performance impact of optimizations Lara admits on this task. Second we show that the LaraDB implementation outperforms Accumulo's native MapReduce integration on a core task involving join and aggregation in the form of matrix multiply, especially at smaller scales that are typically a poor fit for scale-out approaches. We find that LaraDB offers a conceptually lean framework for optimizing mixed-abstraction analytics tasks, without giving up fast record-level updates and scans.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
