BIGDATA: Mid-Scale: DCM: Collaborative Research: Eliminating the Data Ingestion Bottleneck in Big Data Applications

大数据实践表明，数据接收的速度、快速回答查询的能力(例如，通过索引)和数据的新鲜度之间存在权衡。例如，这种感知到的权衡处于OLTP(在线事务处理)和OLAP(在线分析处理)之间历史上的分歧的核心。在OLTP数据库中，数据被快速摄取，可供查询的数据是最新的，但分析查询的运行速度慢得令人望而却步。在OLAP数据仓库中，数据被缓冲以供离线索引，以便快速运行分析查询，但当数据被索引时，它已经过时了。这种权衡在所有类型的存储系统的设计中都有所体现。例如，一些文件系统针对读取进行了优化，而另一些文件系统针对写入进行了优化，但工作负载通常涉及读取和写入的混合。在这个项目中，PI表明这不是一个基本的权衡，而是由数据结构的选择强加的权衡。PI使用写优化结构(传统索引方法的替代方法)来构建存储系统，在该存储系统中，这种权衡显著减轻或完全减轻。这种索引方案的性能承诺来自PI以前的工作，即写优化的数据结构可以加快插入和查询的速度。该项目解决了在大数据文件系统和数据库中部署写优化索引的剩余障碍。大数据会对任何存储系统施加一组新的约束，PI将展示写优化索引如何在规模上带来数量级的性能提升。特别是，该项目将表明，这些技术不仅适用于今天，而且将随着硬件趋势(包括固态硬盘(SSD)的广泛采用)而扩展。

Big-data practice suggests that there is a tradeoff between the speed of data ingestion, the ability to answer queries quickly (e.g., via indexing), and the freshness of data. This perceived tradeoff lies, for example, at the heart of the historic division between OLTP (online transaction processing) and OLAP (online analytical processing). In an OLTP database, data gets ingested quickly and the data available for querying is fresh, but analytical queries run prohibitively slowly. In an OLAP data warehouse, data is buffered for off-line indexing so that analytical queries run quickly, but by the time the data gets indexed, it is stale. This tradeoff has manifestations in the design of all types of storage systems. For example, some file-systems are optimized for reads and others for writes, but workloads generally involve a mixture of reads and writes. In this project the PIs show that this is not a fundamental tradeoff, but rather a tradeoff imposed by the choice of data structure. The PIs use write-optimized structures, an alternative to traditional indexing methodologies, to build storage systems in which this tradeoff is significantly mitigated or alleviated altogether. The performance promise of such indexing schemes follows from the PIs previous work establishing that write-optimized data structures can speed up both inserts and queries. This project addresses the remaining obstacles in the deployment of write-optimized indexes within big-data file-systems and databases. Big data imposes a new set of constraints on any storage system, and the PIs will show how write-optimized indexing can yield order-of-magnitude performance improvements at scale. In particular, this project will show that such techniques are not only applicable today but that they will scale with hardware trends, including the widespread adoption of solid-state disks (SSDs).