并行数据库技术分析及未来展望.pptxVIP

并行数据库技术分析及未来展望;Agenda;Parallel database system seeks to improve performance through parallelization of various operations such as loading data ,building indexes, and evaluating queries by using multiple CPUs and Disks in Parallel.[1] Motivation Parallel machines are becoming quite common and affordable Prices of microprocessors, memory and disks have dropped sharply Databases are growing increasingly large large volumes of transaction data are collected and stored for later analysis. multimedia objects like images are increasingly stored in databases ;; Speed-Up Adding more resources results in proportionally less running time for a fixed amount of data. 10 seconds to scan a DB of 10,000 records using 1 CPU 1 second to scan a DB of 10,000 records using 10 CPUs;Shared-Memory and Shared-Disk Architectures Shared-Memory: all processors share a common main memory and secondary memory Load balancing is relatively easy to achieve, but suffer from memory and bus contention Shared-Disk: all processors, each of which has its own local main memory, share the disks ;Shared-Nothing Architecture Each processor has its own local main memory and disks Load balancing becomes difficult ;Shared-Something Architecture A mixture of shared-memory and shared-nothing architectures Each node is a shared-memory architecture connected to an interconnection network ala shared-nothing architecture ;元数据管理 查询分析和执行器 分布式事务 分布式锁 并发控制 副本数据同步 ;Agenda;11/24/2014;11/23/2014;11/23/2014;技术要点 – 存储和索引(2);技术要点 – 存储和索引(3);技术要点 – 查询引擎;技术要点 – 分布式事务(1);技术要点 – 分布式事务(2);技术要点 – 任务调度与资源管理;技术要点 – 高可用;技术要点 – 集群扩展;技术要点 – 备份和灾难恢复;技术要点 – SSD;Agenda;未来展望 – 并行数据库在大数据基础设置中的定位(1);;未来展望 – 并行数据库与Hadoop;未来展望 – 并行数据库与Hadoop集成 (SQL-on-Hadoop;未来展望 – 并行数据库与Hadoop集成 (Connector);未来展望 – 并行数据库与Hadoop集成 (Hybrid);未来展望 – IT基础设施的变化; Figure 5. Tenant-related factors and how they affect “ isolated versus shared data architecture decisions;未来展望 – 并行数据库技术的发展;正如“MapReduce and Parallel Databases: Friends or Foes?”的