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Building a columnar database on RAMCloud database design for the low-latency enabled data center /

This book examines the field of parallel database management systems and illustrates the great variety of solutions based on a shared-storage or a shared-nothing architecture.

Main Author: Tinnefeld, Christian,
Other Authors: SpringerLink (Online service)
Format: eBook
Language: English
Published: Cham : Springer, [2015]
Physical Description: 1 online resource (xix, 130 pages) : illustrations.
Series: In-memory data management research.
Subjects:
Database design.
Database management.
Cloud computing.
Table of Contents:
  • Machine generated contents note:
  • 1.
  • Introduction
  • 1.1.
  • Motivation
  • 1.2.
  • Research Questions and Scope
  • 1.3.
  • Outline
  • 2.
  • Related Work and Background
  • 2.1.
  • Current Computing Hardware Trends
  • 2.1.1.
  • Larger and Cheaper Main Memory Capacities
  • 2.1.2.
  • Multi-Core Processors and the Memory Wall
  • 2.1.3.
  • Switch Fabric Network and Remote Direct Memory Access
  • 2.2.
  • In-Memory Database Management Systems
  • 2.2.1.
  • Column-and Row-Oriented Data Layout
  • 2.2.2.
  • Transactional Versus Analytical Versus Mixed Workload Processing
  • 2.2.3.
  • State-of-the-Art In-Memory Database Management Systems
  • 2.3.
  • Parallel Database Management Systems
  • 2.3.1.
  • Shared-Memory Versus Shared-Disk Versus Shared-Nothing
  • 2.3.2.
  • State-of-the-Art Parallel Database Management Systems
  • 2.3.3.
  • Database-Aware Storage Systems
  • 2.3.4.
  • Operator Placement for Distributed Query Processing
  • 2.4.
  • Cloud Storage Systems
  • 2.4.1.
  • State-of-the-Art Cloud Storage Systems
  • 2.4.2.
  • Combining Database Management and Cloud Storage Systems
  • 2.5.
  • Classification
  • pt. I
  • Database System Architecture for a Shared Main Memory-Based Storage
  • 3.
  • System Architecture
  • 3.1.
  • System Architecture
  • Requirements, Assumptions, and Overview
  • 3.2.
  • AnalyticsDB
  • 3.3.
  • Stanford's RAMCloud
  • 4.
  • Data Storage
  • 4.1.
  • Mapping from Columnar Data to RAMCloud Objects
  • 4.2.
  • Main Memory Access Costs and Object Sizing
  • 5.
  • Data Processing
  • 5.1.
  • Database Operators in AnalyticsDB
  • 5.2.
  • Operator Push-Down into RAMCloud
  • 5.3.
  • From SQL Statement to Main Memory Access
  • pt. II
  • Database Operator Execution on a Shared Main Memory-Based Storage
  • 6.
  • Operator Execution on One Relation
  • 6.1.
  • Evaluating Operator Execution Strategies
  • 6.2.
  • Optimizing Operator Execution
  • 6.3.
  • Implications of Data Partitioning
  • 7.
  • Operator Execution on Two Relations
  • 7.1.
  • Grace Join
  • 7.2.
  • Distributed Block Nested Loop Join
  • 7.3.
  • Cyclo Join
  • 7.4.
  • Join Algorithm Comparison
  • 7.5.
  • Parallel Join Executions
  • pt. III
  • Evaluation
  • 8.
  • Performance Evaluation
  • 8.1.
  • Analytical Workload: Star Schema Benchmark
  • 8.2.
  • Mixed Workload: Point-of-Sales Customer Data
  • 9.
  • High-Availability Evaluation
  • 10.
  • Elasticity Evaluation
  • pt. IV
  • Conclusions
  • 11.
  • Conclusions.

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