文件名称：Learning Apache Cassandra - Second Edition
文件大小：10.68MB
文件格式：AZW3
更新时间：2020-05-16 23:33:56
Apache Cassandra
Learning Apache Cassandra - Second Edition by Sandeep Yarabarla English | 25 Apr. 2017 | ASIN: B01N52R0B5 | 360 Pages | AZW3 | 10.68 MB Key Features Install Cassandra and set up multi-node clusters Design rich schemas that capture the relationships between different data types Master the advanced features available in Cassandra 3.x through a step-by-step tutorial and build a scalable, high performance database layer Book Description Cassandra is a distributed database that stands out thanks to its robust feature set and intuitive interface, while providing high availability and scalability of a distributed data store. This book will introduce you to the rich feature set offered by Cassandra, and empower you to create and manage a highly scalable, performant and fault-tolerant database layer. The book starts by explaining the new features implemented in Cassandra 3.x and get you set up with Cassandra. Then you'll walk through data modeling in Cassandra and the rich feature set available to design a flexible schema. Next you'll learn to create tables with composite partition keys, collections and user-defined types and get to know different methods to avoid denormalization of data. You will then proceed to create user-defined functions and aggregates in Cassandra. Then, you will set up a multi node cluster and see how the dynamics of Cassandra change with it. Finally, you will implement some application-level optimizations using a Java client. By the end of this book, you'll be fully equipped to build powerful, scalable Cassandra database layers for your applications. What you will learn Install Cassandra Create keyspaces and tables with multiple clustering columns to organize related data Use secondary indexes and materialized views to avoid denormalization of data Effortlessly handle concurrent updates with collection columns Ensure data integrity with lightweight transactions and logged batches Understand eventual consistency and use the right consistency level for your situation Understand data distribution with Cassandra Develop simple application using Java driver and implement application-level optimizations About the Author Sandeep Yarabarla is a professional software engineer working for Verizon Labs, based out of Palo Alto, CA. After graduating from Carnegie Mellon University, he has worked on several big data technologies for a spectrum of companies. He has developed applications primarily in Java and Go. His experience includes handling large amounts of unstructured and structured data in Hadoop, and developing data processing applications using Spark and MapReduce. Right now, he is working with some cutting-edge technologies such as Cassandra, Kafka, Mesos, and Docker to build fault-tolerant and highly scalable applications. Table of Contents Getting Up and Running with Cassandra The First Table Organizing Related Data Beyond Key-Value Lookup Establishing Relationships Denormalizing Data for Maximum Performance Expanding Your Data Model Collections, Tuples, and User-Defined Types Aggregating Time-Series Data How Cassandra Distributes Data Cassandra Multi-Node Cluster Application Development Using the Java Driver Peeking under the Hood Authentication and Authorization