Book Image

Mastering Apache Cassandra 3.x - Third Edition

By : Aaron Ploetz, Tejaswi Malepati, Nishant Neeraj
Book Image

Mastering Apache Cassandra 3.x - Third Edition

By: Aaron Ploetz, Tejaswi Malepati, Nishant Neeraj

Overview of this book

With ever-increasing rates of data creation, the demand for storing data fast and reliably becomes a need. Apache Cassandra is the perfect choice for building fault-tolerant and scalable databases. Mastering Apache Cassandra 3.x teaches you how to build and architect your clusters, configure and work with your nodes, and program in a high-throughput environment, helping you understand the power of Cassandra as per the new features. Once you’ve covered a brief recap of the basics, you’ll move on to deploying and monitoring a production setup and optimizing and integrating it with other software. You’ll work with the advanced features of CQL and the new storage engine in order to understand how they function on the server-side. You’ll explore the integration and interaction of Cassandra components, followed by discovering features such as token allocation algorithm, CQL3, vnodes, lightweight transactions, and data modelling in detail. Last but not least you will get to grips with Apache Spark. By the end of this book, you’ll be able to analyse big data, and build and manage high-performance databases for your application.

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Table of Contents (12 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Quick Start
Quick Start
Introduction to Cassandra
Installation
Configuration
Starting Cassandra
Cassandra Cluster Manager
A quick introduction to the data model
Shutting down Cassandra
Summary
2
Cassandra Architecture
Cassandra Architecture
Why was Cassandra created?
Cassandra's ring architecture
Cassandra's write path
Cassandra's read path
On-disk storage
Additional components of Cassandra
Summary
3
Effective CQL
Effective CQL
An overview of Cassandra data modeling
cqlsh
Getting started with CQL
Summary
4
Configuring a Cluster
Configuring a Cluster
Evaluating instance requirements
Operating system optimizations
Configuring the JVM
Configuring Cassandra
Managing a deployment pipeline
Summary
5
Performance Tuning
Performance Tuning
Cassandra-Stress
Write performance
Read performance
Other performance considerations
Summary
6
Managing a Cluster
Managing a Cluster
Revisiting nodetool
Scaling up
Scaling down
Backing up and restoring data
Maintenance
Summary
7
Monitoring
Monitoring
JMX interface
The nodetool utility
Metric stack
Log stack
Troubleshooting
All-in-one Docker
Summary
8
Application Development
Application Development
Getting started
Building a Java application
Summary
9
Integration with Apache Spark
Integration with Apache Spark
Spark
PySpark
SparkR
RStudio
Jupyter
PYSpark through Juypter
Summary
10
References
References
Chapter 1 – Quick Start
Chapter 2 – Cassandra Architecture
Chapter 3 – Effective CQL
Chapter 4 – Configuring a Cluster
Chapter 5 – Performance Tuning
Chapter 6 – Managing a Cluster
Chapter 7 – Monitoring
Chapter 8 – Application Development
Chapter 9 – Integration with Apache Spark
11
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Monitoring

This chapter will cover monitoring, logging, and administration. With large-scale datasets on distributed architectures comes the problem of providing a reliable service, which makes it more complex to keep an eye on all servers. As DataStax OpsCenter is no longer an option for use with Apache Cassandra,this chapter concentrates only on the open source toolset. Specifically, this chapter will cover the following topics:

  • The Java Management Extension (JMX) interface: JConsole, JMXTerm
  • Nodetool
  • Metrics: JMXTrans, Telegraf, InfluxDB, Grafana, Alerting
  • Logging: Filebeat, Kibana
  • Troubleshooting

By the end of this chapter, you will understand different monitoring and logging tools, and how they provide more insight for problem solving on your cluster. You will be able to make decisions using reliable out-of-the-box applications from the open source community, including...

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