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Fair Use Source: B09R2BVFB1 (CsndrDfGd 2022)

Foreword

Preface Why Apache Cassandra?

Is This Book for You?

What’s in This Book?

New for the Third Edition]]

Conventions Used in This Book

Using Code Examples

O'Reilly Interactive Katacoda Scenarios

O'Reilly Online Learning

How to Contact Us

Acknowledgments

1. Beyond Relational Databases What’s Wrong with Relational Databases?

A Quick Review of Relational Databases Transactions, ACID-ity, and Two-Phase Commit

Schema

Sharding and Shared-No[[thing Architecture

Web Scale

The Rise of NoSQL

Summary

2. Introducing Cassandra The Cassandra Elevator Pitch Cassandra in 50 Words or Less

Distributed and De[[centralized

Elastic Scalability

High [[Availability and Fault Tolerance

Tuneable Consistency

Brewer’s CAP Theorem

Row-Oriented

High Performance

Where Did Cassandra Come From?

Is Cassandra a Good Fit for My Project? Large Deployments

Lots of Writes, Statistics, and Analysis

Geographical Distribution

Hybrid [[Cloud and Multi[[cloud Deployment

Getting Involved

Summary

3. Installing Cassandra Installing the Apache Distribution Extracting]] the Download

What’s in There?

Building from Source Additional Build Targets

Running Cassandra Setting the Environment

Starting the Server

Stopping Cassandra

Other Cassandra Distributions

Running the CQL Shell

Basic cqlsh Commands cqlsh Help

Describing the Environment in cqlsh

Creating a Keyspace and Table in cqlsh

Writing and Reading Data in cqlsh

Running Cassandra in Docker

Summary

4. The Cassandra Query Language The Relational Data Model

Cassandra’s Data Model Clusters

Keyspaces

Tables

Columns

CQL Types Numeric Data Types

Textual Data Types

Time and Identity Data Types

Other Simple Data Types

Collections

Tuples

User-Defined Types

Summary

5. Data Modeling Conceptual Data Modeling

RDBMS Design Design Differences Between RDBMS and Cassandra

Defining Application Queries

Logical Data Modeling Hotel Logical Data Model

Reservation Logical Data Model

Physical Data Modeling Hotel Physical Data Model

Reservation Physical Data Model

Evaluating and Refining calculating Partition Size

calculating Size on Disk

Breaking Up Large Partitions

Defining Database Schema Cassandra Data Modeling Tools

Summary

6. The Cassandra Architecture Data [[Centers and Racks

Gossip and Failure [[Detection

Snitches

Rings and Tokens

Virtual Nodes

Partitioners

Replication Strategies

Consistency Levels

Queries and Coordinator Nodes

Hinted Handoff

Anti-Entropy, Repair, and Merkle Trees

Lightweight Transactions and Paxos

Memtables, SSTables, and Commit Logs

Bloom Filters

Caching

Compaction

Deletion and Tombstones

Managers and Services Cassandra Daemon

Storage Engine]]

Storage Service

Storage Proxy

Messaging Service

Stream Manager

CQL Native Transport Server

System Keyspaces

Summary

7. Designing Applications with Cassandra Hotel Application Design Cassandra and Microservice Architecture

Microservice Architecture for a Hotel Application

Identifying Bounded Contexts

Identifying Services

Designing Microservice Persistence

Extending Designs Se[[condary]] Indexes

Materialized]] Views

Reservation Service: A Sample Microservice Design Choices for a Java Microservice

Deployment and Integration Considerations Services, Keyspaces, and Clusters

Data [[Centers and Load Balancing

Interactions Between Microservices

Summary

8. Application Development with Drivers DataStax Java Driver Development Environment Configuration

Connecting to a Cluster

Statements

Simple Statements

Prepared Statements

Query Builder

Object Mapper

Asyn[[chronous]] Execution

Driver Configuration

Metadata

Debugging and Monitoring]]

Other Cassandra Drivers

Summary

9. Writing and Reading Data Writing Write Consistency Levels

The Cassandra Write Path

Writing Files to Disk

Lightweight Transactions

Batches

Reading Read Consistency Levels

The Cassandra Read Path

Read Repair

Range Queries, Ordering and Filtering

Paging

Deleting

Summary

10. Configuring and Deploying Cassandra Cassandra Cluster Manager Creating a Cluster

Adding Nodes to a Cluster

Dynamic Ring Participation

Node Configuration Seed Nodes

Snitches

Partitioners

Tokens and Virtual Nodes

Network Interfaces

Data [[Storage

Startup and JVM Settings

Planning a Cluster Deployment Cluster Topology and Replication Strategies

Sizing Your Cluster

Selecting Instances

Storage

Network

Cloud Deployment Amazon Web Services]]

Google [[Cloud Platform]]

Microsoft Azure

Summary

11. Monitoring]] Monitoring]] Cassandra with JMX

Cassandra’s MBeans Database MBeans

Cluster-Related MBeans

Internal MBeans

Monitoring]] with nodetool Getting Cluster Information

Getting Statistics

Virtual Tables System Virtual Schema

System Views

Metrics

Logging Examining Log Files

Full Query Logging

Summary

12. Maintenance Health Check

Common Maintenance Tasks Flush

Cleanup

Repair

Rebuilding Indexes

Moving Tokens

Adding Nodes Adding Nodes to an Existing Data [[Center

Adding a Data [[Center to a Cluster

Handling Node Failure Repairing Failed Nodes

ReplacingNodes

Removing Nodes

Upgrading Cassandra

Backup and Re[[covery]] Taking a Snapshot

Clearing a Snapshot

Enabling Incremental Backup

Restoring from Snapshot

SSTable Utilities

Maintenance Tools Netflix Priam

DataStax OpsCenter

Cassandra Sidecars

Cassandra Kubernetes Operators

Summary

13. Performance Tuning Managing Performance Setting Performance Goals

Benchmarking and Stress Testing

Monitoring]] Performance

Analyzing Performance [[Issues

Tracing

Tuning Methodology

Caching Key Cache

Row Cache

Chunk Cache

Counter Cache

Saved Cache Settings

Memtables

Commit Logs

SSTables

Hinted Handoff

Compaction

Concurrency and Threading

Networking and Timeouts

JVM Settings Memory

Garbage Collection

Summary

14. Security Authentication and Authorization Password Authenticator]]

Using CassandraAuthorizer

Role-Based Access Control

Encryption SSL, TLS, and Certificates

Node-to-Node Encryption

Client-to-Node Encryption

JMX Security Securing JMX Access

Security MBeans

Audit Logging

Summary

15. Migrating and Integrating Knowing When to Migrate

Adapting the Data Model Translating Entities

Translating Relationships

Adapting the Application Refactoring Data Access

Maintaining Consistency

Migrating Stored Procedures

Planning the Deployment

Migrating Data Zero-Downtime Migration

Bulk Loading

Common Integrations Managing Data Flow with Apache Kafka]]

Searching with Apache Lucene, SOLR, and Elasticsearch

Analyzing Data]] with Apache Spark]]

Summary

Index

Fair Use Sources:

• Cassandra - The Definitive Guide on DuckDuckGo
• B09R2BVFB1 (CsndrDfGd 2022)