Cassandra: The Definitive Guide. Revised 3rd. Edition / Cassandra: Определяющее руководство. Пересмотренное 3-е издание
Год издания: 2022
Автор: Carpenter Jeff, Hewitt Eben / Карпентер Джефф, Хьюитт Эбен
Издательство: O’Reilly Media, Inc.
ISBN: 978-1-492-09714-3
Язык: Английский
Формат: PDF/EPUB
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 433
Описание: Imagine what you could do if scalability wasn't a problem. With this hands-on guide, you'll learn how the Cassandra database management system handles hundreds of terabytes of data while remaining highly available across multiple data centers. This revised third edition--updated for Cassandra 4.0 and new developments in the Cassandra ecosystem, including deployments in Kubernetes with K8ssandra--provides technical details and practical examples to help you put this database to work in a production environment.
Authors Jeff Carpenter and Eben Hewitt demonstrate the advantages of Cassandra's nonrelational design, with special attention to data modeling. Developers, DBAs, and application architects looking to solve a database scaling issue or future-proof an application will learn how to harness Cassandra's speed and flexibility.
Understand Cassandra's distributed and decentralized structure
Use the Cassandra Query Language (CQL) and cqlsh (the CQL shell)
Create a working data model and compare it with an equivalent relational model
Design and develop applications using client drivers
Explore cluster topology and learn how nodes exchange data
Maintain a high level of performance in your cluster
Deploy Cassandra onsite, in the cloud, or with Docker and Kubernetes
Integrate Cassandra with Spark, Kafka, Elasticsearch, Solr, and Lucene
Представьте, что вы могли бы сделать, если бы масштабируемость не была проблемой. Из этого практического руководства вы узнаете, как система управления базами данных Cassandra обрабатывает сотни терабайт данных, оставаясь при этом высокодоступной в нескольких центрах обработки данных. Это пересмотренное третье издание, обновленное для Cassandra 4.0 и новых разработок в экосистеме Cassandra, включая развертывание в Kubernetes с помощью K8ssandra, содержит технические подробности и практические примеры, которые помогут вам использовать эту базу данных в производственной среде.
Авторы Джефф Карпентер и Эбен Хьюитт демонстрируют преимущества нереляционного дизайна Cassandra, уделяя особое внимание моделированию данных. Разработчики, администраторы баз данных и архитекторы приложений, которые хотят решить проблему масштабирования базы данных или создать перспективное приложение, узнают, как использовать скорость и гибкость Cassandra.
Понимать распределенную и децентрализованную структуру Cassandra
Использовать язык запросов Cassandra (CQL) и cqlsh (оболочку CQL)
Создать рабочую модель данных и сравнить ее с эквивалентной реляционной моделью
Проектировать и разрабатывать приложения с использованием клиентских драйверов
Изучите топологию кластера и узнайте, как узлы обмениваются данными
Поддерживайте высокий уровень производительности в вашем кластере
Разверните Cassandra на месте, в облаке или с помощью Docker и Kubernetes
Интегрируйте Cassandra с Spark, Kafka, Elasticsearch, Solr и Lucene
Примеры страниц (скриншоты)
Оглавление
Foreword xiii
Preface xv
1. Beyond Relational Databases 1
What’s Wrong with Relational Databases? 1
A Quick Review of Relational Databases 5
Transactions, ACID-ity, and Two-Phase Commit 6
Schema 9
Sharding and Shared-Nothing Architecture 10
Web Scale 12
The Rise of NoSQL 13
Summary 16
2. Introducing Cassandra 17
The Cassandra Elevator Pitch 17
Cassandra in 50 Words or Less 17
Distributed and Decentralized 18
Elastic Scalability 19
High Availability and Fault Tolerance 19
Tuneable Consistency 20
Brewer’s CAP Theorem 23
Row-Oriented 27
High Performance 28
Where Did Cassandra Come From? 28
Is Cassandra a Good Fit for My Project? 30
Large Deployments 30
Lots of Writes, Statistics, and Analysis 30
Geographical Distribution 31
Hybrid Cloud and Multicloud Deployment 31
Getting Involved 31
Summary 34
3. Installing Cassandra 35
Installing the Apache Distribution 35
Extracting the Download 36
What’s in There? 36
Building from Source 38
Additional Build Targets 39
Running Cassandra 39
Setting the Environment 40
Starting the Server 41
Stopping Cassandra 43
Other Cassandra Distributions 44
Running the CQL Shell 45
Basic cqlsh Commands 46
cqlsh Help 46
Describing the Environment in cqlsh 47
Creating a Keyspace and Table in cqlsh 48
Writing and Reading Data in cqlsh 51
Running Cassandra in Docker 53
Summary 54
4. The Cassandra Query Language 55
The Relational Data Model 55
Cassandra’s Data Model 56
Clusters 59
Keyspaces 59
Tables 59
Columns 63
CQL Types 66
Numeric Data Types 67
Textual Data Types 67
Time and Identity Data Types 68
Other Simple Data Types 70
Collections 72
Tuples 75
User-Defined Types 76
Summary 79
5. Data Modeling 81
Conceptual Data Modeling 81
RDBMS Design 82
Design Differences Between RDBMS and Cassandra 83
Defining Application Queries 86
Logical Data Modeling 87
Hotel Logical Data Model 89
Reservation Logical Data Model 91
Physical Data Modeling 93
Hotel Physical Data Model 94
Reservation Physical Data Model 96
Evaluating and Refining 97
Calculating Partition Size 97
Calculating Size on Disk 98
Breaking Up Large Partitions 100
Defining Database Schema 101
Cassandra Data Modeling Tools 103
Summary 106
6. The Cassandra Architecture 107
Data Centers and Racks 107
Gossip and Failure Detection 108
Snitches 110
Rings and Tokens 111
Virtual Nodes 113
Partitioners 113
Replication Strategies 114
Consistency Levels 115
Queries and Coordinator Nodes 117
Hinted Handoff 118
Anti-Entropy, Repair, and Merkle Trees 119
Lightweight Transactions and Paxos 120
Memtables, SSTables, and Commit Logs 122
Bloom Filters 124
Caching 125
Compaction 125
Deletion and Tombstones 127
Managers and Services 128
Cassandra Daemon 128
Storage Engine 129
Storage Service 129
Storage Proxy 130
Table of Contents | vii
Messaging Service 130
Stream Manager 130
CQL Native Transport Server 131
System Keyspaces 131
Summary 134
7. Designing Applications with Cassandra 135
Hotel Application Design 135
Cassandra and Microservice Architecture 135
Microservice Architecture for a Hotel Application 137
Identifying Bounded Contexts 138
Identifying Services 138
Designing Microservice Persistence 140
Extending Designs 143
Secondary Indexes 144
Materialized Views 148
Reservation Service: A Sample Microservice 152
Design Choices for a Java Microservice 152
Deployment and Integration Considerations 153
Services, Keyspaces, and Clusters 153
Data Centers and Load Balancing 154
Interactions Between Microservices 154
Summary 156
8. Application Development with Drivers 157
DataStax Java Driver 158
Development Environment Configuration 158
Connecting to a Cluster 159
Statements 161
Simple Statements 162
Prepared Statements 163
Query Builder 165
Object Mapper 167
Asynchronous Execution 170
Driver Configuration 172
Metadata 178
Debugging and Monitoring 180
DataStax Python Driver 181
DataStax Node.js Driver 182
DataStax C# Driver 183
Other Cassandra Drivers 183
Summary 185
9. Writing and Reading Data 187
Writing 187
Write Consistency Levels 187
The Cassandra Write Path 189
Writing Files to Disk 191
Lightweight Transactions 193
Batches 196
Reading 199
Read Consistency Levels 199
The Cassandra Read Path 201
Read Repair 203
Range Queries, Ordering and Filtering 205
Paging 208
Deleting 210
Summary 212
10. Configuring and Deploying Cassandra 213
Cassandra Cluster Manager 213
Creating a Cluster 214
Adding Nodes to a Cluster 217
Dynamic Ring Participation 218
Node Configuration 219
Seed Nodes 219
Snitches 220
Partitioners 222
Tokens and Virtual Nodes 224
Network Interfaces 225
Data Storage 226
Startup and JVM Settings 227
Planning a Cluster Deployment 229
Cluster Topology and Replication Strategies 229
Sizing Your Cluster 232
Selecting Instances 234
Storage 234
Network 235
Cloud Deployment 236
Amazon Web Services 236
Google Cloud Platform 239
Microsoft Azure 240
Summary 241
11. Monitoring 243
Monitoring Cassandra with JMX 243
Cassandra’s MBeans 246
Database MBeans 247
Cluster-Related MBeans 250
Internal MBeans 252
Monitoring with nodetool 253
Getting Cluster Information 254
Getting Statistics 257
Virtual Tables 259
System Virtual Schema 260
System Views 261
Metrics 263
Logging 266
Examining Log Files 268
Full Query Logging 270
Summary 271
12. Maintenance 273
Health Check 273
Common Maintenance Tasks 274
Flush 274
Cleanup 275
Repair 276
Rebuilding Indexes 281
Moving Tokens 281
Adding Nodes 282
Adding Nodes to an Existing Data Center 282
Adding a Data Center to a Cluster 283
Handling Node Failure 284
Repairing Failed Nodes 285
Replacing Nodes 286
Removing Nodes 287
Upgrading Cassandra 290
Backup and Recovery 292
Taking a Snapshot 293
Clearing a Snapshot 294
Enabling Incremental Backup 294
Restoring from Snapshot 295
SSTable Utilities 296
Maintenance Tools 297
Netflix Priam 297
DataStax OpsCenter 298
Cassandra Sidecars 298
Cassandra Kubernetes Operators 299
Summary 303
13. Performance Tuning 305
Managing Performance 305
Setting Performance Goals 305
Benchmarking and Stress Testing 307
Monitoring Performance 312
Analyzing Performance Issues 313
Tracing 314
Tuning Methodology 316
Caching 317
Key Cache 317
Row Cache 318
Chunk Cache 319
Counter Cache 319
Saved Cache Settings 320
Memtables 320
Commit Logs 321
SSTables 323
Hinted Handoff 324
Compaction 325
Concurrency and Threading 328
Networking and Timeouts 329
JVM Settings 331
Memory 332
Garbage Collection 332
Summary 336
14. Security 337
Authentication and Authorization 339
Password Authenticator 339
Using CassandraAuthorizer 342
Role-Based Access Control 344
Encryption 346
SSL, TLS, and Certificates 347
Node-to-Node Encryption 350
Client-to-Node Encryption 352
JMX Security 353
Securing JMX Access 353
Security MBeans 355
Audit Logging 356
Summary 359
15. Migrating and Integrating 361
Knowing When to Migrate 361
Adapting the Data Model 362
Translating Entities 363
Translating Relationships 365
Adapting the Application 367
Refactoring Data Access 368
Maintaining Consistency 368
Migrating Stored Procedures 370
Planning the Deployment 375
Migrating Data 376
Zero-Downtime Migration 376
Bulk Loading 377
Common Integrations 379
Managing Data Flow with Apache Kafka 379
Searching with Apache Lucene, SOLR, and Elasticsearch 382
Analyzing Data with Apache Spark 382
Summary 389
Index 391
