Kafka in Action Final Release 1st Edition by Dylan Scott, Viktor Gamov, Dave Klein ISBN 9781617295232 161729523X

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Product details:

ISBN 10: 161729523X
ISBN 13: 9781617295232
Author: Dylan Scott, Viktor Gamov, Dave Klein

Kafka in Action Final Release 1st Edition Table of contents:

Part 1. Getting started

1 Introduction to Kafka

1.1 What is Kafka?

1.2 Kafka usage

1.2.1 Kafka for the developer

1.2.2 Explaining Kafka to your manager

1.3 Kafka myths

1.3.1 Kafka only works with Hadoop®

1.3.2 Kafka is the same as other message brokers

1.4 Kafka in the real world

1.4.1 Early examples

1.4.2 Later examples

1.4.3 When Kafka might not be the right fit

1.5 Online resources to get started

Summary

References

2 Getting to know Kafka

2.1 Producing and consuming a message

2.2 What are brokers?

2.3 Tour of Kafka

2.3.1 Producers and consumers

2.3.2 Topics overview

2.3.3 ZooKeeper usage

2.3.4 Kafka’s high-level architecture

2.3.5 The commit log

2.4 Various source code packages and what they do

2.4.1 Kafka Streams

2.4.2 Kafka Connect

2.4.3 AdminClient package

2.4.4 ksqlDB

2.5 Confluent clients

2.6 Stream processing and terminology

2.6.1 Stream processing

2.6.2 What exactly-once means

Summary

References

Part 2. Applying Kafka

3 Designing a Kafka project

3.1 Designing a Kafka project

3.1.1 Taking over an existing data architecture

3.1.2 A first change

3.1.3 Built-in features

3.1.4 Data for our invoices

3.2 Sensor event design

3.2.1 Existing issues

3.2.2 Why Kafka is the right fit

3.2.3 Thought starters on our design

3.2.4 User data requirements

3.2.5 High-level plan for applying our questions

3.2.6 Reviewing our blueprint

3.3 Format of your data

3.3.1 Plan for data

3.3.2 Dependency setup

Summary

References

4 Producers: Sourcing data

4.1 An example

4.1.1 Producer notes

4.2 Producer options

4.2.1 Configuring the broker list

4.2.2 How to go fast (or go safer)

4.2.3 Timestamps

4.3 Generating code for our requirements

4.3.1 Client and broker versions

Summary

References

5 Consumers: Unlocking data

5.1 An example

5.1.1 Consumer options

5.1.2 Understanding our coordinates

5.2 How consumers interact

5.3 Tracking

5.3.1 Group coordinator

5.3.2 Partition assignment strategy

5.4 Marking our place

5.5 Reading from a compacted topic

5.6 Retrieving code for our factory requirements

5.6.1 Reading options

5.6.2 Requirements

Summary

References

6 Brokers

6.1 Introducing the broker

6.2 Role of ZooKeeper

6.3 Options at the broker level

6.3.1 Kafka’s other logs: Application logs

6.3.2 Server log

6.3.3 Managing state

6.4 Partition replica leaders and their role

6.4.1 Losing data

6.5 Peeking into Kafka

6.5.1 Cluster maintenance

6.5.2 Adding a broker

6.5.3 Upgrading your cluster

6.5.4 Upgrading your clients

6.5.5 Backups

6.6 A note on stateful systems

6.7 Exercise

Summary

References

7 Topics and partitions

7.1 Topics

7.1.1 Topic-creation options

7.1.2 Replication factors

7.2 Partitions

7.2.1 Partition location

7.2.2 Viewing our logs

7.3 Testing with EmbeddedKafkaCluster

7.3.1 Using Kafka Testcontainers

7.4 Topic compaction

Summary

References

8 Kafka storage

8.1 How long to store data

8.2 Data movement

8.2.1 Keeping the original event

8.2.2 Moving away from a batch mindset

8.3 Tools

8.3.1 Apache Flume

8.3.2 Red Hat® Debezium™

8.3.3 Secor

8.3.4 Example use case for data storage

8.4 Bringing data back into Kafka

8.4.1 Tiered storage

8.5 Architectures with Kafka

8.5.1 Lambda architecture

8.5.2 Kappa architecture

8.6 Multiple cluster setups

8.6.1 Scaling by adding clusters

8.7 Cloud- and container-based storage options

8.7.1 Kubernetes clusters

Summary

References

9 Management: Tools and logging

9.1 Administration clients

9.1.1 Administration in code with AdminClient

9.1.2 kcat

9.1.3 Confluent REST Proxy API

9.2 Running Kafka as a systemd service

9.3 Logging

9.3.1 Kafka application logs

9.3.2 ZooKeeper logs

9.4 Firewalls

9.4.1 Advertised listeners

9.5 Metrics

9.5.1 JMX console

9.6 Tracing option

9.6.1 Producer logic

9.6.2 Consumer logic

9.6.3 Overriding clients

9.7 General monitoring tools

Summary

References

Part 3. Going further

10 Protecting Kafka

10.1 Security basics

10.1.1 Encryption with SSL

10.1.2 SSL between brokers and clients

10.1.3 SSL between brokers

10.2 Kerberos and the Simple Authentication and Security Layer (SASL)

10.3 Authorization in Kafka

10.3.1 Access control lists (ACLs)

10.3.2 Role-based access control (RBAC)

10.4 ZooKeeper

10.4.1 Kerberos setup

10.5 Quotas

10.5.1 Network bandwidth quota

10.5.2 Request rate quotas

10.6 Data at rest

10.6.1 Managed options

Summary

References

11 Schema registry

11.1 A proposed Kafka maturity model

11.1.1 Level 0

11.1.2 Level 1

11.1.3 Level 2

11.1.4 Level 3

11.2 The Schema Registry

11.2.1 Installing the Confluent Schema Registry

11.2.2 Registry configuration

11.3 Schema features

11.3.1 REST API

11.3.2 Client library

11.4 Compatibility rules

11.4.1 Validating schema modifications

11.5 Alternative to a schema registry

Summary

References

12 Stream processing with Kafka Streams and ksqlDB

12.1 Kafka Streams

12.1.1 KStreams API DSL

12.1.2 KTable API

12.1.3 GlobalKTable API

12.1.4 Processor API

12.1.5 Kafka Streams setup

12.2 ksqlDB: An event-streaming database

12.2.1 Queries

12.2.2 Local development

12.2.3 ksqlDB architecture

12.3 Going further

12.3.1 Kafka Improvement Proposals (KIPs)

12.3.2 Kafka projects you can explore

12.3.3 Community Slack channel

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Tags: Dylan Scott, Viktor Gamov, Dave Klein, Action, Kafka