Apache Pulsar in Action
- Length: 358 pages
- Edition: 1
- Language: English
- Publisher: Manning
- Publication Date: 2021-12-07
- ISBN-10: 1617296880
- ISBN-13: 9781617296888
- Sales Rank: #4611334 (See Top 100 Books)
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https://boxfanexpo.com/3kvnkm8mc3 Distributed applications demand reliable, high-performance messaging. The Apache Pulsar server-to-server messaging system provides a secure, stable platform without the need for a stream processing engine like Spark. Apache Pulsar in Action is a hands-on guide to building scalable streaming messaging systems for distributed applications and microservices systems.
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https://trevabrandonscharf.com/i4a127rfwmv Apache Pulsar in Action brief contents contents foreword preface acknowledgments about this book Who should read this book How this book is organized: A roadmap About the code Other online resources liveBook discussion forum about the author about the cover illustration Part 1: Getting started with Apache Pulsar Chapter 1: Introduction to Apache Pulsar 1.1 Enterprise messaging systems 1.1.1 Key capabilities 1.2 Message consumption patterns 1.2.1 Publish–subscribe messaging 1.2.2 Message queuing 1.3 The evolution of messaging systems 1.3.1 Generic messaging systems 1.3.2 Message-oriented middleware 1.3.3 Enterprise service bus 1.3.4 Distributed messaging systems 1.4 Comparison to Apache Kafka 1.4.1 Multilayered architecture 1.4.2 Message consumption 1.4.3 Data durability 1.4.4 Message acknowledgment 1.4.5 Message retention 1.5 Why do I need Pulsar? 1.5.1 Guaranteed message delivery 1.5.2 Infinite scalability 1.5.3 Resilient to failure 1.5.4 Support for millions of topics 1.5.5 Geo-replication and active failover 1.6 Real-world use cases 1.6.1 Unified messaging systems 1.6.2 Microservices platforms 1.6.3 Connected cars 1.6.4 Fraud detection Chapter 2: Pulsar concepts and architecture 2.1 Pulsar’s physical architecture 2.1.1 Pulsar’s layered architecture 2.1.2 Stateless serving layer 2.1.3 Stream storage layer 2.1.4 Metadata storage 2.2 Pulsar’s logical architecture 2.2.1 Tenants, namespaces, and topics 2.2.2 Addressing topics in Pulsar 2.2.3 Producers, consumers, and subscriptions 2.2.4 Subscription types 2.3 Message retention and expiration 2.3.1 Data retention 2.3.2 Backlog quotas 2.3.3 Message expiration 2.3.4 Message backlog vs. message expiration 2.4 Tiered storage Chapter 3: Interacting with Pulsar 3.1 Getting started with Pulsar 3.2 Administering Pulsar 3.2.1 Creating a tenant, namespace, and topic 3.2.2 Java Admin API 3.3 Pulsar clients 3.3.1 The Pulsar Java client 3.3.2 The Pulsar Python client 3.3.3 The Pulsar Go client 3.4 Advanced administration 3.4.1 Persistent topic metrics 3.4.2 Message inspection Part 2: Apache Pulsar development essentials Chapter 4: Pulsar functions 4.1 Stream processing 4.1.1 Traditional batching 4.1.2 Micro-batching 4.1.3 Stream native processing 4.2 What is Pulsar Functions? 4.2.1 Programming model 4.3 Developing Pulsar functions 4.3.1 Language native functions 4.3.2 The Pulsar SDK 4.3.3 Stateful functions 4.4 Testing Pulsar functions 4.4.1 Unit testing 4.4.2 Integration testing 4.5 Deploying Pulsar functions 4.5.1 Generating a deployment artifact 4.5.2 Function configuration 4.5.3 Function deployment 4.5.4 The function deployment life cycle 4.5.5 Deployment modes 4.5.6 Pulsar function data flow Chapter 5: Pulsar IO connectors 5.1 What are Pulsar IO connectors? 5.1.1 Sink connectors 5.1.2 Source connectors 5.1.3 PushSource connectors 5.2 Developing Pulsar IO connectors 5.2.1 Developing a sink connector 5.2.2 Developing a PushSource connector 5.3 Testing Pulsar IO connectors 5.3.1 Unit testing 5.3.2 Integration testing 5.3.3 Packaging Pulsar IO connectors 5.4 Deploying Pulsar IO connectors 5.4.1 Creating and deleting connectors 5.4.2 Debugging deployed connectors 5.5 Pulsar’s built-in connectors 5.5.1 Launching the MongoDB cluster 5.5.2 Link the Pulsar and MongoDB containers 5.5.3 Configure and create the MongoDB sink 5.6 Administering Pulsar IO connectors 5.6.1 Listing connectors 5.6.2 Monitoring connectors Chapter 6: Pulsar security 6.1 Transport encryption 6.2 Authentication 6.2.1 TLS authentication 6.2.2 JSON Web Token authentication 6.3 Authorization 6.3.1 Roles 6.3.2 An example scenario 6.4 Message encryption Chapter 7: Schema registry 7.1 Microservice communication 7.1.1 Microservice APIs 7.1.2 The need for a schema registry 7.2 The Pulsar schema registry 7.2.1 Architecture 7.2.2 Schema versioning 7.2.3 Schema compatibility 7.2.4 Schema compatibility check strategies 7.3 Using the schema registry 7.3.1 Modelling the food order event in Avro 7.3.2 Producing food order events 7.3.3 Consuming the food order events 7.3.4 Complete example 7.4 Evolving the schema Part 3: Hands-on application development with Apache Pulsar Chapter 8: Pulsar Functions patterns 8.1 Data pipelines 8.1.1 Procedural programming 8.1.2 DataFlow programming 8.2 Message routing patterns 8.2.1 Splitter pattern 8.2.2 Dynamic router pattern 8.2.3 Content-based router pattern 8.3 Message transformation patterns 8.3.1 Message translator pattern 8.3.2 Content enricher pattern 8.3.3 Content filter pattern Chapter 9: Resiliency patterns 9.1 Pulsar Functions resiliency 9.1.1 Adverse events 9.1.2 Fault detection 9.2 Resiliency design patterns 9.2.1 Retry pattern 9.2.2 Circuit breaker pattern 9.2.3 Rate limiter pattern 9.2.4 Time limiter pattern 9.2.5 Cache pattern 9.2.6 Fallback pattern 9.2.7 Credential refresh pattern 9.3 Multiple layers of resiliency Chapter 10: Data access 10.1 Data sources 10.2 Data access use cases 10.2.1 Device validation 10.2.2 Driver location data Chapter 11: Machine learning in Pulsar 11.1 Deploying ML models 11.1.1 Batch processing 11.1.2 Near real-time 11.2 Near real-time model deployment 11.3 Feature vectors 11.3.1 Feature stores 11.3.2 Feature calculation 11.4 Delivery time estimation 11.4.1 ML model export 11.4.2 Feature vector mapping 11.4.3 Model deployment 11.5 Neural nets 11.5.1 Neural net training 11.5.2 Neural net deployment in Java Chapter 12: Edge analytics 12.1 IIoT architecture 12.1.1 The perception and reaction layer 12.1.2 The transportation layer 12.1.3 The data processing layer 12.2 A Pulsar-based processing layer 12.3 Edge analytics 12.3.1 Telemetric data 12.3.2 Univariate and multivariate 12.4 Univariate analysis 12.4.1 Noise reduction 12.4.2 Statistical analysis 12.4.3 Approximation 12.5 Multivariate analysis 12.5.1 Creating a bidirectional messaging mesh 12.5.2 Multivariate dataset construction 12.6 Beyond the book appendix A: Running Pulsar on Kubernetes A.1 Create a Kubernetes cluster A.1.1 Install prerequisites A.1.2 Minikube A.2 The Pulsar Helm chart A.2.1 What is Helm? A.2.2 The Pulsar Helm chart A.3 Using the Pulsar Helm chart A.3.1 Administering Pulsar on Kubernetes A.3.2 Configuring clients appendix B: Geo-replication B.1 Synchronous geo-replication B.2 Asynchronous geo-replication B.2.1 Configuring asynchronous geo-replication B.3 Asynchronous geo-replication patterns B.3.1 Multi-active geo-replication B.3.2 Active-standby geo-replication B.3.3 Aggregation geo-replication index A B C D E F G H I J K L M N O P Q R S T U V W Y Z
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