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ویرایش:
نویسندگان: Mitch Seymour
سری:
ISBN (شابک) : 9781492062493
ناشر: O'Reilly Media, Inc.
سال نشر: 2021
تعداد صفحات:
زبان: English
فرمت فایل : EPUB (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 5 Mb
در صورت تبدیل فایل کتاب Mastering Kafka Streams and ksqlDB به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب تسلط بر جریانهای کافکا و ksqlDB نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
Cover Copyright Table of Contents Foreword Preface Who Should Read This Book Navigating This Book Source Code Kafka Streams Version ksqlDB Version Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments Part I. Kafka Chapter 1. A Rapid Introduction to Kafka Communication Model How Are Streams Stored? Topics and Partitions Events Kafka Cluster and Brokers Consumer Groups Installing Kafka Hello, Kafka Summary Part II. Kafka Streams Chapter 2. Getting Started with Kafka Streams The Kafka Ecosystem Before Kafka Streams Enter Kafka Streams Features at a Glance Operational Characteristics Scalability Reliability Maintainability Comparison to Other Systems Deployment Model Processing Model Kappa Architecture Use Cases Processor Topologies Sub-Topologies Depth-First Processing Benefits of Dataflow Programming Tasks and Stream Threads High-Level DSL Versus Low-Level Processor API Introducing Our Tutorial: Hello, Streams Project Setup Creating a New Project Adding the Kafka Streams Dependency DSL Processor API Streams and Tables Stream/Table Duality KStream, KTable, GlobalKTable Summary Chapter 3. Stateless Processing Stateless Versus Stateful Processing Introducing Our Tutorial: Processing a Twitter Stream Project Setup Adding a KStream Source Processor Serialization/Deserialization Building a Custom Serdes Defining Data Classes Implementing a Custom Deserializer Implementing a Custom Serializer Building the Tweet Serdes Filtering Data Branching Data Translating Tweets Merging Streams Enriching Tweets Avro Data Class Sentiment Analysis Serializing Avro Data Registryless Avro Serdes Schema Registry–Aware Avro Serdes Adding a Sink Processor Running the Code Empirical Verification Summary Chapter 4. Stateful Processing Benefits of Stateful Processing Preview of Stateful Operators State Stores Common Characteristics Persistent Versus In-Memory Stores Introducing Our Tutorial: Video Game Leaderboard Project Setup Data Models Adding the Source Processors KStream KTable GlobalKTable Registering Streams and Tables Joins Join Operators Join Types Co-Partitioning Value Joiners KStream to KTable Join (players Join) KStream to GlobalKTable Join (products Join) Grouping Records Grouping Streams Grouping Tables Aggregations Aggregating Streams Aggregating Tables Putting It All Together Interactive Queries Materialized Stores Accessing Read-Only State Stores Querying Nonwindowed Key-Value Stores Local Queries Remote Queries Summary Chapter 5. Windows and Time Introducing Our Tutorial: Patient Monitoring Application Project Setup Data Models Time Semantics Timestamp Extractors Included Timestamp Extractors Custom Timestamp Extractors Registering Streams with a Timestamp Extractor Windowing Streams Window Types Selecting a Window Windowed Aggregation Emitting Window Results Grace Period Suppression Filtering and Rekeying Windowed KTables Windowed Joins Time-Driven Dataflow Alerts Sink Querying Windowed Key-Value Stores Summary Chapter 6. Advanced State Management Persistent Store Disk Layout Fault Tolerance Changelog Topics Standby Replicas Rebalancing: Enemy of the State (Store) Preventing State Migration Sticky Assignment Static Membership Reducing the Impact of Rebalances Incremental Cooperative Rebalancing Controlling State Size Deduplicating Writes with Record Caches State Store Monitoring Adding State Listeners Adding State Restore Listeners Built-in Metrics Interactive Queries Custom State Stores Summary Chapter 7. Processor API When to Use the Processor API Introducing Our Tutorial: IoT Digital Twin Service Project Setup Data Models Adding Source Processors Adding Stateless Stream Processors Creating Stateless Processors Creating Stateful Processors Periodic Functions with Punctuate Accessing Record Metadata Adding Sink Processors Interactive Queries Putting It All Together Combining the Processor API with the DSL Processors and Transformers Putting It All Together: Refactor Summary Part III. ksqlDB Chapter 8. Getting Started with ksqlDB What Is ksqlDB? When to Use ksqlDB Evolution of a New Kind of Database Kafka Streams Integration Connect Integration How Does ksqlDB Compare to a Traditional SQL Database? Similarities Differences Architecture ksqlDB Server ksqlDB Clients Deployment Modes Interactive Mode Headless Mode Tutorial Installing ksqlDB Running a ksqlDB Server Precreating Topics Using the ksqlDB CLI Summary Chapter 9. Data Integration with ksqlDB Kafka Connect Overview External Versus Embedded Connect External Mode Embedded Mode Configuring Connect Workers Converters and Serialization Formats Tutorial Installing Connectors Creating Connectors with ksqlDB Showing Connectors Describing Connectors Dropping Connectors Verifying the Source Connector Interacting with the Kafka Connect Cluster Directly Introspecting Managed Schemas Summary Chapter 10. Stream Processing Basics with ksqlDB Tutorial: Monitoring Changes at Netflix Project Setup Source Topics Data Types Custom Types Collections Creating Source Collections With Clause Working with Streams and Tables Showing Streams and Tables Describing Streams and Tables Altering Streams and Tables Dropping Streams and Tables Basic Queries Insert Values Simple Selects (Transient Push Queries) Projection Filtering Flattening/Unnesting Complex Structures Conditional Expressions Coalesce IFNULL Case Statements Writing Results Back to Kafka (Persistent Queries) Creating Derived Collections Putting It All Together Summary Chapter 11. Intermediate and Advanced Stream Processing with ksqlDB Project Setup Bootstrapping an Environment from a SQL File Data Enrichment Joins Windowed Joins Aggregations Aggregation Basics Windowed Aggregations Materialized Views Clients Pull Queries Curl Push Queries Push Queries via Curl Functions and Operators Operators Showing Functions Describing Functions Creating Custom Functions Additional Resources for Custom ksqlDB Functions Summary Part IV. The Road to Production Chapter 12. Testing, Monitoring, and Deployment Testing Testing ksqlDB Queries Testing Kafka Streams Behavioral Tests Benchmarking Kafka Cluster Benchmarking Final Thoughts on Testing Monitoring Monitoring Checklist Extracting JMX Metrics Deployment ksqlDB Containers Kafka Streams Containers Container Orchestration Operations Resetting a Kafka Streams Application Rate-Limiting the Output of Your Application Upgrading Kafka Streams Upgrading ksqlDB Summary Appendix A. Kafka Streams Configuration Configuration Management Configuration Properties Consumer-Specific Configurations Appendix B. ksqlDB Configuration Query Configurations Server Configurations Security Configurations Index About the Author Colophon