Stream processing with Apache Flink: fundamentals, implementation, and operation of streaming applications
"Get started with Apache Flink, the open source framework that powers some of the world's largest stream processing applications. With this practical book, you'll explore the fundamental concepts of parallel stream processing and discover how this technology differs from traditional b...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Beijing
O'Reilly
2019
|
| Ausgabe: | First edition |
| Schlagworte: | |
| Online-Zugang: | FHD01 |
| Zusammenfassung: | "Get started with Apache Flink, the open source framework that powers some of the world's largest stream processing applications. With this practical book, you'll explore the fundamental concepts of parallel stream processing and discover how this technology differs from traditional batch data processing. Longtime Apache Flink committers Fabian Hueske and Vasia Kalavri show you how to implement scalable streaming applications with Flink's DataStream API and continuously run and maintain these applications in operational environments. Stream processing is ideal for many use cases, including low-latency ETL, streaming analytics, and real-time dashboards as well as fraud detection, anomaly detection, and alerting. You can process continuous data of any kind, including user interactions, financial transactions, and loT data, as soon as you generate them."-- |
| Beschreibung: | 1 Online-Ressource (xiii, 292 Seiten) |
| ISBN: | 9781491974261 |
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| 245 | 1 | 0 | |a Stream processing with Apache Flink |b fundamentals, implementation, and operation of streaming applications |c Fabian Hueske and Vasiliki Kalavri |
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| 505 | 8 | |a Cover; Copyright; Table of Contents; Preface; What You Will Learn in This Book; Conventions Used in This Book; Using Code Examples; O'Reilly Online Learning; How to Contact Us; Acknowledgments; Chapter 1. Introduction to Stateful Stream Processing; Traditional Data Infrastructures; Transactional Processing; Analytical Processing; Stateful Stream Processing; Event-Driven Applications; Data Pipelines; Streaming Analytics; The Evolution of Open Source Stream Processing; A Bit of History; A Quick Look at Flink; Running Your First Flink Application; Summary | |
| 505 | 8 | |a Chapter 2. Stream Processing FundamentalsIntroduction to Dataflow Programming; Dataflow Graphs; Data Parallelism and Task Parallelism; Data Exchange Strategies; Processing Streams in Parallel; Latency and Throughput; Operations on Data Streams; Time Semantics; What Does One Minute Mean in Stream Processing?; Processing Time; Event Time; Watermarks; Processing Time Versus Event Time; State and Consistency Models; Task Failures; Result Guarantees; Summary; Chapter 3. The Architecture of Apache Flink; System Architecture; Components of a Flink Setup; Application Deployment; Task Execution | |
| 505 | 8 | |a Highly Available SetupData Transfer in Flink; Credit-Based Flow Control; Task Chaining; Event-Time Processing; Timestamps; Watermarks; Watermark Propagation and Event Time; Timestamp Assignment and Watermark Generation; State Management; Operator State; Keyed State; State Backends; Scaling Stateful Operators; Checkpoints, Savepoints, and State Recovery; Consistent Checkpoints; Recovery from a Consistent Checkpoint; Flink's Checkpointing Algorithm; Performace Implications of Checkpointing; Savepoints; Summary; Chapter 4. Setting Up a Development Environment for Apache Flink; Required Software | |
| 505 | 8 | |a Run and Debug Flink Applications in an IDEImport the Book's Examples in an IDE; Run Flink Applications in an IDE; Debug Flink Applications in an IDE; Bootstrap a Flink Maven Project; Summary; Chapter 5. The DataStream API (v1.7); Hello, Flink!; Set Up the Execution Environment; Read an Input Stream; Apply Transformations; Output the Result; Execute; Transformations; Basic Transformations; KeyedStream Transformations; Multistream Transformations; Distribution Transformations; Setting the Parallelism; Types; Supported Data Types; Creating Type Information for Data Types | |
| 505 | 8 | |a Explicitly Providing Type InformationDefining Keys and Referencing Fields; Field Positions; Field Expressions; Key Selectors; Implementing Functions; Function Classes; Lambda Functions; Rich Functions; Including External and Flink Dependencies; Summary; Chapter 6. Time-Based and Window Operators; Configuring Time Characteristics; Assigning Timestamps and Generating Watermarks; Watermarks, Latency, and Completeness; Process Functions; TimerService and Timers; Emitting to Side Outputs; CoProcessFunction; Window Operators; Defining Window Operators; Built-in Window Assigners | |
| 520 | |a "Get started with Apache Flink, the open source framework that powers some of the world's largest stream processing applications. With this practical book, you'll explore the fundamental concepts of parallel stream processing and discover how this technology differs from traditional batch data processing. Longtime Apache Flink committers Fabian Hueske and Vasia Kalavri show you how to implement scalable streaming applications with Flink's DataStream API and continuously run and maintain these applications in operational environments. Stream processing is ideal for many use cases, including low-latency ETL, streaming analytics, and real-time dashboards as well as fraud detection, anomaly detection, and alerting. You can process continuous data of any kind, including user interactions, financial transactions, and loT data, as soon as you generate them."-- | ||
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| 650 | 4 | |a Streaming technology (Telecommunications) / Software | |
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| 650 | 7 | |a Streaming technology (Telecommunications) |2 fast | |
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| author | Hüske, Fabian Kalavri, Vasiliki |
| author_GND | (DE-588)1104145170 |
| author_facet | Hüske, Fabian Kalavri, Vasiliki |
| author_role | aut aut |
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| contents | Cover; Copyright; Table of Contents; Preface; What You Will Learn in This Book; Conventions Used in This Book; Using Code Examples; O'Reilly Online Learning; How to Contact Us; Acknowledgments; Chapter 1. Introduction to Stateful Stream Processing; Traditional Data Infrastructures; Transactional Processing; Analytical Processing; Stateful Stream Processing; Event-Driven Applications; Data Pipelines; Streaming Analytics; The Evolution of Open Source Stream Processing; A Bit of History; A Quick Look at Flink; Running Your First Flink Application; Summary Chapter 2. Stream Processing FundamentalsIntroduction to Dataflow Programming; Dataflow Graphs; Data Parallelism and Task Parallelism; Data Exchange Strategies; Processing Streams in Parallel; Latency and Throughput; Operations on Data Streams; Time Semantics; What Does One Minute Mean in Stream Processing?; Processing Time; Event Time; Watermarks; Processing Time Versus Event Time; State and Consistency Models; Task Failures; Result Guarantees; Summary; Chapter 3. The Architecture of Apache Flink; System Architecture; Components of a Flink Setup; Application Deployment; Task Execution Highly Available SetupData Transfer in Flink; Credit-Based Flow Control; Task Chaining; Event-Time Processing; Timestamps; Watermarks; Watermark Propagation and Event Time; Timestamp Assignment and Watermark Generation; State Management; Operator State; Keyed State; State Backends; Scaling Stateful Operators; Checkpoints, Savepoints, and State Recovery; Consistent Checkpoints; Recovery from a Consistent Checkpoint; Flink's Checkpointing Algorithm; Performace Implications of Checkpointing; Savepoints; Summary; Chapter 4. Setting Up a Development Environment for Apache Flink; Required Software Run and Debug Flink Applications in an IDEImport the Book's Examples in an IDE; Run Flink Applications in an IDE; Debug Flink Applications in an IDE; Bootstrap a Flink Maven Project; Summary; Chapter 5. The DataStream API (v1.7); Hello, Flink!; Set Up the Execution Environment; Read an Input Stream; Apply Transformations; Output the Result; Execute; Transformations; Basic Transformations; KeyedStream Transformations; Multistream Transformations; Distribution Transformations; Setting the Parallelism; Types; Supported Data Types; Creating Type Information for Data Types Explicitly Providing Type InformationDefining Keys and Referencing Fields; Field Positions; Field Expressions; Key Selectors; Implementing Functions; Function Classes; Lambda Functions; Rich Functions; Including External and Flink Dependencies; Summary; Chapter 6. Time-Based and Window Operators; Configuring Time Characteristics; Assigning Timestamps and Generating Watermarks; Watermarks, Latency, and Completeness; Process Functions; TimerService and Timers; Emitting to Side Outputs; CoProcessFunction; Window Operators; Defining Window Operators; Built-in Window Assigners |
| ctrlnum | (OCoLC)1258967225 (DE-599)BVBBV047349081 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | First edition |
| format | Electronic eBook |
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| index_date | 2024-07-03T17:37:13Z |
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| spelling | Hüske, Fabian Verfasser (DE-588)1104145170 aut Stream processing with Apache Flink fundamentals, implementation, and operation of streaming applications Fabian Hueske and Vasiliki Kalavri First edition Beijing O'Reilly 2019 1 Online-Ressource (xiii, 292 Seiten) txt rdacontent c rdamedia cr rdacarrier Cover; Copyright; Table of Contents; Preface; What You Will Learn in This Book; Conventions Used in This Book; Using Code Examples; O'Reilly Online Learning; How to Contact Us; Acknowledgments; Chapter 1. Introduction to Stateful Stream Processing; Traditional Data Infrastructures; Transactional Processing; Analytical Processing; Stateful Stream Processing; Event-Driven Applications; Data Pipelines; Streaming Analytics; The Evolution of Open Source Stream Processing; A Bit of History; A Quick Look at Flink; Running Your First Flink Application; Summary Chapter 2. Stream Processing FundamentalsIntroduction to Dataflow Programming; Dataflow Graphs; Data Parallelism and Task Parallelism; Data Exchange Strategies; Processing Streams in Parallel; Latency and Throughput; Operations on Data Streams; Time Semantics; What Does One Minute Mean in Stream Processing?; Processing Time; Event Time; Watermarks; Processing Time Versus Event Time; State and Consistency Models; Task Failures; Result Guarantees; Summary; Chapter 3. The Architecture of Apache Flink; System Architecture; Components of a Flink Setup; Application Deployment; Task Execution Highly Available SetupData Transfer in Flink; Credit-Based Flow Control; Task Chaining; Event-Time Processing; Timestamps; Watermarks; Watermark Propagation and Event Time; Timestamp Assignment and Watermark Generation; State Management; Operator State; Keyed State; State Backends; Scaling Stateful Operators; Checkpoints, Savepoints, and State Recovery; Consistent Checkpoints; Recovery from a Consistent Checkpoint; Flink's Checkpointing Algorithm; Performace Implications of Checkpointing; Savepoints; Summary; Chapter 4. Setting Up a Development Environment for Apache Flink; Required Software Run and Debug Flink Applications in an IDEImport the Book's Examples in an IDE; Run Flink Applications in an IDE; Debug Flink Applications in an IDE; Bootstrap a Flink Maven Project; Summary; Chapter 5. The DataStream API (v1.7); Hello, Flink!; Set Up the Execution Environment; Read an Input Stream; Apply Transformations; Output the Result; Execute; Transformations; Basic Transformations; KeyedStream Transformations; Multistream Transformations; Distribution Transformations; Setting the Parallelism; Types; Supported Data Types; Creating Type Information for Data Types Explicitly Providing Type InformationDefining Keys and Referencing Fields; Field Positions; Field Expressions; Key Selectors; Implementing Functions; Function Classes; Lambda Functions; Rich Functions; Including External and Flink Dependencies; Summary; Chapter 6. Time-Based and Window Operators; Configuring Time Characteristics; Assigning Timestamps and Generating Watermarks; Watermarks, Latency, and Completeness; Process Functions; TimerService and Timers; Emitting to Side Outputs; CoProcessFunction; Window Operators; Defining Window Operators; Built-in Window Assigners "Get started with Apache Flink, the open source framework that powers some of the world's largest stream processing applications. With this practical book, you'll explore the fundamental concepts of parallel stream processing and discover how this technology differs from traditional batch data processing. Longtime Apache Flink committers Fabian Hueske and Vasia Kalavri show you how to implement scalable streaming applications with Flink's DataStream API and continuously run and maintain these applications in operational environments. Stream processing is ideal for many use cases, including low-latency ETL, streaming analytics, and real-time dashboards as well as fraud detection, anomaly detection, and alerting. You can process continuous data of any kind, including user interactions, financial transactions, and loT data, as soon as you generate them."-- Apache Flink (Electronic resource) Streaming technology (Telecommunications) / Software Big data COMPUTERS / General bisacsh Big data fast Streaming technology (Telecommunications) fast Apache Programm (DE-588)4460947-4 gnd rswk-swf Streaming Kommunikationstechnik (DE-588)4614855-3 gnd rswk-swf Apache Programm (DE-588)4460947-4 s Streaming Kommunikationstechnik (DE-588)4614855-3 s DE-604 Kalavri, Vasiliki Verfasser aut Erscheint auch als Druck-Ausgabe 978-1-491-97429-2 |
| spellingShingle | Hüske, Fabian Kalavri, Vasiliki Stream processing with Apache Flink fundamentals, implementation, and operation of streaming applications Cover; Copyright; Table of Contents; Preface; What You Will Learn in This Book; Conventions Used in This Book; Using Code Examples; O'Reilly Online Learning; How to Contact Us; Acknowledgments; Chapter 1. Introduction to Stateful Stream Processing; Traditional Data Infrastructures; Transactional Processing; Analytical Processing; Stateful Stream Processing; Event-Driven Applications; Data Pipelines; Streaming Analytics; The Evolution of Open Source Stream Processing; A Bit of History; A Quick Look at Flink; Running Your First Flink Application; Summary Chapter 2. Stream Processing FundamentalsIntroduction to Dataflow Programming; Dataflow Graphs; Data Parallelism and Task Parallelism; Data Exchange Strategies; Processing Streams in Parallel; Latency and Throughput; Operations on Data Streams; Time Semantics; What Does One Minute Mean in Stream Processing?; Processing Time; Event Time; Watermarks; Processing Time Versus Event Time; State and Consistency Models; Task Failures; Result Guarantees; Summary; Chapter 3. The Architecture of Apache Flink; System Architecture; Components of a Flink Setup; Application Deployment; Task Execution Highly Available SetupData Transfer in Flink; Credit-Based Flow Control; Task Chaining; Event-Time Processing; Timestamps; Watermarks; Watermark Propagation and Event Time; Timestamp Assignment and Watermark Generation; State Management; Operator State; Keyed State; State Backends; Scaling Stateful Operators; Checkpoints, Savepoints, and State Recovery; Consistent Checkpoints; Recovery from a Consistent Checkpoint; Flink's Checkpointing Algorithm; Performace Implications of Checkpointing; Savepoints; Summary; Chapter 4. Setting Up a Development Environment for Apache Flink; Required Software Run and Debug Flink Applications in an IDEImport the Book's Examples in an IDE; Run Flink Applications in an IDE; Debug Flink Applications in an IDE; Bootstrap a Flink Maven Project; Summary; Chapter 5. The DataStream API (v1.7); Hello, Flink!; Set Up the Execution Environment; Read an Input Stream; Apply Transformations; Output the Result; Execute; Transformations; Basic Transformations; KeyedStream Transformations; Multistream Transformations; Distribution Transformations; Setting the Parallelism; Types; Supported Data Types; Creating Type Information for Data Types Explicitly Providing Type InformationDefining Keys and Referencing Fields; Field Positions; Field Expressions; Key Selectors; Implementing Functions; Function Classes; Lambda Functions; Rich Functions; Including External and Flink Dependencies; Summary; Chapter 6. Time-Based and Window Operators; Configuring Time Characteristics; Assigning Timestamps and Generating Watermarks; Watermarks, Latency, and Completeness; Process Functions; TimerService and Timers; Emitting to Side Outputs; CoProcessFunction; Window Operators; Defining Window Operators; Built-in Window Assigners Apache Flink (Electronic resource) Streaming technology (Telecommunications) / Software Big data COMPUTERS / General bisacsh Big data fast Streaming technology (Telecommunications) fast Apache Programm (DE-588)4460947-4 gnd Streaming Kommunikationstechnik (DE-588)4614855-3 gnd |
| subject_GND | (DE-588)4460947-4 (DE-588)4614855-3 |
| title | Stream processing with Apache Flink fundamentals, implementation, and operation of streaming applications |
| title_auth | Stream processing with Apache Flink fundamentals, implementation, and operation of streaming applications |
| title_exact_search | Stream processing with Apache Flink fundamentals, implementation, and operation of streaming applications |
| title_exact_search_txtP | Stream processing with Apache Flink fundamentals, implementation, and operation of streaming applications |
| title_full | Stream processing with Apache Flink fundamentals, implementation, and operation of streaming applications Fabian Hueske and Vasiliki Kalavri |
| title_fullStr | Stream processing with Apache Flink fundamentals, implementation, and operation of streaming applications Fabian Hueske and Vasiliki Kalavri |
| title_full_unstemmed | Stream processing with Apache Flink fundamentals, implementation, and operation of streaming applications Fabian Hueske and Vasiliki Kalavri |
| title_short | Stream processing with Apache Flink |
| title_sort | stream processing with apache flink fundamentals implementation and operation of streaming applications |
| title_sub | fundamentals, implementation, and operation of streaming applications |
| topic | Apache Flink (Electronic resource) Streaming technology (Telecommunications) / Software Big data COMPUTERS / General bisacsh Big data fast Streaming technology (Telecommunications) fast Apache Programm (DE-588)4460947-4 gnd Streaming Kommunikationstechnik (DE-588)4614855-3 gnd |
| topic_facet | Apache Flink (Electronic resource) Streaming technology (Telecommunications) / Software Big data COMPUTERS / General Streaming technology (Telecommunications) Apache Programm Streaming Kommunikationstechnik |
| work_keys_str_mv | AT huskefabian streamprocessingwithapacheflinkfundamentalsimplementationandoperationofstreamingapplications AT kalavrivasiliki streamprocessingwithapacheflinkfundamentalsimplementationandoperationofstreamingapplications |