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Stream Processing with Apache Flink.pdf

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 相关标签： processing Apache flink LIN PAC
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实例介绍

【实例简介】Stream Processing with Apache Flink

【实例截图】

【核心代码】

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1. Preface
a. What You Will Learn in This Book
b. Conventions Used in This Book
c. Using Code Examples
d. O’Reilly Online Learning
e. How to Contact Us
f. Acknowledgments
2. 1. Introduction to Stateful Stream Processing
a. Traditional Data Infrastructures
i. Transactional Processing
ii. Analytical Processing
b. Stateful Stream Processing
i. Event-Driven Applications
ii. Data Pipelines
iii. Streaming Analytics
c. The Evolution of Open Source Stream Processing
i. A Bit of History
d. A Quick Look at Flink
i. Running Your First Flink Application
e. Summary
3. 2. Stream Processing Fundamentals
a. Introduction to Dataflow Programming
i. Dataflow Graphs
ii. Data Parallelism and Task Parallelism
iii. Data Exchange Strategies
b. Processing Streams in Parallel
i. Latency and Throughput
ii. Operations on Data Streams
c. Time Semantics
i. What Does One Minute Mean in Stream
Processing?
ii. Processing Time
iii. Event Time
iv. Watermarks
v. Processing Time Versus Event Time
d. State and Consistency Models
i. Task Failures
ii. Result Guarantees
e. Summary
4. 3. The Architecture of Apache Flink
a. System Architecture
i. Components of a Flink Setup
ii. Application Deployment
iii. Task Execution
iv. Highly Available Setup
b. Data Transfer in Flink
i. Credit-Based Flow Control
ii. Task Chaining
c. Event-Time Processing
i. Timestamps
ii. Watermarks
iii. Watermark Propagation and Event Time
iv. Timestamp Assignment and Watermark
Generation
d. State Management
i. Operator State
ii. Keyed State
iii. State Backends
iv. Scaling Stateful Operators
e. Checkpoints, Savepoints, and State Recovery
i. Consistent Checkpoints
ii. Recovery from a Consistent Checkpoint
iii. Flink’s Checkpointing Algorithm
iv. Performace Implications of
Checkpointing
v. Savepoints
f. Summary
5. 4. Setting Up a Development Environment for Apache
Flink
a. Required Software
b. Run and Debug Flink Applications in an IDE
i. Import the Book’s Examples in an IDE
ii. Run Flink Applications in an IDE
iii. Debug Flink Applications in an IDE
c. Bootstrap a Flink Maven Project
d. Summary
6. 5. The DataStream API (v1.7)
a. Hello, Flink!
i. Set Up the Execution Environment
ii. Read an Input Stream
iii. Apply Transformations
iv. Output the Result
v. Execute
b. Transformations
i. Basic Transformations
ii. KeyedStream Transformations
iii. Multistream Transformations
iv. Distribution Transformations
c. Setting the Parallelism
d. Types
i. Supported Data Types
ii. Creating Type Information for Data
Types
iii. Explicitly Providing Type Information
e. Defining Keys and Referencing Fields
i. Field Positions
ii. Field Expressions
iii. Key Selectors
f. Implementing Functions
i. Function Classes
ii. Lambda Functions
iii. Rich Functions
g. Including External and Flink Dependencies
h. Summary
7. 6. Time-Based and Window Operators
a. Configuring Time Characteristics
i. Assigning Timestamps and Generating
Watermarks
ii. Watermarks, Latency, and Completeness
b. Process Functions
i. TimerService and Timers
ii. Emitting to Side Outputs
iii. CoProcessFunction
c. Window Operators
i. Defining Window Operators
ii. Built-in Window Assigners
iii. Applying Functions on Windows
iv. Customizing Window Operators
d. Joining Streams on Time
i. Interval Join
ii. Window Join
e. Handling Late Data
i. Dropping Late Events
ii. Redirecting Late Events
iii. Updating Results by Including Late
Events
f. Summary
8. 7. Stateful Operators and Applications
a. Implementing Stateful Functions
i. Declaring Keyed State at
RuntimeContext
ii. Implementing Operator List State with
the ListCheckpointed Interface
iii. Using Connected Broadcast State
iv. Using the CheckpointedFunction
Interface
v. Receiving Notifications About
Completed Checkpoints
b. Enabling Failure Recovery for Stateful
Applications
c. Ensuring the Maintainability of Stateful
Applications
i. Specifying Unique Operator Identifiers
ii. Defining the Maximum Parallelism of
Keyed State Operators
d. Performance and Robustness of Stateful
Applications
i. Choosing a State Backend
ii. Choosing a State Primitive
iii. Preventing Leaking State
e. Evolving Stateful Applications
i. Updating an Application without
Modifying Existing State
ii. Removing State from an Application
iii. Modifying the State of an Operator
f. Queryable State
i. Architecture and Enabling Queryable
State
ii. Exposing Queryable State
iii. Querying State from External
Applications
g. Summary
9. 8. Reading from and Writing to External Systems
a. Application Consistency Guarantees
i. Idempotent Writes
ii. Transactional Writes
b. Provided Connectors
i. Apache Kafka Source Connector
ii. Apache Kafka Sink Connector
iii. Filesystem Source Connector
iv. Filesystem Sink Connector
v. Apache Cassandra Sink Connector
c. Implementing a Custom Source Function
i. Resettable Source Functions
ii. Source Functions, Timestamps, and
Watermarks
d. Implementing a Custom Sink Function
i. Idempotent Sink Connectors
ii. Transactional Sink Connectors
e. Asynchronously Accessing External Systems
f. Summary
10. 9. Setting Up Flink for Streaming Applications
a. Deployment Modes
i. Standalone Cluster
ii. Docker
iii. Apache Hadoop YARN
iv. Kubernetes
b. Highly Available Setups
i. HA Standalone Setup
ii. HA YARN Setup
iii. HA Kubernetes Setup
c. Integration with Hadoop Components
d. Filesystem Configuration
e. System Configuration
i. Java and Classloading
ii. CPU
iii. Main Memory and Network Buffers
iv. Disk Storage
v. Checkpointing and State Backends
vi. Security
f. Summary
11. 10. Operating Flink and Streaming Applications
a. Running and Managing Streaming Applications
i. Savepoints
ii. Managing Applications with the
Command-Line Client
iii. Managing Applications with the REST
API
iv. Bundling and Deploying Applications in
Containers
b. Controlling Task Scheduling
i. Controlling Task Chaining
ii. Defining Slot-Sharing Groups
c. Tuning Checkpointing and Recovery
i. Configuring Checkpointing
ii. Configuring State Backends
iii. Configuring Recovery
d. Monitoring Flink Clusters and Applications
i. Flink Web UI
ii. Metric System
iii. Monitoring Latency
e. Configuring the Logging Behavior
f. Summary
12. 11. Where to Go from Here?
a. The Rest of the Flink Ecosystem
i. The DataSet API for Batch Processing
ii. Table API and SQL for Relational
Analysis
iii. FlinkCEP for Complex Event Processing
and Pattern Matching
iv. Gelly for Graph Processing
b. A Welcoming Community
13. Index

标签： processing Apache flink LIN PAC

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