最新章節(jié)

• Summary
• Management tools
• Containerized infrastructure
• State query and data consistency
• State management
• Machine learning integration

• Summary 更新時(shí)間：2021-07-02 22:39:10
• Management tools
• Containerized infrastructure
• State query and data consistency
• State management
• Machine learning integration
• Bridging the gap between data science and engineering
• Better programming API
• Streaming SQL
• Visual development tools
• Lower barrier for building streaming pipelines
• The Future of Stream Processing
• Summary
• Running Apache Beam WordCount on Apache Apex
• Testing the pipeline at small scale with DirectRunner
• Writing to a sharded text file in parallel
• Format your results
• Counting the occurrences of each word
• Eliminating empty strings
• Splitting each line on spaces
• Reading the works of Shakespeare in parallel
• Setting up your pipeline
• WordCount in Apache Beam
• Advanced topic – stateful ParDo
• Triggering in Beam
• Watermarks in Beam
• Windowing in Beam
• Windowing watermarks and triggering in Beam
• GroupByKey/CombinePerKey – aggregation across elements
• ParDo – elementwise computation
• Pipelines PTransforms and PCollections
• Beam concepts
• Introduction to Apache Beam
• Introduction to Apache Beam
• Summary
• Calcite integration
• Understanding application logs
• Application testing
• Partitioning
• The application code
• Application configuration
• Building and running the application
• The application pipeline
• Example Project – ETL Using SQL
• Summary
• Running the application on GCP Dataproc
• Running the application
• Serving the data with WebSocket
• Windowed operator configuration
• Looking up of the zip code and preparing for the windowing operation
• Parsing the data
• Simulation of a real-time feed using historical data
• The pipeline
• Datasource
• The goal
• Example Project – Real-Time Ride Service Data Processing
• Summary
• Setting up the dashboard
• The Grafana Pub/Sub adapter server
• Installing Grafana Simple JSON Datasource
• Installing Grafana
• Grafana visualization
• The Pub/Sub server
• Enabling WebSocket output
• Configuring Twitter API access
• Running the application
• TweetStats
• Top Hashtags
• Analyzing Twitter feed
• The application pattern in a real-world use case
• Streaming ETL and beyond
• Example Project – Real-Time Aggregation and Visualization
• Summary
• The exactly-once output to JDBC
• Example – exactly-once counting
• Processing guarantees
• Incremental recovery
• Incremental state saving
• What to checkpoint
• How to checkpoint
• When to checkpoint
• Checkpointing
• Fault-tolerance components and mechanism in Apex
• Distributed systems need to be resilient
• Fault Tolerance and Reliability
• Summary
• Performance – other aspects for custom operators
• Sample application for dynamic partitioning
• Low-latency versus throughput
• Affinity and anti-affinity
• Performance optimizations
• Custom dynamic partitioning
• Unifier
• StreamCodec
• Configuring and triggering partitioning
• Partitioning toolkit
• Elasticity
• Partitioning and how it works
• Scalability Low Latency and Performance
• Summary
• State Management
• Join
• Dedup
• The windowing example
• Merging of streams
• Triggering
• Allowed lateness
• Watermarks
• State storage
• Accumulation Mode
• Accumulation
• State
• Window propagation
• Session Windows
• Sliding Time Windows
• Time Windows
• Global Window
• Windowing
• Windowed transformations
• Custom functions
• Map transform
• Enrichment
• Filter
• Parser
• Transformations
• Other databases
• JDBC output
• JDBC input
• Databases
• File writer
• File splitter and block reader
• File input
• Files
• Kinesis streams
• JMS (ActiveMQ SQS and so on)
• Other streaming integrations
• Kafka output
• Kafka input
• Apache Kafka
• Integrations
• An overview of the library
• The Apex Library
• Summary
• Dynamically adjusting logging levels
• Logging
• Apex CLI
• YARN web UI
• Working on the cluster
• Running the application
• Installing Apex Docker sandbox
• Execution layer components
• Running the application on YARN
• Writing the integration test
• Testing in the IDE
• Application configuration
• IdleTimeHandler
• ActivationListener
• CheckpointListener/CheckpointNotificationListener
• The Apex operator model
• Custom operator development
• Application specifications
• Creating a new Maven project
• Setting up the development environment
• Development process and methodology
• Getting Started with Application Development
• Summary
• Where Apex is not suitable
• Where Apex excels
• Performance
• Native streaming versus micro-batch
• Low latency and stateful processing
• Value proposition of Apex
• Windowing and time
• JSON
• SQL
• High-level Stream Java API
• Apex DAG Java API
• Directed Acyclic Graph (DAG)
• Application Model and API
• Silver Spring Networks (SSN)
• Real-time threat detection (Capital One)
• Industrial IoT applications (GE)
• Real-time insights for Advertising Tech (PubMatic)
• Use cases and case studies
• What is Apex and why is it important?
• Stream processing systems
• Stream processing
• Unbounded data and continuous processing
• Introduction to Apex
• Questions
• Piracy
• Errata
• Downloading the color images of this book
• Downloading the example code
• Customer support
• Reader feedback
• Conventions
• Who this book is for
• What you need for this book
• What this book covers
• Preface
• Customer Feedback
• Why subscribe?
• www.PacktPub.com
• About the Reviewer
• About the Authors
• Credits
• 版權(quán)信息
• 封面

• 封面
• 版權(quán)信息
• Credits
• About the Authors
• About the Reviewer
• www.PacktPub.com
• Why subscribe?
• Customer Feedback
• Preface
• What this book covers
• What you need for this book
• Who this book is for
• Conventions
• Reader feedback
• Customer support
• Downloading the example code
• Downloading the color images of this book
• Errata
• Piracy
• Questions
• Introduction to Apex
• Unbounded data and continuous processing
• Stream processing
• Stream processing systems
• What is Apex and why is it important?
• Use cases and case studies
• Real-time insights for Advertising Tech (PubMatic)
• Industrial IoT applications (GE)
• Real-time threat detection (Capital One)
• Silver Spring Networks (SSN)
• Application Model and API
• Directed Acyclic Graph (DAG)
• Apex DAG Java API
• High-level Stream Java API
• SQL
• JSON
• Windowing and time
• Value proposition of Apex
• Low latency and stateful processing
• Native streaming versus micro-batch
• Performance
• Where Apex excels
• Where Apex is not suitable
• Summary
• Getting Started with Application Development
• Development process and methodology
• Setting up the development environment
• Creating a new Maven project
• Application specifications
• Custom operator development
• The Apex operator model
• CheckpointListener/CheckpointNotificationListener
• ActivationListener
• IdleTimeHandler
• Application configuration
• Testing in the IDE
• Writing the integration test
• Running the application on YARN
• Execution layer components
• Installing Apex Docker sandbox
• Running the application
• Working on the cluster
• YARN web UI
• Apex CLI
• Logging
• Dynamically adjusting logging levels
• Summary
• The Apex Library
• An overview of the library
• Integrations
• Apache Kafka
• Kafka input
• Kafka output
• Other streaming integrations
• JMS (ActiveMQ SQS and so on)
• Kinesis streams
• Files
• File input
• File splitter and block reader
• File writer
• Databases
• JDBC input
• JDBC output
• Other databases
• Transformations
• Parser
• Filter
• Enrichment
• Map transform
• Custom functions
• Windowed transformations
• Windowing
• Global Window
• Time Windows
• Sliding Time Windows
• Session Windows
• Window propagation
• State
• Accumulation
• Accumulation Mode
• State storage
• Watermarks
• Allowed lateness
• Triggering
• Merging of streams
• The windowing example
• Dedup
• Join
• State Management
• Summary
• Scalability Low Latency and Performance
• Partitioning and how it works
• Elasticity
• Partitioning toolkit
• Configuring and triggering partitioning
• StreamCodec
• Unifier
• Custom dynamic partitioning
• Performance optimizations
• Affinity and anti-affinity
• Low-latency versus throughput
• Sample application for dynamic partitioning
• Performance – other aspects for custom operators
• Summary
• Fault Tolerance and Reliability
• Distributed systems need to be resilient
• Fault-tolerance components and mechanism in Apex
• Checkpointing
• When to checkpoint
• How to checkpoint
• What to checkpoint
• Incremental state saving
• Incremental recovery
• Processing guarantees
• Example – exactly-once counting
• The exactly-once output to JDBC
• Summary
• Example Project – Real-Time Aggregation and Visualization
• Streaming ETL and beyond
• The application pattern in a real-world use case
• Analyzing Twitter feed
• Top Hashtags
• TweetStats
• Running the application
• Configuring Twitter API access
• Enabling WebSocket output
• The Pub/Sub server
• Grafana visualization
• Installing Grafana
• Installing Grafana Simple JSON Datasource
• The Grafana Pub/Sub adapter server
• Setting up the dashboard
• Summary
• Example Project – Real-Time Ride Service Data Processing
• The goal
• Datasource
• The pipeline
• Simulation of a real-time feed using historical data
• Parsing the data
• Looking up of the zip code and preparing for the windowing operation
• Windowed operator configuration
• Serving the data with WebSocket
• Running the application
• Running the application on GCP Dataproc
• Summary
• Example Project – ETL Using SQL
• The application pipeline
• Building and running the application
• Application configuration
• The application code
• Partitioning
• Application testing
• Understanding application logs
• Calcite integration
• Summary
• Introduction to Apache Beam
• Introduction to Apache Beam
• Beam concepts
• Pipelines PTransforms and PCollections
• ParDo – elementwise computation
• GroupByKey/CombinePerKey – aggregation across elements
• Windowing watermarks and triggering in Beam
• Windowing in Beam
• Watermarks in Beam
• Triggering in Beam
• Advanced topic – stateful ParDo
• WordCount in Apache Beam
• Setting up your pipeline
• Reading the works of Shakespeare in parallel
• Splitting each line on spaces
• Eliminating empty strings
• Counting the occurrences of each word
• Format your results
• Writing to a sharded text file in parallel
• Testing the pipeline at small scale with DirectRunner
• Running Apache Beam WordCount on Apache Apex
• Summary
• The Future of Stream Processing
• Lower barrier for building streaming pipelines
• Visual development tools
• Streaming SQL
• Better programming API
• Bridging the gap between data science and engineering
• Machine learning integration
• State management
• State query and data consistency
• Containerized infrastructure
• Management tools
• Summary 更新時(shí)間：2021-07-02 22:39:10