Flink 1.7 文档翻译活动期待大家的参与 | ApacheCN
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章节列表
- Apache Flink Documentation
- Dataflow Programming Model
- Distributed Runtime Environment
- DataStream API Tutorial
- Local Setup Tutorial
- Running Flink on Windows
- Examples
- Batch Examples
- Project Template for Java
- Project Template for Scala
- Configuring Dependencies, Connectors, Libraries
- Basic API Concepts
- Scala API Extensions
- Java Lambda Expressions
- Flink DataStream API Programming Guide
- Event Time
- Generating Timestamps / Watermarks
- Pre-defined Timestamp Extractors / Watermark Emitters
- State & Fault Tolerance
- Working with State
- The Broadcast State Pattern
- Checkpointing
- Queryable State Beta
- State Backends
- State Schema Evolution
- Custom Serialization for Managed State
- Operators
- Windows
- Joining
- Process Function (Low-level Operations)
- Asynchronous I/O for External Data Access
- Streaming Connectors
- Fault Tolerance Guarantees of Data Sources and Sinks
- Apache Kafka Connector
- Apache Cassandra Connector
- Amazon AWS Kinesis Streams Connector
- Elasticsearch Connector
- HDFS Connector
- Streaming File Sink
- RabbitMQ Connector
- Apache NiFi Connector
- Twitter Connector
- Side Outputs
- Python Programming Guide (Streaming) Beta
- Testing
- Experimental Features
- Flink DataSet API Programming Guide
- DataSet Transformations
- Fault Tolerance
- Iterations
- Zipping Elements in a DataSet
- Connectors
- Python Programming Guide Beta
- Hadoop Compatibility Beta
- Local Execution
- Cluster Execution
- Table API & SQL
- Concepts & Common API
- Streaming Concepts
- Dynamic Tables
- Time Attributes
- Joins in Continuous Queries
- Temporal Tables
- Detecting Patterns in Tables Beta
- Query Configuration
- Connect to External Systems
- Table API
- SQL
- Built-In Functions
- User-defined Sources & Sinks
- User-defined Functions
- SQL Client Beta
- Data Types & Serialization
- Register a custom serializer for your Flink program
- Execution Configuration
- Program Packaging and Distributed Execution
- Parallel Execution
- Execution Plans
- Restart Strategies
- FlinkCEP - Complex event processing for Flink
- Storm Compatibility Beta
- Gelly: Flink Graph API
- Graph API
- Iterative Graph Processing
- Library Methods
- Graph Algorithms
- Graph Generators
- Bipartite Graph
- FlinkML - Machine Learning for Flink
- Quickstart Guide
- Alternating Least Squares
- How to Contribute
- Cross Validation
- Distance Metrics
- k-Nearest Neighbors Join
- MinMax Scaler
- Multiple Linear Regression
- Looking under the hood of pipelines
- Polynomial Features
- Stochastic Outlier Selection
- Standard Scaler
- SVM using CoCoA
- Best Practices
- API Migration Guides
- Standalone Cluster
- YARN Setup
- Mesos Setup
- Docker Setup
- Kubernetes Setup
- Amazon Web Services (AWS)
- Google Compute Engine Setup
- MapR Setup
- Hadoop Integration
- JobManager High Availability (HA)
- Checkpoints
- Savepoints
- State Backends
- Tuning Checkpoints and Large State
- Configuration
- Production Readiness Checklist
- Command-Line Interface
- Scala REPL
- Kerberos Authentication Setup and Configuration
- SSL Setup
- File Systems
- Upgrading Applications and Flink Versions
- Metrics
- How to use logging
- History Server
- Monitoring Checkpointing
- Monitoring Back Pressure
- Monitoring REST API
- Debugging Windows & Event Time
- Debugging Classloading
- Application Profiling
- Importing Flink into an IDE
- Building Flink from Source
- Component Stack
- Data Streaming Fault Tolerance
- Jobs and Scheduling
- Task Lifecycle
- File Systems
流程
一、认领
首先查看整体进度,确认没有人认领了你想认领的章节。
然后回复 ISSUE,注明“章节 + QQ 号”(一定要留 QQ)。
二、翻译
可以合理利用翻译引擎(例如谷歌),但一定要把它变得可读!
如果遇到格式问题,请随手把它改正。
三、提交
fork
Github 项目- 将译文放在
docs
文件夹下 push
pull request
请见 Github 入门指南。
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