StreamX

Make Flink|Spark easier

A magical framework that makes Flink development easier

🚀 Introduction

The original intention of StreamX is to make the development of Flink easier. StreamX focuses on the management of development phases and tasks. Our ultimate goal is to build a one-stop big data solution integrating stream processing, batch processing, data warehouse and data laker.

🎉 Features

Scaffolding
Out-of-the-box connectors
Support maven compilation
Configuration
Multi version flick support(1.12.x,1.13.x,1.14.x)
on Kubernetes deployment (K8s-Native-Application/K8s-Native-Session)
on YARN deployment (YARN-Application/YARN-Pre-Job)
Support Applicaion and Yarn-Per-Job mode
start, stop, savepoint, resume from savepoint
Flame graph
Notebook
Project configuration and dependency version management
Task backup and rollback
Manage dependencies
UDF
Flink SQL WebIDE
Catalog、Hive
Full support from task development to deployment
...

🏳‍🌈 Components

Streamx consists of three parts,streamx-core,streamx-pump and streamx-console

1️⃣ streamx-core

streamx-core is a framework that focuses on coding, standardizes configuration, and develops in a way that is better than configuration by convention. Also it provides a development-time RunTime Content and a series of Connector out of the box. At the same time, it extends DataStream some methods, and integrates DataStream and Flink sql api to simplify tedious operations, focus on the business itself, and improve development efficiency and development experience.

2️⃣ streamx-pump

streamx-pump is a planned data extraction component, similar to flinkx. Based on the various connector provided in streamx-core, the purpose is to create a convenient, fast, out-of-the-box real-time data extraction and migration component for big data, and it will be integrated into the streamx-console.

3️⃣ streamx-console

streamx-console is a stream processing and Low Code platform, capable of managing Flink tasks, integrating project compilation, deploy, configuration, startup, savepoint, flame graph, Flink SQL, monitoring and many other features. Simplify the daily operation and maintenance of the Flink task.

Our ultimate goal is to build a one-stop big data solution integrating stream processing, batch processing, data warehouse and data laker.

Thanks to the above excellent open source projects and many outstanding open source projects that are not mentioned, for giving the greatest respect, special thanks to Apache Zeppelin , IntelliJ IDEA, Thanks to the fire-spark project for the early inspiration and help.

🚀 Quick Start

git clone https://github.com/streamxhub/streamx.git
cd streamx
./mvnw clean install -DskipTests -Denv=prod

click Document for more information

👻 Why not...❓

Apache Zeppelin

Apache Zeppelin is a Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Java, Scala and more.

At the same time we also need a one-stop tool that can cover development, test, package, deploy, and start. streamx-console solves these pain points very well, positioning is a one-stop stream processing platform, and has developed more exciting features (such as Flink SQL WebIDE, dependency isolation, task rollback , flame diagram etc.)

FlinkX

FlinkX is a distributed offline and real-time data synchronization framework based on flink widely used in DTStack, which realizes efficient data migration between multiple heterogeneous data sources.

StreamX focuses on the management of development phases and tasks. The streamx-pump module is also under planning, dedicated to solving data source migration, and will eventually be integrated into the streamx-console.

🤝 Contributing

You can submit any ideas as pull requests or as GitHub issues.

If you're new to posting issues, we ask that you read How To Ask Questions The Smart Way (This guide does not provide actual support services for this project!), How to Report Bugs Effectively prior to posting. Well written bug reports help us help you!

Thanks to JetBrains for supporting us free open source licenses.