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Spring Cloud Data Flow is a microservices-based toolkit for building streaming and batch data processing pipelines in Cloud Foundry and Kubernetes.

Data processing pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks.

This makes Spring Cloud Data Flow ideal for a range of data processing use cases, from import/export to event streaming and predictive analytics.

Components

Architecture: The Spring Cloud Data Flow Server is a Spring Boot application that provides RESTful API and REST clients (Shell, Dashboard, Java DSL). A single Spring Cloud Data Flow installation can support orchestrating the deployment of streams and tasks to Local, Cloud Foundry, and Kubernetes.

Familiarize yourself with the Spring Cloud Data Flow architecture and feature capabilities.

Deployer SPI: A Service Provider Interface (SPI) is defined in the Spring Cloud Deployer project. The Deployer SPI provides an abstraction layer for deploying the apps for a given streaming or batch data pipeline and managing the application lifecycle.

Spring Cloud Deployer Implementations:

• Local
• Cloud Foundry
• Kubernetes

Domain Model: The Spring Cloud Data Flow domain module includes the concept of a stream that is a composition of Spring Cloud Stream applications in a linear data pipeline from a source to a sink, optionally including processor application(s) in between. The domain also includes the concept of a task, which may be any process that does not run indefinitely, including Spring Batch jobs.

Application Registry: The App Registry maintains the metadata of the catalog of reusable applications. For example, if relying on Maven coordinates, an application URI would be of the format: maven://<groupId>:<artifactId>:<version>.

Shell/CLI: The Shell connects to the Spring Cloud Data Flow Server's REST API and supports a DSL that simplifies the process of defining a stream or task and managing its lifecycle.

Building

Clone the repo and type

$ ./mvnw clean install 

Looking for more information? Follow this link.

Building on Windows

When using Git on Windows to check out the project, it is important to handle line-endings correctly during checkouts. By default Git will change the line-endings during checkout to CRLF. This is, however, not desired for Spring Cloud Data Flow as this may lead to test failures under Windows.

Therefore, please ensure that you set Git property core.autocrlf to false, e.g. using: $ git config core.autocrlf false. For more information please refer to the Git documentation, Formatting and Whitespace.

Contributing

We welcome contributions! Follow this link for more information on how to contribute.

Code formatting guidelines

• The directory ./src/eclipse has two files for use with code formatting, eclipse-code-formatter.xml for the majority of the code formatting rules and eclipse.importorder to order the import statements.

• In eclipse you import these files by navigating Windows -> Preferences and then the menu items Preferences > Java > Code Style > Formatter and Preferences > Java > Code Style > Organize Imports respectfully.

• In IntelliJ, install the plugin Eclipse Code Formatter. You can find it by searching the "Browse Repositories" under the plugin option within IntelliJ (Once installed you will need to reboot Intellij for it to take effect). Then navigate to Intellij IDEA > Preferences and select the Eclipse Code Formatter. Select the eclipse-code-formatter.xml file for the field Eclipse Java Formatter config file and the file eclipse.importorder for the field Import order. Enable the Eclipse code formatter by clicking Use the Eclipse code formatter then click the OK button. ** NOTE: If you configure the Eclipse Code Formatter from File > Other Settings > Default Settings it will set this policy across all of your Intellij projects.

License

Spring Cloud Data Flow is Open Source software released under the Apache 2.0 license.