Spring Cloud Data Flow is no longer maintained as an open-source project by Broadcom, Inc.
For information about extended support or commercial options for Spring Cloud Data Flow, please read the official blog post here.
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 -s .settings.xml 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.
Running Locally w/ Oracle
By default, the Dataflow server jar does not include the Oracle database driver dependency.
If you want to use Oracle for development/testing when running locally, you can specify the local-dev-oracle Maven profile when building.
The following command will include the Oracle driver dependency in the jar:
$ ./mvnw -s .settings.xml clean package -Plocal-dev-oracle
You can follow the steps in the Oracle on Mac ARM64 Wiki to run Oracle XE locally in Docker with Dataflow pointing at it.
NOTE: If you are not running Mac ARM64 just skip the steps related to Homebrew and Colima
Running Locally w/ Microsoft SQL Server
By default, the Dataflow server jar does not include the MSSQL database driver dependency.
If you want to use MSSQL for development/testing when running locally, you can specify the local-dev-mssql Maven profile when building.
The following command will include the MSSQL driver dependency in the jar:
$ ./mvnw -s .settings.xml clean package -Plocal-dev-mssql
You can follow the steps in the MSSQL on Mac ARM64 Wiki to run MSSQL locally in Docker with Dataflow pointing at it.
NOTE: If you are not running Mac ARM64 just skip the steps related to Homebrew and Colima
Running Locally w/ IBM DB2
By default, the Dataflow server jar does not include the DB2 database driver dependency.
If you want to use DB2 for development/testing when running locally, you can specify the local-dev-db2 Maven profile when building.
The following command will include the DB2 driver dependency in the jar:
$ ./mvnw -s .settings.xml clean package -Plocal-dev-db2
You can follow the steps in the DB2 on Mac ARM64 Wiki to run DB2 locally in Docker with Dataflow pointing at it.
NOTE: If you are not running Mac ARM64 just skip the steps related to Homebrew and Colima
Contributing
We welcome contributions! See the CONTRIBUTING guide for details.
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.
