The Eclipse Dataspace Connector provides a framework for sovereign, inter-organizational data exchange. It will implement the International Data Spaces standard (IDS) as well as relevant protocols associated with GAIA-X. The connector is designed in an extensible way in order to support alternative protocols and integrate in various ecosystems.
Please also refer to:
- The Eclipse Project Homepage
- International Data Spaces
- The GAIA-X project
- The Onboarding Guide
Note: items marked with [TBW] indicate that the respective documentation is yet to be written
One of the guiding principles in developing the connector is simplicity and keeping the core small and efficient with as little external dependencies as possible to avoid version conflicts. We do not want to force any third-party dependencies onto our users, so we aim to avoid any of the big frameworks. Of course, if you want to use them, you still can add them to your extensions (see: [TBW]). The connector is a plain Java application built with Gradle, but it can be embedded into any form of application deployment.
For detailed information about the project, please have a look at our Documentation.
The project requires JDK 11+. To get started:
git clone [email protected]:eclipse-dataspaceconnector/DataSpaceConnector.git
cd DataSpaceConnector
./gradlew clean buildThat will build the connector and run unit tests.
If you wish to configure your IDE/editor to automatically apply the EDC code style, please follow this guide.
Note: the style guide will be checked/enforced in GitHub Actions.
Connectors can be started using the concept of "launchers", which are essentially compositions of Java modules defined
as gradle build files. There is a basic launcher, which launches a simple connector that has no cloud-based extensions
whatsoever.
In a shell run
./gradlew :launchers:basic:shadowJar
java -jar launchers/basic/build/libs/dataspaceconnector-basic.jarOnce it says "Dataspace Connector ready" the connector is up and running.
More information about the extension concept can be found here [TBW].
More information about shadowJar can be found here.
Please refer to this document.
This is the primary extension point for the connector. It contains all necessary interfaces that need to be implemented
as well as essential model classes and enums. Basically, the spi modules defines the extent to what users can
customize and extend the code.
Contains all absolutely essential building that is necessary to run a connector such as TransferProcessManager,
ProvisionManager, DataFlowManager, various model classes, the protocol engine and the policy piece. While it is
possible to build a connector with just the code from the core module, it will have very limited capabilities to
communicate and to interact with a data space.
This contains code that extends the connector's core functionality with technology- or cloud-provider-specific code. For example a transfer process store based on Azure CosmosDB, a secure vault based on Azure KeyVault, etc. This is where technology- and cloud-specific implementations should go.
If someone were to create a configuration service based on Postgres, then the implementation should go into
the extensions/database/configuration-postgres module.
Launchers are essentially connector packages that are runnable. What modules get included in the build (and thus: what
capabilities a connector has) is defined by the build.gradle.kts file inside the launcher subdirectory. That's also
where a Java class containing a main method should go. We will call that class a "runtime" and in order for the
connector to become operational the runtime needs to perform several important tasks (="bootstrapping"). For an
example take a look at
this runtime
Contains a Helm chart for the EDC runtime. You can use the launchers/generic/Dockerfile to build a runtime image for
your connector runtime, and deploy the resulting image to Kubernetes.
Contains implementations for communication protocols a connector might use, such as IDS.
Contains code that demonstrates how the connector can be used in various scenarios. For example, it shows how to run a connector from a unit test in order to try out functionality quickly or how to implement an outward-facing REST API for a connector.
Contains utility code such as collection utils, string utils and helper classes.
Contains several scripts to deploy a connector in an AKS cluster on Microsoft Azure using Terraform.
Please refer to the dedicated style guide and the patterns we documented in architecture principles.
See here for more information about project and milestone planning. Scheduled and ongoing milestones are listed here.
See how to contribute.
Available tags can be found here.