Spark dependencies
Dependencies allow you to make use of third-party or custom code in your data Pipelines and Jobs. Dependencies can be written in Java, Scala, or PySpark, and can be connected to your data Pipelines by pointing to Maven or PyPI coordinates.
Dependencies can be stored at the Project or Pipeline level. Project-level dependencies are available to all Pipelines in the Project.
We recommend that users store dependencies at the Project level whenever possible to improve the experience of interactive development. This will minimize the time spent connecting to clusters when switching between Pipelines in the IDE.
Storage
Dependencies are natively saved within your build system files. For example, if you're using Scala, dependencies are saved in the pom.xml file. If you're using Python, dependencies are saved in the setup.py file.
View dependencies
There are two ways to view all of your Project dependencies:
- Open your Project from the Metadata page. Then, click on the Dependencies tab.
- Open your Project in the Pipeline Editor. Then, click … > Dependencies from the project header.
You can add, edit, and delete dependencies from these views, too.
Add dependencies
The table below describes the fields available when you select Add Dependency.
| Parameter | Description |
|---|---|
| Scope | The dependency is enabled at the Project level or the Pipeline level. |
| Type | The dependency is either from the Package Hub, Scala (Maven) or Python (PyPi). |
| Name | This will identify the dependency. |
| Version/Package/Coordinates | For Package Hub dependencies, input the package version. For Scala, use the Maven coordinates in the groupId:artifcatId:version format. For example, use org.postgresql:postgresql:42.3.3 For Python, use the package and the version number. |
| Repository (Advanced) | If you'd like to use an external repository (like your organization’s repository), you can specify the link to it in this field. |
| Exclusion (Advanced) | For Scala only. This is an optional list of groupId:artifactId pairs of dependencies you'd like to exclude. Learn more about dependency exclusion here. |
Once you save a new dependency, Prophecy will validate it to make sure the dependency coordinates are valid and accessible. If that fails, you should see an invalid coordinates error.
In rare cases, your dependency might be only accessible to the Spark cluster or the build system, but not to Prophecy itself. If you're confident that the dependency is correct, but the validation error shows up, it's safe to press Save Anyways to ignore that warning.
Update dependencies
To update dependencies, you can do so in the Dependencies section of your Pipeline IDE. The Update label should appear if an update is available. Connecting a Prophecy project to a Spark cluster with a different dependency version will prompt a cluster restart.
Install dependencies on a Spark cluster
When you connect a Pipeline to a cluster, dependencies are automatically installed on that cluster. However, there are two cases that can prevent automatic installation:
- Dependency on the cluster's Spark and Scala versions. A few dependencies depend on your cluster’s Spark and Scala versions. You can usually find these version requirements in the respective repositories. For example, take a look at the Spark Excel page in the Maven Repository.
- Required manual installation on cluster. Additionally, certain dependencies must be installed directly on your cluster. This is documented per dependency.
Build System template
The Build System template in the Settings tab of a Project defines how build files are generated for all pipelines within a project. You can edit the template to include specific items you need during the build process—for example, adding the JaCoCo plugin to enable coverage report generation.
For any help required to enable the template for older Projects, please reach out to the Prophecy support team.
Jobs support
When the Build System template is enabled for a Project and you add a dependency, Prophecy will automatically update your pom.xml or setup.py files to include it.
Though not recommended, if templating is disabled and you still want to add dependencies that are visible to your Pipelines when scheduled, you can manually edit the pom.xml or setup.py files. Below is an example for a Scala Project.
To add the io.github.etspaceman:scalacheck-faker_2.12:7.0.0 dependency, edit the pom.xml like so:
...
<dependencies>
<dependency>
<groupId>io.github.etspaceman</groupId>
<artifactId>scalacheck-faker_2.12</artifactId>
<version>7.0.0</version>
</dependency>
...
</dependencies>
...
Managing dependencies for WHL format deployments
Special consideration for Scala dependencies in Pyspark projects must be given when deploying WHL files outside of PBT.
If you do not create Jobs in the Prophecy editor or use the pbt deploy or pbt deploy-v2 commands, then this
section will help track those Scala dependencies.
When manually deploying Pipelines using WHL format, you need to account for dependencies in both Python and Scala. WHL files inherently record Python dependencies, which ensures Python-related packages are handled during deployment. You can use the following option in Prophecy Build Tool (PBT) to generate and include Scala dependency metadata in your deployment.
Run the PBT command
Run the following PBT command in your project directory to capture Scala dependencies and include them in the WHL package:
pbt build-v2 --add-pom-xml-python --path .
This command will:
- Generate a dummy
pom.xmlandMAVEN_COORDINATEScontaining Maven dependencies. (These files contain the same information in different formats for your convenience.) - Add these files to the WHL package under the directory:
{package_name}-1.0.data/data/
Configuring Spark version
Set the SPARK_VERSION environment variable to specify the Spark version you intend to use in your execution environment.
The version must end with .0. To set the environment variable, run a command like export SPARK_VERSION=3.4.0.
If SPARK_VERSION is not set, PBT will set the Spark version in the Maven coordinate with the placeholder string {{REPLACE_ME}}.