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To learn more about ml-gradle, choose from one of the links below, or browse the index of Wiki pages on the right:

• Getting an error with ml-gradle? Please see the Debugging guide.
• Getting started - this walks you through the basics of setting up a new project
• Project layout - see examples of what a typical ml-gradle project looks like
• Example projects - working examples of some of the features of ml-gradle
• Port reference - list of the MarkLogic server ports commonly used by ml-gradle
• Property reference - list of all properties supported by ml-gradle
• Resource reference - list of all resources supported by ml-gradle
• Task reference - list of all tasks supported by ml-gradle

If you are deploying an ml-gradle application to Progress Data Cloud, please see the Progress Data Cloud Support page for assistance on configuring your project.

ml-gradle Configuration

"Configuration" can refer to several aspects of ml-gradle; the following links describe the different ways that ml-gradle can be configured:

• Configuring ml-gradle describes how the ml-gradle plugin can be configured via Gradle properties and scripting in the Gradle build file.
• Configuring resources describes how to create files that define the different MarkLogic resources for an application, such as databases, app servers, scheduled tasks, etc.
• Configuring security describes the different MarkLogic users that can be used for each of the jobs performed by ml-gradle during a deployment

Loading modules

ml-gradle provides a number of features for loading modules, and there are a number of things to be aware of as well, as described by these pages:

• How modules are loaded goes over the basics of how ml-gradle loads modules
• Bundles describes how you can depend on external bundles that include MarkLogic modules
• Debugging module loading
• Loading modules through a load balancer
• Loading modules via SSL
• Loading modules with static checking
• Watching for module changes

Integration guide

ml-gradle easily integrates with other Java-based MarkLogic tools, as described by the following pages:

• MarkLogic Content Pump (mlcp) and Gradle
• Corb and Gradle
• Data Movement API

Extending ml-gradle

A common way to extend ml-gradle is by creating your own Gradle tasks, but it can be extended and overridden at lower levels as well:

• Dynamically creating tasks describes techniques for reducing duplication across many custom Gradle tasks
• Writing your own task describes the support ml-gradle provides for custom tasks for your project
• Writing your own command describes how to extend and customize the behavior of mlDeploy and mlUndeploy
• Writing a task that talks to a different port describes how to write tasks that talk to a variety of ports in MarkLogic