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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
Looking to migrate a Roxy project to ml-gradle? Please see the migration guide.
Need to use ml-gradle, but you're in an environment where you can't use Gradle to download ml-gradle and its dependencies? See Offline support.
"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
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
ml-gradle easily integrates with other Java-based MarkLogic tools, as described by the following pages:
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