ml-gradle | Gradle plugin for automating everything involving MarkLogic | Plugin library

I'm using ml-gradle plugin to deploy the data and modules to a ML10 database. I have used mlModulePermissions property to set permissions for modules.

Is there a similar property to set permissions for the files loaded from ml-data directory?

MarkLogic Visual Studio Code Extension provides a way to dynamically send the Xquery code to different MarkLogic enviroments by changing mlxps:setting in its header comment block.

However, it does not work if the Xquery will need to refer to other Xquery library files stored on the local drive, as there is no option to set which ML Http server that the Xquery should be evaluated at.

In contrast, if we need to run the same Xquery from QConsole,

Here is the screenshot if I try send it to port 8300 which is the used for that http server form visual studio code.

Any solution for that? Does it mean this mlxprs could only work for Module in Module DB? (This is the same problem for sending Xquery form ml-gradle, as one could not specify which App server the XQuery will be sent to.)

By default, ML HTTP server will use the Module DB inside ML. (It seems all ML training materials refer to that type of configuration.) Any changes in the XQuery programs will need to upload into the Module DB first. That could be accomplished by using mlLoadModules or mlReloadModules ml-gradle commands. CI/CD does not access the ML cluster directly. Everything is via ml-gradle from a machine dedicated from code deployment to different ML enviroments like dev/uat/prod etc.

However it is also possible to configure the ML app server to use the XQuery program from physical disk location like below screenshot. With that configuration, it is not required to reload the programs into ML Module DB. The changes in the program have to be in the ML server itself. CI/CD will need to access to the ML cluster directly. One advantage of this way is that developer will easily see whether the changes in the program have been indeed deployed, as all changes are sitting as physical readable text files in the disk.

Questions:

1. Which way is better? Why?
2. Any ML query perforemance difference between these two different approaches?
3. For the physical file approach, does it mean that CI/CD will need to deploy the program changes to all the ML hosts in the ML cluster? (I guess it is not a concern if HTTP server refers XQuery programs from Module DB inside ML. ML cluster will auto sync the code among different hosts.)

mlDeploy and mlUndeploy enable to rebuild the ML application from CI/CD. Using ml-gradle is the default way to manage new ML app server. That is great.

How to migrate existing ML app to use ml-gradle to manage? How to automatically rebuild the ML gradle project from an existing ML app servers, particaully different indexes? (We have over 50 different types of indexes on one existing ML app server.) It is really slow and easy to make human mistake by recreating the ml-gradle project manually.

Any suggestions to automate that reverse engineering?

I am looking for any sample project to create MarkLogic 3 Nodes Cluster directly from ML Docker Hub image (https://hub.docker.com/_/marklogic) via Gradle on a single machine.

The idea is to auto spin off different ML versions for the dev enviroment set up.

Current three node cluster example in gitbub ml-gradle is to install ML from rpm installation package. I would like to directly use the ML docker hub image instead.

I'm currently working on configuring the resources like databases and app servers for an application using ml-gradle.These configurations would be deployed to ML10 environment.

I have a content database and there are nine hosts in the environment. I have set the ml-gradle property mlContentForestsPerHost=1 ,so it is creating 9 forests in total for the content database.

But I need only 5 forests in total for my content database to be created.

Is there a way to do it via any ml-gradle property or this can be achieved using json forest config only?

Please share your thoughts.

mlPrintForestPlan gives an error when I try to use mlDatabasesWithForestsOnOneHost with replicas in a 3-node cluster.

I need to export a project from a 4.x Data Hub. I can use mlhubExportProject command for a 5.x Data Hub, but am unsure of the equivelent command/process for 4.x Data Hub.

( Can I also confirm which version ml-gradle plugin should be used with a combination of Gradle 5.1.1 , Data Hub 4.1.1 and openJDK 1.8.0_262? )

Thanks in advance.

How can I add public and private keys for a certificate to a certificate template via ml-gradle?

I'm trying to configure some new servers entirely via ml-gradle + DHF 5. I can set up the certificate templates by adding a configuration file to ml-config/security/certificate-templates. I already have an SSL certificate for these servers, but can't figure out how to import them without using the admin web interface.

There is a REST endpoint for performing certificate operations at /manage/v2/certificate-templates/{id|name}, so I guess I just want to invoke that as part of the deployment process in the right order.