MarkLogic Geo Data Services

MarkLogic Geo Data Services (GDS) enables a rich set of geospatial query capabilities enables against a MarkLogic database. It is the backend behind the MarkLogic Koop Provider and a future release of the MarkLogic GeoTools Plugin.

Installation

The MarkLogic Geo Data Services capability can be included in a ml-gradle project using a mlBundle configuration. To add the services to your project you will need to update your build.gradle file, configure a schemas database, add the data services configuration, TDE templates, and add any required indexes. The MarkLogic Geo Data Services modules will be deployed to your modules database when you run the mlDeploy gradle task in your project.

Please see the examples directory for sample projects making use of this project with ml-gradle's mlBundle capability.

Update your build.gradle file

buildscript {
  repositories {
    jcenter()
  }
  dependencies {
    classpath "com.marklogic:marklogic-geo-data-services-modules:0.0.4"
  }
}
plugins {
  id "net.saliman.properties" version "1.4.6"
  id "com.marklogic.ml-gradle" version "3.13.0"
}
repositories {
  jcenter()
}

dependencies {
  mlBundle "com.marklogic:marklogic-geo-data-services-modules:0.0.4"
}

Add a schema database to your gradle project

If your database doesn't already use a schemas database, you will need to add one to use GDS. To add one, add the following to your src/main/ml-config/databases/content-database.json

  "schema-database": "%%SCHEMAS_DATABASE%%"

And then create, the following src/main/ml-config/databases/schemas-database.json

{
  "database-name": "%%SCHEMAS_DATABASE%%"
}

Variance Plugin

Geo Data Services relies on a MarkLogic native plugin to perform standard deviation and variance calculations. The Variance plugin will be built using the build system's gcc. The MarkLogic CPU architecture and OS must match the build system for the variance plugin to install successfully.

Note: The native plugin is a C++ program required to support standard deviation and variance aggregation calls for the feature service. gcc is used to build the plugin and then it is installed to MarkLogic via the management API. Because of this the connector build must run on a machine that is the same platform that the MarkLogic cluster is running on. If the build is run on a Windows machine, the build and install of the native plugin will be skipped.

Configure your Geo Data Services

Feature Service Descriptors

GDS uses what it calls a service descriptor to describe the feature services. The service descriptors define the name, description, and other metadata about the services as well what layers are available through that service. Each layer defines the type of features available via the layer as well as the data sources (SPARQL and/or TDE views) that will be used to generate the features.

The service descriptors live in the content database and they must be in the http://marklogic.com/feature-services collection for the connector to be able to find them.

To add service descriptors, place them in the src/main/ml-data directory of your project along with the correct collections.properties and permissions.properties files. They will be loaded into your database and placed into the required collection when you run the ./gradlew mlDeploy task for your project.

See examples/sample-project/src/main/ml-data/example/services for example service descriptors and the required collections.properties and permissions.properties files.

Stay tuned for updates to the wiki for more details about all the configuration options that you can use in the service descriptors.

TDE Templates

GDS can generate feature data from SPARQL queries, TDE views or a combination of both. To use TDE views, follow the MarkLogic TDE documentation and the GKG example provided examples/sample-project/src/main/ml-schemas/tde/example-gkg.tdex to create TDE templates to build views for the data you want to expose as features.

TDE templates should be placed in the src/main/ml-schemas/tde directory of your project. They will be automically installed when you run the ./gradlew mlDeploy task for your project.

OBJECTIDs

The features returned by the Koop provider service with an Esri client should contain a field named OBJECTID or a field that can be identified as the OBJECTID in to the Esri Feature Service clients. The OBJECTID must be an unsigned integer. In order to support pagination across large result sets, the OBJECTIDs need to be increasing numbers. They don't have to be continguous to but should be fairly evenly distributed between the minimum and maximum values.

OBJECTIDs can either be added to the documents and then exposed as a column in a TDE view or computed by an expression in a TDE template column using using existing field(s) in the documents.

For example, you can add the following to each JSON document for a reasonably likely unique id:

koopObjectId: xdmp.hash32(sem.uuidString())

Configuring Time Aware Feature Layers

Time aware feature layers allow users to query specific time periods. ArcGIS supports this using a time slider. More info on configuring time settings in ArcGIS Online, https://doc.arcgis.com/en/arcgis-online/create-maps/configure-time.htm. Time aware layers have additional configuration properties, primarily a start and end date. A sample of the layer configuration is included in src/test/ml-data/feature-services/test/GDeltGKG.json, layer 6. You must have a dateTime property defined in your TDE. The layer configuration will reference this property name.

Limitations (TODO in another release)

• This implementation only works for a start date. The end date configured in the layer and in your TDE will be ignored.
• This implementation assumes that all server side dates are in UTC, there are no time zone conversions. The time zone and daylight savings indicator configured in your layer will be ignored.
• The time extent configured in the layer is ignored

Deploy your application

Once you have added the required configuration to your build file, configured your service descriptors, TDE templates and indexes, use the ./gradlew mlDeploy task in your project to deploy your application with MarkLogic Geo Data Services.

Contributing

Geo Data Services is an open source project and we welcome contributions to improve it. Please submit issues for bugs or enhancement requests and, even better, fork it and submit PRs with changes!

Testing

Use the following steps to install GDS as a standalone project and run the tests:

1. Configure gradle-test.properties for your environment
2. Run ./gradlew -PenvironmentName=test mlDeploy
3. Run ./gradlew -PenvironmentName=test loadTestData
4. Run ./gradlew -PenvironmentName=test test

Testing with MarkLogic Koop Provider

You can make use of the koop-provider-marklogic project to test the MarkLogic Geo Data Services though the MarkLogic Koop Connector. The koop-provider-marklogic project expects this repository to be deployed, the associated test data, and users & roles to be deployed (see: Testing). Once that is complete, configure the gradle-test.properties in koop-provider-marklogic for your environment and run the following in two different command line sessions to begin the tests.

Command Line 1

1. Configure /config/<environment>.json
2. Install npm install
3. Environment Setting export NODE_ENV=<environment>
4. Start Koop node server.js

Command Line 2

1. cd test
2. ../gradlew -PenvironmentName=test test