commit	5209f42102348d19a389017c1717f0edc3733b36	[log] [tgz]
author	Lee, Tian (tl5884) <tianl@amdocs.com>	Fri Nov 09 10:48:40 2018 +0000
committer	Lee, Tian (tl5884) <tianl@amdocs.com>	Fri Nov 09 10:48:40 2018 +0000
tree	f1d14fdad3ebd22e898bbf75c895e84ed4fdaf7c
parent	cb6bc64b417d297c0e98a1b73175b3c36e9938dc [diff]

tree: f1d14fdad3ebd22e898bbf75c895e84ed4fdaf7c

Readme.md

Introduction

The A&AI Model Loader Service is an application that facilitates the distribution and ingestion of new service and resource models and VNF catalogs from the SDC to the A&AI.

Features

The Model Loader:

registers with the SDC to receive notification events
polls the UEB/DMaap cluster for notification events
downloads artifacts from SDC upon receipt of a distribution event
pushes distribution components to A&AI

VNF Catalog loading

The Model Loader supports two methods for supplying VNF Catalog data for loading into A&AI:

Embedded TOSCA image and vendor data
VNF Catalog data can be embedded within the TOSCA yaml files contained in the CSAR.
VNF Catalog XML files
VNF Catalog data in the form of XML files can be supplied in the CSAR under the path Artifacts/Deployment/VNF_CATALOG

Note: Each CSAR should provide VNF Catalog information using only one of the above methods. If a CSAR contains both TOSCA and XML VNF Catalog information, a deploy failure will be logged and published to SDC, and no VNF Catalog data will be loaded into A&AI

Compiling Model Loader

Model Loader can be compiled by running mvn clean install A Model Loader docker image can be created by running docker build -t onap/model-loader target

Running Model Loader

Push the Docker image to your Docker repository. Pull this down to the host machine.

Create the following directories on the host machine:

./logs
./opt/app/model-loader/appconfig
./opt/app/model-loader/appconfig/auth

You will be mounting these as data volumes when you start the Docker container. For examples of the files required in these directories, see the aai/test/config repository (https://gerrit.onap.org/r/#/admin/projects/aai/test-config)

Populate these directories as follows:

Contents of /opt/app/model-loader/appconfig

The following file must be present in this directory on the host machine:

model-loader.properties

# Always false.  TLS Auth currently not supported 
ml.distribution.ACTIVE_SERVER_TLS_AUTH=false

# Address/port of the SDC
ml.distribution.ASDC_ADDRESS=<SDC-Hostname>:8443

# DMaaP consumer group.  
ml.distribution.CONSUMER_GROUP=aai-ml-group

# DMaaP consumer ID
ml.distribution.CONSUMER_ID=aai-ml

# SDC Environment Name.  This must match the environment name configured on the SDC
ml.distribution.ENVIRONMENT_NAME=<Environment Name>

# Currently not used
ml.distribution.KEYSTORE_PASSWORD=

# Currently not used
ml.distribution.KEYSTORE_FILE=

# Obfuscated password to connect to the SDC.  To obtain this value, use the following Jetty library to 
# obfuscate the cleartext password:  http://www.eclipse.org/jetty/documentation/9.4.x/configuring-security-secure-passwords.html
ml.distribution.PASSWORD=OBF:<password>

# How often (in seconds) to poll the DMaaP cluster for new model events
ml.distribution.POLLING_INTERVAL=<integer>

# Timeout value (in seconds) when polling DMaaP for new model events
ml.distribution.POLLING_TIMEOUT=<integer>

# Username to use when connecting to the SDC
ml.distribution.USER=<username>

# Artifact type we want to download from the SDC (the values below will typically suffice)
ml.distribution.ARTIFACT_TYPES=MODEL_QUERY_SPEC,TOSCA_CSAR

# List of message bus addresses on which to listen for distribution events
ml.distribution.MSG_BUS_ADDRESSES=<host1>,<host2>

# URL of the A&AI
ml.aai.BASE_URL=https://<AAI-Hostname>:8443

# A&AI endpoint to post models
ml.aai.MODEL_URL=/aai/v*/service-design-and-creation/models/model/

# A&AI endpoint to post named queries
ml.aai.NAMED_QUERY_URL=/aai/v*/service-design-and-creation/named-queries/named-query/

# A&AI endpoint to post vnf images
ml.aai.VNF_IMAGE_URL=/aai/v*/service-design-and-creation/vnf-images

# Name of certificate to use in connecting to the A&AI
ml.aai.KEYSTORE_FILE=aai-os-cert.p12

# Obfuscated keystore password to connect to the A&AI.  This is only required if using 2-way SSL (not basic auth).
# To obtain this value, use the following Jetty library to obfuscate the cleartext password:
# http://www.eclipse.org/jetty/documentation/9.4.x/configuring-security-secure-passwords.html
ml.aai.KEYSTORE_PASSWORD=OBF:<password>

# Name of user to use when connecting to the A&AI.  This is only required if using basic auth (not 2-way SSL).
ml.aai.AUTH_USER=<username>

# Obfuscated password to connect to the A&AI.  This is only required if using basic auth (not 2-way SSL).
# To obtain this value, use the following Jetty library to obfuscate the cleartext password:
# http://www.eclipse.org/jetty/documentation/9.4.x/configuring-security-secure-passwords.html
ml.aai.AUTH_PASSWORD=OBF:<password>

Contents of the /opt/app/model-loader/app-config/auth Directory

The following files must be present in this directory on the host machine:

aai-os-cert.p12

The certificate used to connected to the A&AI

Start the service:

You can now start the Docker container for the Model Loader Service, e.g:

docker run -d \
	-e CONFIG_HOME=/opt/app/model-loader/config/ \
    -v /logs:/logs \
    -v /opt/app/model-loader/appconfig:/opt/app/model-loader/config \
    --name model-loader \
    {{your docker repo}}/model-loader

where

{{your docker repo}}

is the Docker repository you have published your image to.