Search Data Service Micro Service

Overview

The Search Data Service acts as an abstraction layer for clients which have a need to interact with data which is most suitably persisted in a searchable document store. The purpose of imposing an abstraction service between the client and the document store itself is to decouple clients from any direct knowledge of any specific document store technology, allowing the underlying technology to be swapped out without a direct impact to any clients which interact with search data.

Please refer to the following sub-sections for more detailed information:

High Level Concepts - Discussion of the high level concepts and building blocks of the Search Data Service.

Index API - Details regarding manipulating document indexes.

Document API - Details regarding manipulating documents.

Search API - Details regarding querying the data set.

Bulk API - Details regarding submitted bulk operation requests.

Getting Started

Building The Micro Service

After cloning the project, execute the following Maven command from the project's top level directory to build the project:

> mvn clean install

Now, you can build your Docker image:

> docker build -t openecomp/search-data-service target

Deploying The Micro Service

Push the Docker image that you have built to your Docker repository and pull it down to the location that you will be running the search service from.

Note that the current version of the Search Data Service uses ElasticSearch as its document store back end. You must therefore deploy an instance of ElasticSearch and make it accessible to the Search Data Service.

Create the following directories on the host machine:

/logs
/opt/app/search-data-service/appconfig

You will be mounting these as data volumes when you start the Docker container.

Populate these directories as follows:

Contents of /opt/app/search-data-service/appconfig

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

analysis-config.json Create this file with exactly the contents below:

[
    {
            "name": "whitespace_analyzer",
            "description": "A standard whitespace analyzer.",
            "behaviours": [
                    "Tokenize the text using white space characters as delimeters.",
                    "Convert all characters to lower case.",
                    "Convert all alphanumeric and symbolic Unicode characters above the first 127 ASCII characters into their ASCII equivalents."
            ],
            "tokenizer": "whitespace",
            "filters": [
                    "lowercase",
                    "asciifolding"
            ]
    },
    {
            "name": "ngram_analyzer",
            "description": "An analyzer which performs ngram filtering on the data stream.",
            "behaviours": [
                    "Tokenize the text using white space characters as delimeters.",
                    "Convert all characters to lower case.",
                    "Convert all alphanumeric and symbolic Unicode characters above the first 127 ASCII characters into their ASCII equivalents.",
                    "Apply ngram filtering using the following values for minimum and maximum size in codepoints of a single n-gram: minimum = 1, maximum = 2."
            ],
            "tokenizer": "whitespace",
            "filters": [
                    "lowercase",
                    "asciifolding",
                    "ngram_filter"
            ]
    }
]

filter-config.json:

Create this file with exactly the contents below:

[
    {
            "name": "ngram_filter",
            "description": "Custom NGram Filter.",
            "configuration": " \"type\": \"nGram\", \"min_gram\": 1, \"max_gram\": 50, \"token_chars\": [ \"letter\", \"digit\", \"punctuation\", \"symbol\" ]"
    }
]

elastic-search.properties

This properties file configures the Search Data Service for communication with ElasticSearch. The contents of this file will be determined by your ElasticSearch deployment:

es.cluster-name=<<name of your ElasticSearch cluster>>
es.ip-address=<<IP address of your ElasticSearch instance>>
es.http-port=9200
# Optional parameters
es.uri-scheme=<<either http or https>>
es.trust-store=<<key store containing the certs of trusted servers>>
es.trust-store-password=<<encrypted passsword to open the trust store>>
es.key-store=<<key store containing the client cert>>
es.key-store-password=<<encrypted passsword to open the client key store>>
es.auth-user=<<username for HTTP Basic Authentication>>
es.auth-password=<<encrypted passsword for HTTP Basic Authentication>>

Contents of the /opt/app/search-data-service/app-config/auth Directory

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

search_policy.json

Create a policy file defining the roles and users that will be allowed to access the Search Data Service. This is a JSON format file which will look something like the following example:

{
    "roles": [
        {
            "name": "admin",
            "functions": [
                {
                    "name": "search", "methods": [ { "name": "GET" },{ "name": "DELETE" }, { "name": "PUT" }, { "name": "POST" } ]
                }
            ],
            "users": [
                {
                    "username": "CN=searchadmin, OU=My Organization Unit, O=, L=Sometown, ST=SomeProvince, C=CA"
                }    
            ]
        }
    ]
}

tomcat_keystore

Create a keystore with this name containing whatever CA certificates that you want your instance of the Search Data Service to accept for HTTPS traffic.

Start the service:

You can now start the Docker container for the Search Data Service, in the following manner:

docker run -d \
    -p 9509:9509 \
    -e CONFIG_HOME=/opt/app/search-data-service/config/ \
    -e KEY_STORE_PASSWORD={{obfuscated password}} \
    -e KEY_MANAGER_PASSWORD=OBF:{{obfuscated password}} \
    -v /logs:/opt/aai/logroot/AAI-SDB \
    -v /opt/app/search-data-service/appconfig:/opt/app/search-data-service/config \
    --name search-data-service \
    {{your docker repo}}/search-data-service

Where,

{{your docker repo}} = The Docker repository you have published your Search Data Service image to.
{{obfuscated password}} = The password for your key store/key manager after running it through the Jetty obfuscation tool.