1 # Search Data Service Micro Service
3 [![alt text](https://bestpractices.coreinfrastructure.org/projects/1737/badge)](https://bestpractices.coreinfrastructure.org/projects/1737)
6 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.
8 Please refer to the following sub-sections for more detailed information:
10 [High Level Concepts](./CONCEPTS.md) - Discussion of the high level concepts and building blocks of the _Search Data Service_.
12 [Index API](./INDEXES.md) - Details regarding manipulating document indexes.
14 [Document API](./DOCUMENTS.md) - Details regarding manipulating documents.
16 [Search API](./SEARCH.md) - Details regarding querying the data set.
18 [Bulk API](./BULK.md) - Details regarding submitted bulk operation requests.
23 ### Building The Micro Service
25 After cloning the project, execute the following Maven command from the project's top level directory to build the project:
29 Now, you can build your Docker image:
31 > docker build -t openecomp/search-data-service target
33 ### Deploying The Micro Service
35 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.
37 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_.
39 **Create the following directories on the host machine:**
42 /opt/app/search-data-service/appconfig
44 You will be mounting these as data volumes when you start the Docker container.
46 **Populate these directories as follows:**
48 ##### Contents of /opt/app/search-data-service/appconfig
50 The following files must be present in this directory on the host machine:
52 _analysis-config.json_
53 Create this file with exactly the contents below:
58 "name": "whitespace_analyzer",
59 "description": "A standard whitespace analyzer.",
61 "Tokenize the text using white space characters as delimeters.",
62 "Convert all characters to lower case.",
63 "Convert all alphanumeric and symbolic Unicode characters above the first 127 ASCII characters into their ASCII equivalents."
65 "tokenizer": "whitespace",
72 "name": "ngram_analyzer",
73 "description": "An analyzer which performs ngram filtering on the data stream.",
75 "Tokenize the text using white space characters as delimeters.",
76 "Convert all characters to lower case.",
77 "Convert all alphanumeric and symbolic Unicode characters above the first 127 ASCII characters into their ASCII equivalents.",
78 "Apply ngram filtering using the following values for minimum and maximum size in codepoints of a single n-gram: minimum = 1, maximum = 2."
80 "tokenizer": "whitespace",
91 Create this file with exactly the contents below:
95 "name": "ngram_filter",
96 "description": "Custom NGram Filter.",
97 "configuration": " \"type\": \"nGram\", \"min_gram\": 1, \"max_gram\": 50, \"token_chars\": [ \"letter\", \"digit\", \"punctuation\", \"symbol\" ]"
101 _elastic-search.properties_
103 This properties file configures the _Search Data Service_ for communication with ElasticSearch.
104 The contents of this file will be determined by your ElasticSearch deployment:
106 es.cluster-name=<<name of your ElasticSearch cluster>>
107 es.ip-address=<<IP address of your ElasticSearch instance>>
109 # Optional parameters
110 es.uri-scheme=<<either http or https>>
111 es.trust-store=<<key store containing the certs of trusted servers>>
112 es.trust-store-password=<<encrypted passsword to open the trust store>>
113 es.key-store=<<key store containing the client cert>>
114 es.key-store-password=<<encrypted passsword to open the client key store>>
115 es.auth-user=<<username for HTTP Basic Authentication>>
116 es.auth-password=<<encrypted passsword for HTTP Basic Authentication>>
119 ##### Contents of the /opt/app/search-data-service/app-config/auth Directory
121 The following files must be present in this directory on the host machine:
123 _search\_policy.json_
125 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:
133 "name": "search", "methods": [ { "name": "GET" },{ "name": "DELETE" }, { "name": "PUT" }, { "name": "POST" } ]
138 "username": "CN=searchadmin, OU=My Organization Unit, O=, L=Sometown, ST=SomeProvince, C=CA"
147 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.
149 **Start the service:**
151 You can now start the Docker container for the _Search Data Service_, in the following manner:
155 -e CONFIG_HOME=/opt/app/search-data-service/config/ \
156 -e KEY_STORE_PASSWORD={{obfuscated password}} \
157 -e KEY_MANAGER_PASSWORD=OBF:{{obfuscated password}} \
158 -v /logs:/opt/aai/logroot/AAI-SDB \
159 -v /opt/app/search-data-service/appconfig:/opt/app/search-data-service/config \
160 --name search-data-service \
161 {{your docker repo}}/search-data-service
165 {{your docker repo}} = The Docker repository you have published your Search Data Service image to.
166 {{obfuscated password}} = The password for your key store/key manager after running it through the Jetty obfuscation tool.