Elasticsearch

Elasticsearch is used as a search engine to query the questionnaires.

Important

The indices in Elasticsearch only contain public Questionnaires ( with status = active). If pending questionnaires (eg. the pending changes of the current user) are to be displayed, use a database query instead of Elasticsearch.

Administration

There is an administration panel available for superusers. It allows to create and update indices. The panel can be accessed in the user menu or directly via /search/admin/.

Structure

The different indices in QCAT are structured as follows:

/[index]/[type]/[id]

• index: [prefix][questionnaire_type] (examples: qcat_technologies, qcat_approaches).

  • The prefix can be set in the settings.

  • The index is actually an alias pointing to an index in behind (for example qcat_technologies_1). This allows to update the mapping of an index without any downtime (see also https://www.elastic.co/blog/changing-mapping-with-zero-downtime).

• type: questionnaire. This is currently always set to questionnaire.

• id: identifier. The identifier of the questionnaire.

Document format

A document in elasticsearch contains the following entries (in _source):

{
  "data": {
    # A copy of the data dictionary as stored in the JSONB-Field of
    # questionnaire in the database.
  },
  "list_data": {
    # A dictionary containing the entries used for the list
    # representation (as specified in the questionnaire configuration)
  },
  "created": "[timestamp]",
  "updated": "[timestamp]",
  "code": "[code of the questionnaire]",
  "name": {
    # A dictionary containing the translations of the name field (as
    # specified in the questionnaire configuration)
  },
  "configurations": [
    # A list of the configurations in which this questionnaire is
    # available
  ],
  "translations": [
    # A list of the translations in which this questionnaire is
    # available
  ]
}

Example

Example of a document stored in Elasticsearch:

{
  "_index": "qcat_test_technologies_1",
  "_type": "questionnaire",
  "_id": "101",
  "_score": 1,
  "_source": {
     "translations": [
        "fr",
        "en"
     ],
     "data": {
        "tech_qg_2": [
           {
              "tech_definition": {
                 "fr": "Ceci est la déscription 1 en français.",
                 "en": "This is the definition of the first WOCAT practice."
              }
           }
        ],
        "qg_name": [
           {
              "name": {
                 "fr": "WOCAT 1 en français",
                 "en": "WOCAT practice 1"
              }
           }
        ]
     },
     "list_data": {
        "name": {
           "fr": "WOCAT 1 en français",
           "en": "WOCAT practice 1"
        },
        "tech_definition": {
           "fr": "Ceci est la déscription 1 en français.",
           "en": "This is the definition of the first WOCAT practice."
        }
     },
     "code": "tech_1",
     "updated": "2015-02-10T16:07:20.847000+00:00",
     "name": {
        "en": "Unknown name"
     },
     "created": "2015-02-10T16:07:20.847000+00:00",
     "configurations": [
        "technologies"
     ]
  }
}

Helper functions

The helper functions for Elasticsearch are inside the Django app search.

• search.search.advanced_search(): The main function used to query questionnaires based on their configurations, codes, name and other attributes.