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Argo-Quatro

A proof-of-concept for providing Argo search functionality using Elasticsearch and without Blacklight.

Search

Configuration

The primary configuration for search is the Search model. In addition to containing attributes for the query, page, and page size, it contains an attribute for each facet.

A facet attribute is a type Type::Facet. For example:

  attribute :object_type, Type::Facet.new(
    label: 'Object Type'), default: []

Facets are arrays.

A facet can be configured with an aggregation proc. The aggregation proc generates the Elasticsearch Aggregation DSL for the facet. This DSL is used to retrieve the facet values and counts. For example:

    agg_proc: ElasticsearchAggregations::Tags

The default aggregration proc is ElasticsearchAggregations::Terms, which generates a terms aggregation.

A facet can be configured with an aggregation response proc. The aggregation response proc parses the aggregation response from Elasticsearch to get the facet values and counts. For example:

    agg_response_proc: ElasticsearchAggregationResponses::Date,

The default aggregation response proc is ElasticsearchAggregationResponses::Value.

A facet can be configured with a filter proc. The filter proc generates the Query DSL for the facet. This DSL limits the query and facet results for the selected facet values. For example:

    filter_proc: ElasticsearchFilters::ReleasedToSearchworksDate

The default filter proc is ElasticsearchFilters::Terms.

A facet can be configured with a component. The component is used to render the facet. For example:

    component: HierarchicalFacetComponent

The default component is FacetComponent.

Search process

Searching is initiated by calling the SearchService with a populated Search model. This will invoke a SearchJob and one or more FacetSearchJobs.

The Search Job will perform will query Elasticsearch for documents and some aggregations. (These should be fast aggregations only; slow aggregations should be performed by a separate Facet Search Job.) The documents and the aggregations will be Turbo broadcasted.

A Facet Search Job will be performed for each facet that is configured with multi: false. For example:

  attribute :project_tag, Type::Facet.new(
    label: 'Project Tag', 
    multi: false,
    agg_proc: ElasticsearchAggregations::Tags,
    component: HierarchicalFacetComponent), default: []

Facet Search Job should be performed for slow facets (as they require a separate Elasticsearch query). The job will Turbo broadcast the aggregation.

Display

Result documents and facets are all asynchronously rendered by Turbo streams; broadcasts are performed by the search jobs.

For both results lists and item details pages, items are represented by ItemPresenters rather than directly by Elasticsearch source documents or Cocina items.

Adding a facet

  1. Add index field configuration to app/services/create_index.rb. (Note: Currently, the index must be deleted and recreated to change the index configuration. In the future, the index field configuration could just be updated.)
  2. Add permitted parameter to app/controllers/searches_controller.rb.
  3. Add facet configuration to app/models/search.rb.
  4. Optionally, create an aggregation proc, aggregation proc, filter proc, and/or a facet component.

Running locally

docker compose up
docker cp argo4-es01-1:/usr/share/elasticsearch/config/certs/ca/ca.crt tmp/.
bin/dev

Note that Kibana will be running at http://localhost:5601

Indexing

Currently, the index is generated from Solr documents.

bin/rake "index:reindex[100000]"

To tunnel to production Solr:

ssh -L 8983:sul-solr-prod-a.stanford.edu:80 lyberadmin@argo-prod-02.stanford.edu