Liferay stores its information in a database, so why not search the database directly? Why add the complexity of a search engine? First, because database table merges are expensive! Documents in a search index often contain searchable fields from multiple tables in the database. Traversing the date in this way takes too long.
In addition to the performance problem, search engines provide access to additional features, like relevance and scoring. By contrast, databases do not support features like fuzzy searching or relevancy. Moreover, search engines can apply algorithms such as “More Like This” to return similar content. Search engines also support geolocation, faceting of search results, and multi-lingual searching.
This section contains information on extending Liferay’s search functionality, enabling your custom entities to be indexed and searched in Liferay DXP, and configuring the developer-friendly embedded Elasticsearch server to suit your needs. First, some basic search concepts.
Indexing: During indexing, a document is sent to the search engine. This document contains a collection of fields of various types (string, etc.). The search engine processes each field within the document. For each field, the search engine determines whether it needs to simply store the field or if it needs to undertake special analysis (index time analysis). Index time analysis can be configured for each field (see Mapping Definitions).
For fields requiring analysis, the search engine first tokenizes the value to obtain individual words or tokens. Following tokenization, the search engine passes each token through a series of analyzers. Analyzers perform different functions. Some remove common words or stop words (e.g., “the”, “and”, “or”) while others perform operations like lowercasing all characters.
Searching: Searching involves sending a search query and obtaining results (a.k.a. hits) from the search engine. The search query may be comprised of both queries and filters (more on this later). Each query or filter specifies a field to search within and the value to match against. Upon receiving the search query, the search engine iterates through each field within the nested queries and filters. During this process, the engine may perform special analysis prior to executing the query (search time analysis). Search time analysis can be configured for each field (see Mapping Definitions).
Search engines are semi-intelligent, automatically deciphering how to process documents passed to them. However, there are instances where it’s desirable to configure explicitly how to process a field.
Mappings control how a search engine processes a given field. For instance, if a field name ends in “es_ES”, we want to process the field values as Spanish, removing any common Spanish words like “si”.
In Elasticsearch and Solr, the two supported search engines for Liferay Portal,
mappings are defined in
The Elasticsearch mapping JSON file can be seen in the Liferay DXP
schema.xml can be seen in the
portal-search-solr7 module’s source code:
Access the Solr 7 module’s source code from the
These are default mapping files that are shipped with the product. You can further customize these mappings to fit your needs. For example, you might want to use a special analyzer for a custom inventory number field.
Search engines already provide native APIs. Why does Liferay provide search infrastructure to wrap the search engine? Liferay’s search infrastructure does several things:
Index documents with the fields Liferay needs (
groupId, staging status, etc.).
Apply the right filters to search queries (e.g., for scoping results).
Apply permission checking on the results.
Summarizing returned results.
That’s just a taste of Liferay’s Search Infrastructure. Continue reading to learn more.