Overview

The Global Search module provides advanced search capabilities based on Lucene Search for your Mendix app. More specifically, this module allows you to search through multiple entities and attributes with just one query.

- Advanced lucene search: search by using the Lucene Query language.

- Simple and advanced indexing: Simple indexing indexes the mendix entity. Advanced indexing used a json structure to index the entity. This way, you can fully customize what you want to index for each searchable object, including searching over association for example.

- Making entities searchable by creating specializations of GlobalSearch.SearchableObject. No more runtime configuration needed, everything can be set-up during development.

Documentation

Description

The Global Search module provides advanced search capabilities based on Lucene Search for your Mendix app. More specifically, this module allows you to search through multiple entities and attributes with just one query.

The module provides an extended Lucene search query, which allows you to search through text in several ways. Below you find an example of what you can expect when using the search. In this example, the string we want to return as a search result is “This is a search module”, due to the extended query implemented in this module, you can search the following, which will all return the mentioned string:

- Exact word match: e.g. “This”, “search”, “module”.

- Phrase match: e.g. “This is a”, “a search module”.

- Wildcard match: e.g. “sear”, “modul”

- Wildcard Phrase match: e.g. “his is a”, “a search mod”

Furthermore, you can also use the default Lucene query syntax to do a fielded search. For more details, see https://www.lucenetutorial.com/lucene-query-syntax.html.

Typical usage scenario

The Global Search module can be used when searching over multiple objects at once is required.

Features and limitations

Features:

• Faceted searching
• Fuzzy searching
• Fielded searching
• Simple indexing based on entity definition in the domain model
• Advanced indexing based on json structure

Dependencies

• Mendix 9.24.28 or higher
• Community Commons

Configuration

Indexing

To make entities searchable, follow these steps:

0. Add the ASU_GlobalSearch to your after startup microflow.

1. Copy SUB_IndexObject and SUB_DeleteIndex into your own project.

2. Change ACO_SearchableObject and BDE_SearchableObject to use the copies you made in step 1.

3. Let each entity that you want to be searchable inherit from GlobalSearch.SearchableObject. After this, change the two SUB microflows from step 1 to accomodate for these specializations.

For indexing, you can use a simple index. This will index all attributes for an entity with the names you have in the domain model. Here, there is no support for searching over association. You can also index objects based on a JSON string. To do this, create an export mapping for the entity you want to index and add this json to the java action. Only the key/value pairs specified in the json will be indexed. This does support indexing over association, as you can include associated objects/list of objects in the json. For both methods, see the example implementation in SUB_IndexObject. Finally, during indexing, you can also add a list of Facets. These are key value pairs that are stored for that particular object, which can be used for filtering the search results later on.

Searching

The search java action returns a list of SearchableObjects that match the query. In the example snippet, a list view with specialization templates is used in order to display each specialization differently. Of course, your implementation can differ, but this might give a good example. For reference, you can refer to ACT_Searcher_Search.

Index data management

IT IS IMPORTANT TO READ THIS BEFORE DEPLOYING THIS MODULE TO PRODUCTION

Index data to zip

The newest version (5.0.0) of this module contains functionality to pull lucene index data from the TMP folder on the Mendix cloud container in which an app runs (if hosted in the MX cloud). Between deployments data hosted on the container is destroyed due to the inner workings of the Mendix cloud (ephemeral storage). The IndexDataToFileDocument Java action takes all data in the Lucene searchIndex folder and puts it in a Zip file. This file is stored in the IndexZipData entity in the database. This functionality can be accessed from the new Index data to zip tab in the snippet SNIP_Search_View OR it runs in the BSd_StoreIndexData microflow. It is recommended to use this functionality in the Before Shutdown flow of your application to make sure all index data is captured. Set the IndexFileStoreBeforeShutdown constant to True.

Zipped data to the Lucene searchIndex folder

Due to feedback about startup behaviour of apps with this module it is now possible to circumvent the default After startup flow. Set the IndexFileRestoreAfterStartup constant to True. The first time you use this nothing will happen since there is no IndexZipData object available, it is recommended to do a manual sync/rebuild of the index. See below for a how to.

Manual data management

To (re)build the Index data manually use the functionality on the Test Search tab. Click the button ‘Build index’ to trigger a manual rebuild. This can take some time but it will not cause a failed startup flow. It is also possible to use manually clear the current indexed data, but use this functionality with utmost care!

Limitations

The IndexDataToFileDocument stores *all* index data in one IndexZipData filedocument. The limit of a binary attribute on PostgresQL is about 1GB. If your index is more than one gigabyte consider other options for file storage for your app like AWS S3.