mvn -e -B archetype:generate -D archetypeGroupId=com.adobe.granite.archetypes -D archetypeArtifactId=aem-project-archetype -D archetypeVersion=23 -D aemVersion=cloud -D appTitle="My Site" -D appId="mysite" -D groupId="com.mysite" -D frontendModule=general -D includeExamples=n -DappsFolderName=mysi

This is the know issue with Adobe Maven Arch Type 23 - refer https://github.com/adobe/aem-project-archetype for details.

The issue can be fixed by running the Maven command as a Elevated user or Run as Administrator.

AEM(Adobe Experience Manager) as a Cloud Service — Setting up Local Development Environment in Windows

Introduction

• Is the cloud-native way of leveraging the AEM applications.
• Provides Continuous Delivery and Continuous Integration for updates with zero downtime.
• Is based on a dynamic architecture that autoscales, thus removing infrastructure considerations.
• Is security focused, using automated tests to scan for common vulnerabilities.
• Validates customer code using automated tests.
• Has performance topologies optimized for maximum resilience and efficiency.
• Significantly reduces the manual configuration required.
• Is deeply integrated with the Adobe Experience Cloud.

Local Dev environment structure

Prerequisites

• Maven
• Java
• AEM SDK.
• Docker Desktop

Download the required software's

Setup AEM Publisher

Setup WKND Website

Setup AEM Dispatcher

Create a new AEM Project for AEM Cloud

Conclusion

This tutorial explains how to enable search synonyms in AEM with Lucene.

Search Synonyms

Synonyms are used to inform the search engine that searching for one word should also search for others e.g searching for gigabyte should also consider gigabytes, gib and gb etc

The analyzer should be configured for custom oak index to support the search synonyms.

Refer the following tutorial to configure custom oak index and analyzers

https://www.albinsblog.com/2020/04/oak-lucene-index-improve-query-in-aem-configure-lucene-index.html#.Xu7oD2hKjb1

https://www.albinsblog.com/2020/05/how-to-enable-case-insensitive-search-in-aem-lucene.html#.Xu7nAWhKjb1

I have a data node with id property value as "gigabyte", the node will be returned while searching with the value "gigabyte" but not returned while searching with "gigabytes", "gib" or "gb".

Configure Analyzer

Add the following properties to synonyms node

format - solr or wordnet
synonyms - synonyms.txt, file with synonyms definitions

There are two possible formats for the dictionary, I am using solr format for the demo

• wordnet: based on the popular Wordnet community. This required the synonyms configuration in specific format
• solr: it’s more plain text. Comma separated values 

synonyms.txt

GB,gib,gigabyte,gigabytes
MB,mib,megabyte,megabytes
Television, Televisions, TV, TVs

The configurations are ready now, let us re-index the data. Change the value of reindex property under the custom index to true - this will initiate the re-indexing, the property value will be changed to false once the re-indexing is initiated


Wait for few minutes to the index to complete

Let us now search with "gib" - there is no node with value "gib" for id property under /content/sampledata but "gib" is configured as a synonym for "gigabyte" so the node with id property value "gigabyte" will be returned as a result.



Synonyms Configuration - https://github.com/techforum-repo/youttubedata/tree/master/lucene

Define a custom index with synonyms and configure all the possible synonyms to the synonyms.txt file. The configured data can be used to search the data with different synonyms.

This tutorial explains how to enable case insensitive search in AEM with Lucene.

I have two nodes with property "id" under /content/sampledata with same value in different case e.g TEST and test



By default, the Lucene search is case sensitive so the query will return only the matching node - the node matching with value "test"


The case insensitive can be enabled through analyzers in Lucene index

Refer the following URL to configure the custom Lucene index - https://www.albinsblog.com/2020/04/oak-lucene-index-improve-query-in-aem-configure-lucene-index.html#.Xu7oD2hKjb1

Analysis, in Lucene, is the process of converting field text into its most fundamental indexed representation, terms. These terms are used to determine what documents match a query during searching.

An analyzer tokenizes text by performing any number of operations on it, which could include extracting words, discarding punctuation, removing accents from characters, lowercasing (also called normalizing), removing common words, reducing words to a root form (stemming), or changing words into the basic form (lemmatization).

In Lucene, an analyzer is a java class that implements a specific analysis. Analysis occurs any time text needs to be converted into terms, which in Lucene’s core is at two spots: during indexing and when searching.

An analyzer chain starts with a Tokenizer, to produce initial tokens from the characters read from a Reader, then modifies the tokens with any number of chained TokenFilters.

Let’s see the some important built-in analyzer available in Lucene bundle:

WhitespaceAnalyzer, as the name implies, splits text into tokens on whitespace characters and makes no other effort to normalize the tokens. It doesn’t lowercase each token. 

SimpleAnalyzer first splits tokens at nonletter characters, then lowercases each token. Be careful! This analyzer quietly discards numeric characters but keeps all other characters. 

StopAnalyzer is the same as SimpleAnalyzer, except it removes common words. By default, it removes common words specific to the English language (the, a, etc.), though you can pass in your own set.

KeywordAnalyzer treats entire text as a single token. 

StandardAnalyzer is Lucene’s most sophisticated core analyzer. It has quite a bit of logic to identify certain kinds of tokens, such as company names, email addresses, and hostnames. It also lowercases each token and removes stop words and punctuation. 

The built-in analyzers can be used directly by specifying the class name or the analyzers can be composed with tokenizer and series of filters – there are multiple Tokenizers e.g Standard, Keyword, CharTokenizers and Token Filters e.g Stop, LowerCase,Standard 

org.apache.lucene.analysis.standard.StandardAnalyzer 

org.apache.lucene.analysis.core.SimpleAnalyzer

There are multiple standard analyzers, tokenizers and Token Filters, custom analyzers/Tokenizer/TokenFilters can be created if required. 

The analyzers is configured via the analyzers node (of type nt:unstructured) inside the oak:index definition. 

The default analyzer for an index is configured in the default child of the analyzer’s node(of type nt:unstructured). 

Create a child node with name "tokenizer" of type "nt:untstrutured" under default node and add the property "name" with value "Standard"

Create a child node with name "filters" of type "nt:untstrutured" under default node.

Create a child node with name "LowerCase" of type "nt:untstrutured" 

This will index the data by lower casing and also match data by lower casing during the search.

The configurations are ready now, let us index the data. Change the value of reindex property under the custom index to true - this will initiate the re-indexing, the property value will be changed to false once the re-indexing is initiated

Let me now re-execute the query, this time the query returns both nodes with uppercase and lowercase values(TEST and test)

Sample Configuration - https://github.com/techforum-repo/youttubedata/tree/master/lucene