Word Search Service

Java REST API using the Levenshtein distance algorithm to return the frequency and the list of similarities of a provided word inside a given text.

Key features

• Search for the exact match of a provided word in a notebook entry and return it's frequency.
• Given a word , extract the list of similar words inside a notebook entry based on Levenshtein distance algorithm.

Building

Development environment

• IDE : Eclipse Version: 2021-09 (4.21.0)
• Framework: Spring Boot

Prerequisites

1. Java jdk 1.8 .
2. Apache Maven 4.0.0
3. Internet access
4. Git client

Build

git clone https://github.com/edgardsaad/word-search-service.git
cd word-search-service
mvn install

After the mvn install :

• Run as "Spring Boot App" for development purposes
• Find the built jar under "/word-search-service/target" to be used as production package

Additional POC

An Angular project was developed as a POC for consuming this API. The code of this POC is shared in the below repository :

• https://github.com/edgardsaad/word-search-service-FE.git

git clone https://github.com/edgardsaad/word-search-service-FE.git
cd word-search-service-FE
ng serve

Test Scenarios

Two test scenarios are included in this project under "/word-search-service/src/test/java"

• Scenario 1:

  • Parameters :
    • word :Word
    • text :Word Words Wor word
  • Expected result :
    • Frequency :1
    • Similarities :Words,Wor,word

• Scenario 2:

  • Parameters :
  • word :Word
  • text :Word Word Word word
  • Expected result :
    • Frequency :3
    • Similarities :word

Notes

Summary of work done:

• Time spent : 6.5 hrs

• REST API with Get method was created to take two parameters word : represents the word we are searching for text : represents the notebook entry we are searching in

• Testing class with two test scenarios

• Angular project as a POC

Additional Work to be done

• Update the API to accept the Levenshtein acceptable0 ditance as parameter
• Adding security on both frontend and backend , encrypt the requst and add a bearer to the API header
• Additional test scenarios covering all functionalities
• Further documentation of the code
• User Option to use other text distance algorithms in addition to the Levenshtein algorithm
• Containerize the solution (using Docker)