SpringBootElasticSearchPOC

Integration of elastic search with spring boot

D:\programs\ElasticStack\elasticsearch-8.12.2\bin>elasticsearch

Get Request: http://localhost:8080/apis/allProducts

POST Request : http://localhost:8080/apis/insertProduct

{ "id": 3, "name": "AppleMotorola", "description": "Mobile phone", "quantity": 1, "price": 90000.0 }

GET Request: http://localhost:9200/products/_search

==> Give all products from elastic search

Use cases of elastic search with Mongo DB

Combining Elasticsearch with MongoDB can be very powerful, leveraging the strengths of both systems. Here are some common use cases:

1. Enhanced Search Capabilities: MongoDB can store large volumes of data, but Elasticsearch excels at full-text search and real-time data analysis. By integrating Elasticsearch with MongoDB, you can create powerful search functionalities for your application. For example, an e-commerce platform can use MongoDB to store product data and Elasticsearch to power the search functionality, allowing users to search for products by name, description, or other attributes.

2. Log and Event Data Analysis: MongoDB can be used to store log data, while Elasticsearch can be used to analyze and visualize this data in real-time. This combination is often used in monitoring and alerting systems, where logs are collected, stored in MongoDB, and then indexed and analyzed by Elasticsearch for quick querying and visualization using tools like Kibana.

3. Real-Time Analytics: For applications that require real-time analytics on large datasets, MongoDB can store the raw data, and Elasticsearch can be used to perform complex queries and aggregations on this data. This setup is useful for dashboards that display real-time metrics and trends.

4. Content Personalization: Elasticsearch can analyze user behavior and preferences stored in MongoDB to provide personalized content recommendations. This is commonly used in media streaming services, e-commerce platforms, and social networks to enhance user experience by suggesting relevant content.

5. Geospatial Data Analysis: MongoDB supports geospatial queries, and Elasticsearch can enhance these capabilities by providing advanced search and analysis features. This combination is useful for applications that need to perform location-based searches and visualizations, such as mapping services and location-based marketing.

6. Data Enrichment and Transformation: MongoDB can store raw data, and Elasticsearch can be used to enrich and transform this data for better analysis. For example, you can use Elasticsearch to add metadata, perform text analysis, or aggregate data from multiple sources before visualizing it.

Example: E-Commerce Platform: In an e-commerce platform, MongoDB can store product information, customer data, and order history. Elasticsearch can be used to power the search functionality, allowing users to search for products by name, category, price, and other attributes. Additionally, Elasticsearch can analyze user behavior to provide personalized product recommendations.

By combining MongoDB and Elasticsearch, you can create a robust system that leverages the strengths of both databases for efficient data storage, powerful search capabilities, and real-time analytics.

Would you like more detailed examples or guidance on setting up this integration?