May 30, 2023 08:56 by
Peter
Description of ElasticSearch
ElasticSearch is a scalable, open-source search and analytics engine developed on top of Apache Lucene. It is designed to manage vast amounts of data and provide distributed search capabilities that are both rapid and distributed. ElasticSearch is frequently used for a variety of purposes, such as full-text search, log analytics, real-time analytics, and data visualization.
Principal Elements of ElasticSearch
- ElasticSearch is designed to distribute data across multiple nodes, allowing for horizontal scalability and high availability. It employs sharding and replication techniques to distribute and replicate data throughout the cluster.
- Full-Text Search: ElasticSearch provides robust full-text search capabilities, enabling complex queries across structured and unstructured data. It allows for filtering, faceting, highlighting, and relevance scoring.
- ElasticSearch is designed to manage the ingestion and analysis of real-time data. It enables near-real-time indexing and searching of data, making it suitable for applications that require current information and prompt responses.
- ElasticSearch is schema-less, meaning documents can be indexed and searched without a predefined schema. It dynamically maps and indexes data based on its structure, providing flexibility and simplifying the process of indexing data.
- ElasticSearch provides a comprehensive RESTful API that enables interaction with the search engine via HTTP requests. This facilitates ElasticSearch's integration with numerous applications and programming languages.
- ElasticSearch provides robust aggregation capabilities for executing data analytics and generating insightful insights. Aggregations permit the grouping, filtering, and calculation of data sets, facilitating the extraction of valuable information from indexed documents.
- ElasticSearch integrates seamlessly with other Elastic Stack components, such as Logstash for data ingestion, Kibana for data visualization and dashboarding, and Beats for lightweight data shipment. This ecosystem offers a comprehensive data management and analysis solution.
Enterprise search, e-commerce, content management, log analysis, cybersecurity, and other industries utilize ElasticSearch extensively. Its scalability, adaptability, and robust search capabilities make it a popular option for applications requiring the efficient and rapid retrieval of structured and unstructured data.
Here is an example of conducting CRUD operations for managing products using ElasticSearch and the.NET Core API:
Install NEST (Elasticsearch.Net & NEST) NuGet packages.
Specify Product Model:
using Nest;
public class Product
{
[Keyword]
public string Id { get; set; }
[Text]
public string Name { get; set; }
[Number]
public decimal Price { get; set; }
// Add additional properties as needed
}
Configure ElasticSearch Connection:
using Nest;
public class ElasticSearchConfig
{
private readonly string _elasticSearchUrl = "http://localhost:9200";
private readonly string _indexName = "products"; // Name of your index
public ElasticClient GetClient()
{
var settings = new ConnectionSettings(new Uri(_elasticSearchUrl))
.DefaultIndex(_indexName);
return new ElasticClient(settings);
}
}
Create the Product Service:
using Nest;
public class ProductService
{
private readonly ElasticClient _elasticClient;
public ProductService(ElasticClient elasticClient)
{
_elasticClient = elasticClient;
}
public async Task<bool> AddProduct(Product product)
{
var indexResponse = await _elasticClient.IndexDocumentAsync(product);
return indexResponse.IsValid;
}
public async Task<Product> GetProduct(string id)
{
var searchResponse = await _elasticClient.GetAsync<Product>(id);
return searchResponse.Source;
}
public async Task<bool> UpdateProduct(Product product)
{
var updateResponse = await _elasticClient.UpdateAsync<Product>(product.Id, u => u.Doc(product));
return updateResponse.IsValid;
}
public async Task<bool> DeleteProduct(string id)
{
var deleteResponse = await _elasticClient.DeleteAsync<Product>(id);
return deleteResponse.IsValid;
}
}
Create the Product Controller:
using Microsoft.AspNetCore.Mvc;
[ApiController]
[Route("api/products")]
public class ProductController : ControllerBase
{
private readonly ProductService _productService;
public ProductController(ProductService productService)
{
_productService = productService;
}
[HttpPost]
public async Task<IActionResult> Create(Product product)
{
var success = await _productService.AddProduct(product);
if (success)
{
return Ok();
}
return BadRequest("Failed to create product.");
}
[HttpGet("{id}")]
public async Task<IActionResult> Get(string id)
{
var product = await _productService.GetProduct(id);
if (product != null)
{
return Ok(product);
}
return NotFound();
}
[HttpPut]
public async Task<IActionResult> Update(Product product)
{
var success = await _productService.UpdateProduct(product);
if (success)
{
return Ok();
}
return BadRequest("Failed to update product.");
}
[HttpDelete("{id}")]
public async Task<IActionResult> Delete(string id)
{
var success = await _productService.DeleteProduct(id);
if (success)
{
return Ok();
}
return BadRequest("Failed to delete product.");
}
}
Register Dependencies in Startup.cs:
using Nest;
public class Startup
{
// ...
public void ConfigureServices(IServiceCollection services)
{
// ...
// Add ElasticSearch configuration and services
services.AddSingleton<ElasticSearchConfig>();
services.AddScoped<ElasticClient>(serviceProvider =>
{
var config = serviceProvider.GetRequiredService<ElasticSearchConfig>();
return config.GetClient();
});
// Add ProductService
services.AddScoped<ProductService>();
// ...
}
// ...
}
With these code examples, you can create a .NET Core API for CRUD operations on products using ElasticSearch as the data store.