What is Elastic Search and Why is It Used

Elasticsearch is an open–source, distributed search and analytics engine designed for handling large volumes of data with near real-time search capabilities. Part of the Elastic Stack, it stores data in JSON format, supports multi-tenancy, and offers powerful full-text search functionalities.

In this article, We will learn about What is Elasticsearch, the Features for Elasticsearch, the Need of Elasticsearch and so on in detail.

• Elasticsearch is an open-source and distributed search and analytics engine built based on the Apache Lucene.
• It is designed to handle large volumes of data and provide near real-time search capabilities across various types of structured and unstructured data.
• Elasticsearch is part of the Elastic Stack, which includes other tools like Kibana for data visualization, Beats for data shipping, and Logstash for data processing.
• Elasticsearch stores data in JSON format and making it easy to index and search structured and unstructured data.
• Elasticsearch supports the concept of multi-tenancy, allowing us to index and search data for multiple applications or users within a single cluster.
  
• Elasticsearch is developed and supported by Elastic NV, a company that provides commercial products and services around the Elastic Stack.

• Distributed and Scalable: Elasticsearch is distributed by nature, allowing it to scale horizontally across multiple nodes to handle large datasets and high query volumes.
• Full-Text Search: It provides powerful full-text search capabilities, enabling users to search for documents based on their content and relevance.
• Real-Time Data Analysis: Elasticsearch supports real-time indexing and querying, making it suitable for use cases that require up-to-date insights from continuously changing data.
• RESTful API: Elasticsearch exposes a RESTful API, making it easy to interact with the system using simple HTTP requests.
• Schemaless: Elasticsearch is schemaless, meaning we can index and search data without having to define a rigid schema already.

1. Text Search and Analysis

• Elasticsearch good at full-text search, making it an ideal choice for applications that require robust search functionality. Whether it’s searching through blog posts, product catalogs, or log files, Elasticsearch can quickly retrieve relevant documents based on user queries.
• Example: Suppose we have a website with a large collection of articles. With Elasticsearch, users can search for articles containing specific keywords, and the system will return relevant results ranked by relevance.

2. Log and Event Data Analysis

• In the realm of DevOps and system monitoring, Elasticsearch is widely used for log and event data analysis. By indexing logs in Elasticsearch, organizations can perform real-time analysis, detect anomalies, and troubleshoot issues efficiently.
• Example: Imagine a server infrastructure generating log files continuously. By ingesting these logs into Elasticsearch, administrators can search for specific error messages, track system performance metrics, and visualize trends over time.

3. Business Intelligence and Analytics

• Elasticsearch can serve as a backend for business intelligence (BI) and analytics applications, enabling organizations to derive actionable insights from their data. By indexing and aggregating data in Elasticsearch, analysts can perform complex queries, generate reports, and create visualizations to support decision-making processes.
• Example: A retail company can use Elasticsearch to analyze customer purchase patterns, identify popular products, and predict future sales trends based on historical data.

4. Geographic Data and Spatial Search

• Elasticsearch also supports spatial search capabilities, making it suitable for applications that deal with geographic data. It can index and search geospatial data such as locations, coordinates, and polygons, enabling users to perform spatial queries and proximity searches.
• Example: A travel booking platform can use Elasticsearch to search for hotels within a certain distance from a user’s location, filter results based on amenities, and provide interactive maps for visual exploration.

At its core, Elasticsearch operates as a distributed system consisting of one or more nodes, each responsible for storing and indexing data. The system uses a decentralized architecture to ensure high availability, fault tolerance, and scalability.

1. Indexing and Querying

• Indexing: Data is ingested into Elasticsearch through the indexing process. During indexing, documents are analyzed, tokenized, and stored in inverted indexes, enabling fast and efficient search operations.
• Querying: Users interact with Elasticsearch through queries, which can be simple keyword searches or complex aggregations. Elasticsearch employs a query DSL (Domain-Specific Language) to express various types of queries, ranging from basic full-text searches to advanced aggregations and filters.

2. Sharding and Replication

• Elasticsearch uses sharding to distribute data across multiple nodes in a cluster, improving performance and scalability. Each shard is a self-contained index fragment, allowing Elasticsearch to parallelize search and indexing operations.
• Additionally, Elasticsearch employs replication to ensure data redundancy and fault tolerance. Each shard can have one or more replicas, which serve as backups in case of node failures or data loss.

3. Distributed Search and Aggregation

When executing search queries or aggregations, Elasticsearch coordinates with all nodes in the cluster to fetch relevant data. It employs distributed search and aggregation strategies to parallelize computation and merge results from multiple shards.

Example:

Suppose we have an Elasticsearch cluster indexing log data from multiple servers. A simple search query might return the following results:

{
  "took": 5,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": {
      "value": 100,
      "relation": "eq"
    },
    "max_score": 1,
    "hits": [
      {
        "_index": "logs-2022.04.01",
        "_type": "_doc",
        "_id": "1",
        "_score": 1,
        "_source": {
          "timestamp": "2022-04-01T12:00:00",
          "message": "Error: Connection timed out"
        }
      },
      {
        "_index": "logs-2022.04.01",
        "_type": "_doc",
        "_id": "2",
        "_score": 1,
        "_source": {
          "timestamp": "2022-04-01T12:05:00",
          "message": "Warning: Disk space low"
        }
      },
      // More log entries...
    ]
  }
}

This output includes metadata about the query execution (took, _shards, etc.) and the matched documents (hits). Each document contains its index, type, ID, score, and source data.

Overall, Elasticsearch is a good and powerful search and analytics engine that offers real-time indexing, search, and analysis capabilities for a wide range of use cases. By leveraging its distributed architecture, full-text search capabilities, and real-time analytics features, organizations can gain valuable insights from their data, improve operational efficiency, and deliver better user experiences.