Elasticsearch is a NoSQL database and search engine designed to handle large-scale data searches efficiently. It is based on Apache Lucene and provides high-speed data retrieval. Unlike traditional relational databases, Elasticsearch is optimized for full-text search and real-time data analytics.

What is Elastic search? Elasticsearch Interview Questions and Answers for Experienced Code Decode

Summary

  • 🔍 Introduction to Elasticsearch – The video introduces Elasticsearch and the ELK stack, covering common interview questions and fundamental concepts.
  • 🏢 Companies Using Elasticsearch – Various companies utilize Elasticsearch for handling unstructured data efficiently.
  • 📌 Why Use Elasticsearch? – Elasticsearch is designed for high-speed searching and indexing, unlike traditional relational databases.
  • 📂 Core Components of Elasticsearch – Index, Documents, and Fields correspond to a database, rows, and columns in relational databases.
  • ⚡ Operations in Elasticsearch – Similar to CRUD operations in relational databases: Index (Create), Fetch (Read), Update, and Delete.
  • 🔀 Sharding and Replication – Data is divided into smaller partitions (shards) for better management and retrieval; replicas provide fault tolerance.
  • 💡 Use Cases – Elasticsearch is widely used for logging and search functionalities, such as autocomplete in e-commerce platforms.
  • 🖥️ Cluster and Node – A node represents an instance of Elasticsearch, while a cluster is a collection of nodes working together.
  • 🔎 Querying Elasticsearch – Utilizes Apache Lucene’s Query DSL (Domain Specific Language) for searching.
  • 🔢 Default Port and Configuration – Default port is 9200, configurable via the elasticsearch.yaml file.
  • 📊 ELK Stack Overview – ELK stack includes:
    • Elasticsearch: Stores and indexes data.
    • Logstash: Aggregates and processes logs from multiple sources.
    • Kibana: Provides a user interface for visualizing Elasticsearch data.
  • 🏗 ELK Stack Architecture – Logs from multiple servers are collected, processed, and stored in Elasticsearch, with Kibana acting as the visualization tool.
  • 🚀 Scalability and Performance – ELK stack ensures high availability and performance monitoring, supporting large-scale log management.

Insights Based on Numbers

  • 📌 9200 – Default port for accessing Elasticsearch, but can be modified in elasticsearch.yaml.
  • 📌 5 shards per index – By default, an index is divided into five shards for efficient data retrieval.
  • 📌 5601 – Default port for Kibana, the UI tool for Elasticsearch visualization.

Example Exploratory Questions

  1. E1: How does Elasticsearch differ from traditional relational databases in terms of performance and data retrieval?
  2. E2: What are some real-world applications where Elasticsearch provides a competitive advantage?
  3. E3: How does the ELK stack work together to streamline log management and search functionalities?

Why Elasticsearch?

  • Speed and Performance: Traditional databases struggle with search performance when handling massive data sets. Elasticsearch, built for search functionalities, provides near-instantaneous results.
  • Scalability: It allows data to be distributed across multiple nodes in a cluster, ensuring high availability.
  • Flexibility: Unlike relational databases, it does not require a fixed schema, making it ideal for handling unstructured data.
  • Open Source: Being open-source, organizations can use it without licensing costs.

Core Concepts of Elasticsearch

  1. Index – Equivalent to a database in relational systems.
  2. Document – Represents a single record or data entry, similar to a row in a traditional table.
  3. Field – Represents attributes of a document, comparable to columns in SQL databases.
  4. Cluster & Nodes – A cluster consists of multiple nodes, each running an instance of Elasticsearch. Nodes work together to store, search, and analyze data efficiently.

CRUD Operations in Elasticsearch

Elasticsearch supports the basic CRUD operations:

  • Create (Indexing a document)
  • Read (Fetching a document)
  • Update (Modifying a document)
  • Delete (Removing a document)

Sharding and Replication

To manage large volumes of data, Elasticsearch employs sharding and replication:

  • Sharding: Data is divided into smaller units (shards) to improve search efficiency and storage.
  • Replication: Copies of shards are created to ensure data redundancy and high availability.

Common Use Cases of Elasticsearch

  1. Logging & Monitoring: Helps in aggregating logs from different sources, allowing real-time monitoring and analysis.
  2. Search and Autocomplete: Used in applications like Amazon, Netflix, and e-commerce platforms to provide instant search suggestions.
  3. Business Intelligence: Companies use Elasticsearch for data visualization, analytics, and decision-making.

The ELK Stack

Elasticsearch is part of the ELK stack, which includes:

  • Elasticsearch: Stores and searches data.
  • Logstash: Processes and transfers data.
  • Kibana: Visualizes data in dashboards and charts.

Querying Elasticsearch

Elasticsearch supports DSL (Domain-Specific Language), based on Apache Lucene’s search functionality, for flexible and powerful queries.

Configuration and Port Settings

  • Default Port: 9200 (for Elasticsearch), 5601 (for Kibana).
  • Configuration File: elasticsearch.yaml can be modified to change port settings and cluster names.

Performance Benefits of Elasticsearch

  • Real-Time Processing: Updates and queries are executed with minimal latency.
  • Distributed Architecture: Spreads data across multiple nodes to improve reliability.
  • Scalability: Can handle increasing data loads by adding more nodes.

Conclusion

Elasticsearch is a powerful search and analytics engine, widely used for its speed, scalability, and flexibility. Its integration with Logstash and Kibana makes it an essential tool for handling large-scale data and real-time monitoring.

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