The following article explains What is Elasticsearch.
To emphasize, a distributed, open-source search and analytics engine called Elasticsearch is made for quick and effective full-text searches as well as sophisticated data analysis. Accordingly, it is a component of the larger Elastic Stack, commonly known as the ELK Stack, which also consists of Kibana and Logstash (for data preprocessing and visualization). Specifically, text-based search, data exploration, and log and event analysis are just a few of the numerous applications that employ Elasticsearch.
Key features and concepts of Elasticsearch
- Elasticsearch excels at offering robust full-text search capabilities that let users look up and retrieve pertinent documents or data using keywords and phrases.
- Elasticsearch is highly scalable and capable of handling enormous datasets because it is designed to be distributed over several nodes. Data is distributed among nodes via a sharding approach, allowing for effective horizontal scaling.
- Elasticsearch’s near real-time search capabilities allow for the immediate availability of freshly indexed documents for search queries.
- Elasticsearch is schemaless, which enables you to index and search data without first defining a strict schema. It is capable of handling many data types and structures.
- Document-Oriented: Elasticsearch stores data in the form of JSON documents. These documents are arranged into indices, and each one represents a single piece of data.
- Elasticsearch has a RESTful API, which makes it simple to communicate with the platform by sending HTTP queries. As a result, documents can be indexed, searched for, updated, and deleted.
- Strong Query Language: Elasticsearch has a powerful query language that enables a wide range of queries, from straightforward keyword searches to intricate boolean queries, filters, and more.
- Elasticsearch’s multi-tenancy feature allows for the separation and organization of data based on various use cases or applications by supporting numerous indices.
- Elasticsearch’s text analysis and tokenization capabilities are strong, enabling stemming, stop word removal, and synonym management for more accurate search results.
- Elasticsearch has aggregation tools for summarizing and doing calculations on data, including determining average values, counts, and statistical statistics.
- Plugins & Extensibility: The architecture of Elasticsearch offers a large variety of plugins for enhancing its features, including as security, monitoring, and tool integration.
Summary
To summarize, applications for Elasticsearch include log and event data analysis, online search engines, e-commerce product search, data exploration and visualization, and more. Basically, it is a flexible option for developing search and analytics platforms. Since, it has huge its capacity for handling sophisticated queries, massive datasets, and distributed architecture, so it is widely used in product search solutions..
Further Reading
Cloud Computing with Amazon Web Service (AWS)
Getting Started Your Journey into Cloud With AWS
How to Work With AWS Management Console?
What are the Important Components of AWS
Understanding Amazon EC2 and How Does it Work
Features and Benefits of Amazon S3 Bucket
Demystifying Serverless Computing from the Ground Up
Different Types of Load Balancers in AWS
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