Introduction to Elasticsearch: Core Concepts, Use Cases, and Practical Operations

Elasticsearch is presented as a powerful search engine, with its basic concepts compared to relational databases: an index resembles a MySQL database, a type a table, a document a row, mapping a schema, and its Query DSL a JSON‑based SQL.

The article outlines typical application scenarios and then details common index operations: creating an index with PUT http://ip:port/索引值, updating its mapping via PUT http://ip:port/索引值/_mapping/vulnerability, viewing the mapping using GET http://ip:port/索引值/_mapping?pretty, and deleting an index with DELETE http://ip:port/索引值. Screenshots illustrate each step in Kibana.

Document management is covered next: adding a document through PUT http://localhost:9200/索引值/vulnerability/文档ID, viewing the inserted data in Kibana, and performing simple searches using the Query DSL (details to be expanded in future articles).

For bulk operations, the author demonstrates using curl commands within shell scripts, including authentication with the --user flag, to insert large volumes of test data.

The deployment guide shows how to run Elasticsearch and Kibana locally with Docker. It references the official Docker installation guide and provides a concise Docker‑Compose workflow: creating a docker-compose.yml file, then starting the services with docker-compose up -d. Images depict the configuration and running containers.

Overall, the article serves as a practical introduction to Elasticsearch, covering its architecture, RESTful API usage, document handling, and containerized deployment.