Introduction to Elasticsearch
Elasticsearch is an open source distributed search and analytics engine built on top of Apache Lucene. It provides a fast, scalable and distributed search solution with powerful query capabilities. Elasticsearch is widely used in various scenarios, including enterprise search, log and event data analysis, e-commerce product search and recommendation, etc.
The core concepts of Elasticsearch are documents and indexes. A document is the smallest data unit stored in Elasticsearch, which can be structured JSON data. An index, on the other hand, is a collection of documents, each of which has a unique identifier. Indexes allow you to quickly search, filter, and aggregate data.
Advantages and application scenarios of Elasticsearch
Elasticsearch has many advantages that make it one of the most popular search engines today. Here are some major advantages:
-
Distributed and scalable: Elasticsearch can easily scale horizontally, distributing data across multiple nodes for high performance and high availability. Its distributed architecture makes it easy to handle large-scale data.
-
Full-text search: Elasticsearch provides a powerful full-text search function, supporting advanced query, word segmentation and fuzzy search. It uses inverted index technology to quickly locate matching documents, making searches efficient and accurate.
-
Real-time data analysis: Elasticsearch supports real-time data analysis and aggregation operations, and can handle large amounts of log data, index data, and time series data. By using Elasticsearch's aggregation capabilities, you can quickly generate statistical results and data visualizations.
-
Multiple data types and flexibility: Elasticsearch supports multiple data typesÿ