#🧱 Building Block: Elasticsearch

#Overview

Elasticsearch is a distributed, RESTful search and analytics engine based on Apache Lucene. It is designed for horizontal scalability, reliability, and real-time search capabilities.

#Key Concepts

• Index: A collection of documents that have somewhat similar characteristics.
• Document: The basic unit of information that can be indexed.
• Shard: A subset of an index. Sharding allows you to split your data across multiple nodes.
• Replica: A copy of a shard. Provides high availability and increases read throughput.

#When to Use

• Full-text Search: When you need to provide powerful search capabilities (ranking, synonyms, typos).
• Log Analytics: Storing and analyzing large volumes of log data (ELK stack).
• Faceting/Aggregations: When you need to provide real-time counts across various dimensions.

#Trade-offs

• Pros: Extremely fast search, scales horizontally, rich query DSL.
• Cons: Near real-time (not instantaneous), complex to manage at scale, high memory usage.

#Design Patterns

• CQRS: Use ES as the read-store while using a relational DB as the write-store.
• Inverted Index: The core data structure that maps terms to their locations in documents.