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Search engines collect, index, retrieve, and rank documents to provide relevant results.

Redis is a fast in-memory data store used for caching and low-latency applications.

HLD provides an overview of system architecture, while LLD details implementation specifics.

Elasticsearch offers Data Streams for simplicity and Time-Based Indices for control in managing time-series data.

Elasticsearch Data Streams simplify time-series data management with one name for many indices.

Choose Elasticsearch for complex text searches, but use databases for structured queries.

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

Database per Service ensures each microservice owns its data, reducing dependencies.

Caching helps applications respond faster by serving data from memory instead of hitting the database.

Functional requirements define what a system does, while non-functional requirements define how it performs.

Distributed caching spreads data across multiple servers for better performance and reliability.

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

An inverted index helps search engines quickly find documents containing specific terms.

Elasticsearch uses shards for data distribution and replicas for redundancy.

The Bulkhead Pattern isolates resources to protect critical workloads from failures.