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

FAANG engineers use high-level design principles to create scalable systems.

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.

Elasticsearch uses distributed architecture to search data quickly across multiple servers.

Stateless servers are easier to scale because they don't store user session data locally.

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.

API pagination helps manage large datasets by breaking them into smaller parts.

Elasticsearch uses shards for data distribution and replicas for redundancy.

Consistency models determine what data reads in distributed systems can see.

In distributed systems, timestamps don't always reflect the true order of events.

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