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

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

Database replication enhances data availability and read performance but has challenges like replication lag.

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

Database replication helps keep data available and allows systems to handle more read requests.

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.

Cache penetration occurs when requests for non-existent data overload the database.

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

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.

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

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