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

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

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

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

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

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.

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

Connection pooling reuses database connections but can cause issues if misconfigured.

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

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

Availability is about access; reliability is about correct performance.

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

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

Choosing the right distributed lock depends on your system's needs and existing tools.

Cache invalidation is about knowing when to refresh cached data.