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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.

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

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.

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

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

Synchronous replication waits for confirmation from replicas, while asynchronous allows faster writes without waiting.

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

Availability is about access; reliability is about correct performance.

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

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

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

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.