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The choice between SQL and NoSQL databases depends on your application's specific needs.

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

Choosing between a shared database and a database per service affects system design significantly.

Sharding splits databases into smaller parts to improve performance and scalability.

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

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

Distributed locks ensure only one server performs a task, avoiding duplication.

Transactions ensure database operations are reliable using ACID properties.

B-Trees help databases find data quickly without scanning every row.

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

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

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

Failover ensures systems remain operational by managing leader changes during failures.

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

Read replicas allow databases to handle more read requests by distributing them across multiple copies.

Read-after-write consistency ensures users see their latest updates immediately.

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.

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

Message queues allow services to communicate asynchronously, improving scalability and reliability.

Network partitions occur when servers are operational but can't communicate, affecting system performance.

2-Phase Commit helps maintain data consistency in transactions across multiple databases.

Quorum Reads ensure data consistency by querying multiple replicas in distributed systems.

Distributed backpressure helps manage the flow of work between services to prevent overload.

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

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

Load balancers distribute traffic across multiple servers to enhance performance and reliability.

Fallbacks allow systems to handle failures safely and maintain user experience.

Crash failures stop a service, while network failures disrupt communication.

The CAP Theorem explains the trade-offs in distributed systems during network failures.

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

The Saga Pattern manages distributed transactions using local transactions and compensating actions.

A timeout indicates a deadline was exceeded, not necessarily a failure.

Leader election helps distributed systems select one coordinator from many nodes.

Cascading failures occur when one service's failure impacts others, causing widespread issues.

Timeouts in distributed systems indicate suspicion, not confirmed failure.