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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Dependency failures can disrupt applications, but resilience patterns can help manage them.

Kafka uses replication and in-sync replicas to ensure data is always available.

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

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

Elasticsearch uses shards for data distribution and replicas for redundancy.