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

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.

Database isolation levels determine how transactions interact and affect data consistency.

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

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.

Consistent hashing helps distribute data across servers with minimal movement.

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

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

Log replication ensures data consistency by requiring majority acknowledgment before committing changes.

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

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

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

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

Write-Ahead Log ensures data is safely recorded before changes are made.

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

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

Optimistic and pessimistic locking are strategies to manage database access during concurrent transactions.

Database indexing speeds up data retrieval by allowing quick lookups.

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

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

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

Lamport Timestamps help order events in distributed systems while preserving causality.

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.

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

Cache stampede occurs when many requests hit the database due to a cache miss.

Partial failure means some services fail while others keep running, impacting system reliability.

ZooKeeper watches notify clients of changes, avoiding constant polling and reducing server load.

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

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

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

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

ZooKeeper coordinates distributed systems using an ensemble of servers and the ZAB protocol.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

FAANG engineers use high-level design principles to create scalable systems.

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

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

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

Elasticsearch uses master election and quorum to prevent split brain scenarios.

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Consensus ensures that distributed systems can agree on decisions even during failures.

Kafka can help ensure messages are processed once, but requires careful design.