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

Availability is about access; reliability is about correct performance.

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

Idempotency ensures that repeated operations in APIs do not cause duplicate effects.

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

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

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

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.

Kafka rebalance can lead to lost or duplicated work if not handled carefully.

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

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

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

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

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

Transactions ensure database operations are reliable using ACID properties.

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

Message delivery semantics define how messages are sent in distributed systems and handle failures.

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

Replicas keep your system available, while snapshots allow for data recovery.

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

The dual-write trap in Elasticsearch can cause data inconsistency during reindexing.

Primary shards partition data, while replica shards duplicate it for redundancy.

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

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

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

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

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

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

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

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

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

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

Kafka's static membership allows consumers to maintain stable identities, reducing unnecessary rebalances.

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

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.

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

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

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

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

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

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

Raft's safety rules guarantee that committed entries are preserved even after leader failures.

Kafka uses retention to keep historical data and compaction to keep the latest state.

Kafka producers use ACKs, retries, and idempotence to ensure reliable message delivery.

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

The RED Method helps monitor microservices using Rate, Errors, and Duration metrics.

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

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

The retry pattern helps systems recover from temporary failures but can cause overload if misused.

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

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

The USE Method helps find and analyze infrastructure bottlenecks in software systems.

Kafka allows safe message reprocessing by resetting consumer positions without altering data.