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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 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.

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

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.

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

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.

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

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.

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

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.

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.

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

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

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

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

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

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