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Availability is about access; reliability is about correct performance.

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

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

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

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

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

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

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

Keep-Alive allows reusing HTTP connections to improve efficiency and reduce overhead.

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

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

Graceful degradation allows apps to function partially during service failures.

Fail Fast means stopping quickly on errors, while Fail Safe ensures safety in failures.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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

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

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

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

Alerting helps notify the right people about production issues quickly.

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

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

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

Load shedding helps systems reject excess requests to maintain performance during high demand.

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

Elasticsearch aliases allow zero-downtime switching between indices for applications.

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

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

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

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

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

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

A CDN speeds up web applications by caching content closer to users.

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

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

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

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

You can update Elasticsearch indices without downtime using alias switching.

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