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Availability is about access; reliability is about correct performance.

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

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

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

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

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

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

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

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

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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.

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

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

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

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

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

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

Alerting helps notify the right people about production issues quickly.

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

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

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

Classifying failures and using controlled retries in Kafka prevents outages.

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

Retry storms occur when too many retries overload a failing service.

A Circuit Breaker helps prevent system failures by stopping calls to unhealthy services.

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

Exponential backoff helps manage retries by increasing wait times to reduce system overload.

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

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