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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.

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

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

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.

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

Graceful degradation allows apps to function partially during service failures.

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

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

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

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.

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

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

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

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

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.

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

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

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

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

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

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

Classifying failures and using controlled retries in Kafka prevents outages.

Kafka uses retry topics and delayed retries to manage message failures efficiently.

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

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.

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

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

Leader election ensures a single node coordinates tasks in distributed systems, even after failures.

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

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

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

Poison messages in Kafka can cause infinite retries, risking system stability.

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

You can replay Kafka events safely by separating live and replay processes.

Apache Kafka is a system for managing large volumes of events efficiently.