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⚡ One HLD concept. 60 seconds. Interview ready
Elasticsearch Split Brain: Master Election and Quorum Explained
Elasticsearch uses master election and quorum to prevent split brain scenarios.
⚡ One HLD concept. 60 seconds. Interview ready
HLD: Fail-Stop vs Fail-Recover Explained
Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Network Partitions in Distributed Systems
Network partitions occur when servers are operational but can't communicate, affecting system performance.
⚡ One HLD concept. 60 seconds. Interview ready
Consistent Hashing: Key to Distributed Systems Scalability
Consistent hashing helps distribute data across servers with minimal movement.
⚡ One HLD concept. 60 seconds. Interview ready
Kafka Producer Reliability: ACKs, Retries & Idempotence
Kafka producers use ACKs, retries, and idempotence to ensure reliable message delivery.
⚡ One HLD concept. 60 seconds. Interview ready
Log Replication and Majority Commit in Distributed Systems
Log replication ensures data consistency by requiring majority acknowledgment before committing changes.
⚡ One HLD concept. 60 seconds. Interview ready
HLD: Crash Failures vs Network Failures in Distributed Systems
Crash failures stop a service, while network failures disrupt communication.
⚡ One HLD concept. 60 seconds. Interview ready
FAANG HLD 🔥 | Raft Safety — Why Committed Entries Survive Leader Failure! 🛡️
Raft's safety rules guarantee that committed entries are preserved even after leader failures.
⚡ One HLD concept. 60 seconds. Interview ready
Message Delivery Semantics: At-Most-Once vs At-Least-Once vs Exactly-Once
Message delivery semantics define how messages are sent in distributed systems and handle failures.
⚡ One HLD concept. 60 seconds. Interview ready
Leader Election in Distributed Systems: Handling Leader Failures
Leader election ensures a single node coordinates tasks in distributed systems, even after failures.