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distributed systems
60 shorts across 1 course, in learning order
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Results · 10 for “Database Replication”
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⚡ 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
Kafka Replication and ISR: Ensuring Data Availability
Kafka uses replication and in-sync replicas to ensure data is always available.
⚡ One HLD concept. 60 seconds. Interview ready
Elasticsearch Shards vs Replicas: Partitioning vs Duplication
Primary shards partition data, while replica shards duplicate it for redundancy.
⚡ One HLD concept. 60 seconds. Interview ready
Read-After-Write Consistency in Database Systems
Read-after-write consistency ensures users see their latest updates immediately.
⚡ One HLD concept. 60 seconds. Interview ready
Kafka Partition Reassignment: Move Replicas Without Data Loss
Kafka Partition Reassignment moves replicas between brokers while keeping data safe.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Consistency Models in Distributed Systems
Consistency models determine what data reads in distributed systems can see.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Quorum Reads in Distributed Systems
Quorum Reads ensure data consistency by querying multiple replicas in distributed systems.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Raft Log Replication: matchIndex vs commitIndex
matchIndex tracks what each follower has, while commitIndex shows what is committed.
⚡ One HLD concept. 60 seconds. Interview ready
Kafka Exactly-Once Processing: Avoiding Duplicates
Kafka can help ensure messages are processed once, but requires careful design.
⚡ One HLD concept. 60 seconds. Interview ready
Failover and Split-Brain in High-Level Design
Failover ensures systems remain operational by managing leader changes during failures.
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Not tagged “distributed systems”, but closely connected
