CAP theorem, PACELC, replication models, quorum, Raft and Paxos, sharding strategies and consistency models.
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- What does the CAP theorem state a distributed system can guarantee?
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At most two of three: consistency, availability, or partition tolerance- In CAP, what does consistency mean?
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All nodes see the same data at the same time- In CAP, what does availability mean?
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Every request receives a response, though it may not be the most recent data- In CAP, what does partition tolerance mean?
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The system continues operating even when network partitions split nodes- Why can a distributed system not have all three CAP properties?
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During a partition, nodes cannot communicate, so you must sacrifice either consistency or availability- What does PACELC extend about the CAP theorem?
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It addresses both partition scenarios: if partition, choose PA; else, choose between latency and consistency- What trade-off does PACELC introduce in non-partition scenarios?
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Latency vs consistency: lower latency requires accepting eventual consistency- Name a distributed system that chooses availability during partitions.
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Amazon Dynamo or Cassandra, which use eventually consistent replication- What is eventual consistency?
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All replicas converge to the same state eventually, but may be inconsistent temporarily- What is strong consistency, also called linearizability?
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Reads always reflect all acknowledged writes, as if data resided on a single server- What is causal consistency?
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Causally related writes are seen in the same order by all processes- What is session consistency?
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Within a single client session, reads and writes are mutually consistent- What is read-after-write consistency?
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After a write, the same client always reads the value it wrote- What is monotonic read consistency?
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Once a client reads a value, subsequent reads return the same or a newer value- What is monotonic write consistency?
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Writes from the same process are visible to all others in the order they were performed
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