Distributed Systems Essentials
The ideas underneath large systems: unique IDs without coordination, leader election and consensus, transactions across services, probabilistic data structures, and clocks you cannot trust.
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Module 09
Distributed Systems Essentials
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9.1
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Unique ID Generation
Auto-increment IDs stop working once data is spread over many databases. Learn the options for globally unique IDs: UUIDs, ticket servers and Snowflake-style IDs that are unique, roughly time-ordered and fit in 64 bits.
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9.2
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Leader Election and Consensus
Many systems need machines to agree on one leader or one order of changes, even when some fail. Learn why that is hard, how majorities (quorums) make it safe, and how Raft elects a leader and replicates a log.
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9.3
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Distributed Transactions: Two-Phase Commit vs Sagas
When one business action spans several databases or services, a single local transaction is not enough. Learn how two-phase commit gives atomicity at a cost, and how sagas use local transactions plus compensating actions instead.
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9.4
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Bloom Filters and Probabilistic Data Structures
Sometimes an approximate answer from a tiny structure beats an exact answer from a huge one. Learn how Bloom filters test membership, how HyperLogLog counts unique items and how Count-Min Sketch estimates frequencies.
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9.5
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Clocks and Ordering
Every machine has its own clock, and they disagree. Learn why wall-clock timestamps cannot order events across machines, and how Lamport clocks, vector clocks and bounded-uncertainty clocks give a reliable order.