Availability and Consistency
What happens when data lives on several machines and the network between them fails: the CAP theorem, consistency models, fail-over and how to put a number on availability.
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Module 02
Availability and Consistency
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2.1
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CAP Theorem
When the network splits a distributed database in two, each side must either refuse requests to stay consistent or answer with possibly stale data to stay available. Learn what CAP really says, and how PACELC adds the trade-off you face every day.
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2.2
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Consistency Patterns
Weak, eventual and strong consistency describe when a write becomes visible to readers. Learn each one, the session guarantees users actually notice, and how quorums (R + W > N) give strong reads from replicated data.
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2.3
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Availability Patterns: Fail-over and Replication
How systems stay up when machines fail: active-passive and active-active fail-over, leader-follower and multi-leader replication, and the traps that come with them, such as split brain and lost writes.
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2.4
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Availability in Numbers
What 99.9% and 99.99% availability mean in minutes of downtime, how to calculate the availability of components in series and in parallel, and why every dependency you add lowers the total.