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Lesson 3 of 17

16. Consistency and CAP

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The picture to keep in your head
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16.1 What does "consistent" actually mean?
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16.2 What is strong consistency?
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16.3 What is eventual consistency?
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16.4 What are read-your-writes and monotonic reads?
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16.5 What is a network partition?
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16.6 What does the CAP theorem actually say?
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16.7 What do CP and AP systems look like in practice?
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16.8 What is PACELC?
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16.9 How do quorums give tunable consistency?
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16.10 How do you choose consistency for each feature?
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Memory card and where the analogy breaks
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Practice: five levels
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Interview questions
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Solutions
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Sources
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Code lab
16. Consistency and CAP

16.2 What is strong consistency?

3 min read+10 XP

At the bank: However many counters there are, the bank behaves as if there's one ledger. The moment a withdrawal is confirmed at counter 1, counter 2 can't show the old balance. Ever.

In the system: Strong consistency, in its strictest common form called linearizability, means the system behaves as if there were a single copy of the data:

  • once a write is confirmed, every later read, from any replica, returns that value (or a newer one);
  • all clients see operations in one agreed order that matches real time.

strong consistency: every read after the write sees ₹100; eventual: some reads still see ₹500 for a while.

Figure: strong means one copy as far as anyone can tell; eventual means copies agree once writes stop.

How systems provide it: route reads and writes through a single leader (and don't read from lagging followers), or use consensus (Raft, Paxos; Chapter 14, section 14.8) so a majority agrees before a write is confirmed.

What it costs:

  • Latency: replicas must coordinate, often across a network round trip, before answering (16.8).
  • Availability during failures: if a replica can't reach the others, it must refuse rather than risk a stale answer (16.6).

Use it where a wrong answer is worse than a slow one: money, stock at checkout, seat bookings, unique usernames, leader election.

Memory code: "Many counters, one ledger."