CAP Theorem: The “Pick Two” Dilemma

The Theorem

In a Distributed Data Store, you can only guarantee two of the following three properties:

  1. Consistency (Linearizability): Every read receives the most recent write or an error. (Note: This is stricter than ACID Consistency).
  2. Availability: Every request receives a (non-error) response, without the guarantee that it contains the most recent write.
  3. Partition Tolerance: The system continues to operate despite an arbitrary number of messages being dropped or delayed by the Network between nodes.

The “CA” Myth

You will often hear: “Pick CA, CP, or AP.” This is misleading. In a distributed system over a network (like the Internet), Partitions (P) are inevitable. Cables get cut, routers crash, AWS regions go down. You MUST pick P. So your real choice is: CP vs AP.

  • CP (Consistency > Availability): “If the network breaks, shut down the system so we don’t return old data.” (e.g., Banking, Inventory).
  • AP (Availability > Consistency): “If the network breaks, keep serving data, even if it’s slightly old.” (e.g., Twitter/X Timeline, Reddit Comments).

Deep Dive: Google Spanner & TrueTime

Google Spanner claims to be a CA system. How? It uses TrueTime (Atomic Clocks + GPS) to synchronize clocks across data centers with tiny error margins (< 7ms). Because it knows exactly when a transaction happened, it can ensure Consistency without sacrificing Availability in practice. However, technically, if a massive partition happens that exceeds the TrueTime error margin, Spanner chooses Consistency (it pauses), making it effectively CP. But the “pause” is so rare (99.999% availability), it feels like CA.

Interactive Demo: The Split Brain Simulator

Control the Network. See the trade-off.

  1. Write Data: Update the value on Node A (USA).
  2. Cut the Network: Create a Partition.
  3. Read Data: Try to read from Node B (Asia).
    • CP Mode: Node B says “I can’t talk to A, so I won’t answer.” (Error 503).
    • AP Mode: Node B says “I can’t talk to A, but here is what I have.” (Stale Data).
SYSTEM MODE:
Node A (USA)
v1
⚡
Node B (Asia)
v1
STALE (5s)
System Ready.

Decision Matrix: When to pick which?

Scenario Choice Why?
Banking / ATM CP You cannot allow a user to withdraw money they don’t have. Show an error instead of a wrong balance.
Social Media Feed AP It’s okay if a user sees a post 5 seconds late. It’s NOT okay if the feed is blank (Error).
Shopping Cart AP Never stop a user from adding items. Merge the cart later (Dynamo style).
Ticket Booking CP You cannot sell the same seat twice. Locking is required.

Summary Comparison

Feature CP (Consistency-First) AP (Availability-First)
Philosophy “Better to fail than to lie.” “Better to give a wrong answer than no answer.”
Behavior during P Returns Error (503). Returns Stale Data.
Ideal For Banking, Billing, Inventory. Social Feeds, Comments, Likes.
Examples MongoDB, HBase, Redis. Cassandra, DynamoDB, CouchDB.

Next Step: PACELC

You might ask: “What happens when there is NO Partition?” CAP is silent on this. That’s why we have the PACELC Theorem, which extends CAP to handle the trade-off between Latency and Consistency when the network is healthy. Read about PACELC