Senior LevelSoftware Engineering Principles
How do the CAP Theorem and PACELC Theorem govern distributed system tradeoffs?
❓ Interview Prompt: Explain CAP Theorem (Consistency, Availability, Partition Tolerance), CP vs AP systems, and PACELC Theorem (Latency vs Consistency trade-offs during normal operations).
The CAP Theorem proves that a distributed data store can simultaneously guarantee at most 2 out of 3 properties during a network partition:
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C (Consistency): Every read receives the most recent write or an error.
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A (Availability): Every non-failing node returns a non-error response.
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P (Partition Tolerance): System operates despite network dropouts.
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Real World: Network partitions are inevitable ($P$), forcing a choice between CP (PostgreSQL/HBase - choose consistency, sacrifice availability) or AP (Cassandra/DynamoDB - choose availability, sacrifice consistency).
PACELC Theorem Extension:
If there is a Partition (P), choose Availability or Consistency; Else, choose Latency or Consistency! (e.g. DynamoDB chooses PA/EL).
Code Implementation Example
json
{
"database": "Apache Cassandra",
"capClassification": "AP System",
"pacelcClassification": "PA/EL System",
"tradeoff": "Chooses low latency and high availability over strong consistency during normal operations"
}💡PACELC theorem classification for distributed databases.
🎯 Interview Key Takeaway
Apply CAP and PACELC theorems to evaluate consistency, availability, and latency trade-offs when selecting distributed databases.
#CAP Theorem#PACELC#Distributed Systems#Senior
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