Visualization: CAP and PACELC Theorem with Real-World Examples

To understand the CAP theorem and its extended variant PACELC theorem, we’ll visualize scenarios and map real-world distributed systems to different consistency and availability models.

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1. CAP Theorem Visualized

The CAP theorem states that in a distributed system, you can only achieve two out of three guarantees:

1. Consistency (C): All nodes see the same data at the same time.
2. Availability (A): Every request receives a response (success or failure).
3. Partition Tolerance (P): The system continues to operate even if communication between nodes is lost.

CAP Triangle Diagram

graph TD
    C[Consistency]
    A[Availability]
    P[Partition Tolerance]
    C -- "Choose two" --> A
    C -- "Choose two" --> P
    A -- "Choose two" --> P

Real-World Examples:

• CA (Consistency + Availability): Systems like SQL Databases (only when running in a single-node setup without partition tolerance).
• AP (Availability + Partition Tolerance): NoSQL systems like DynamoDB, Cassandra.
• CP (Consistency + Partition Tolerance): Systems like Google Spanner, Zookeeper.

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2. PACELC Theorem Visualized

PACELC extends CAP by considering latency trade-offs in the absence of a partition. It posits:

• If a Partition (P) occurs, systems must choose between Availability (A) or Consistency (C) (as in CAP).
• Else (E) (during normal operation), systems must balance Latency (L) vs Consistency (C).

PACELC Decision Flow

graph TD
    Start[Distributed System]
    Start --> Partition["Is there a network partition?"]
    Partition -->|Yes| CAPChoice["Choose: Consistency or Availability"]
    Partition -->|No| PACELCChoice["Choose: Latency or Consistency"]

Real-World Examples:

• DynamoDB (AP/EL):
  • During a partition, DynamoDB prioritizes availability.
  • Else, it balances latency by offering eventual consistency as default.
• Google Spanner (CP/EC):
  • During a partition, it prioritizes consistency.
  • Else, it ensures strong consistency at the cost of increased latency.

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3. Comparison Table: CAP and PACELC

Property	CAP Choice	PACELC Choice	Real-World Example
Consistency Priority (CP)	Consistency + Partition	Latency vs Consistency	Google Spanner, Zookeeper
Availability Priority (AP)	Availability + Partition	Latency vs Consistency	DynamoDB, Cassandra
Latency Optimized (EL)	-	Latency prioritization	DynamoDB (default eventually consistent reads)

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4. Visualization of Real-World Systems

DynamoDB: AP/EL Scenario

1. Partition Scenario (CAP):
   • DynamoDB chooses Availability (A) over Consistency (C).
   • Even if some nodes are unreachable, requests are served from available nodes with eventual consistency.

graph TD
    Request["Client Request"]
    DynamoDBNode1["Node 1 (Available)"]
    DynamoDBNode2["Node 2 (Unavailable)"]
    DynamoDBNode3["Node 3 (Available)"]
    Request --> DynamoDBNode1
    Request --> DynamoDBNode3
    DynamoDBNode1 --> Response1["Response: Stale Data Possible"]
    DynamoDBNode3 --> Response2["Response: Stale Data Possible"]

1. No Partition Scenario (PACELC):
   • DynamoDB defaults to eventual consistency, reducing latency for most reads.
   • Strong consistency can be requested, increasing latency.

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Google Spanner: CP/EC Scenario

1. Partition Scenario (CAP):
   • Spanner prioritizes Consistency (C) over Availability (A).
   • If a partition occurs, it refuses writes that could cause inconsistency.

graph TD
    WriteRequest["Write Request"]
    Partition["Network Partition"]
    Node1["Node 1"]
    Node2["Node 2"]
    WriteRequest --> Partition
    Partition -->|Consistency Prioritized| RefuseWrite["Write Refused"]

1. No Partition Scenario (PACELC):
   • Spanner guarantees strong consistency by synchronizing data across regions using TrueTime, increasing latency.

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5. Latency vs Consistency: PACELC in Practice

Measuring Latency Trade-offs

Using DynamoDB as an example:

• Strongly Consistent Reads:
  • Ensures the most up-to-date data but has higher latency.
• Eventually Consistent Reads:
  • Offers faster response times by allowing stale data.

Latency vs Consistency Diagram

graph LR
    Client["Client Request"]
    StrongRead["Strongly Consistent Read"]
    EventualRead["Eventually Consistent Read"]
    Response["Response"]
    Client --> StrongRead
    StrongRead -->|High Latency| Response
    Client --> EventualRead
    EventualRead -->|Low Latency| Response