Drawing and Planning

Visualizing concepts like CAP Theorem, PACELC, and system metrics is essential to understanding their trade-offs and practical implications. Below, we’ll create diagrams and flowcharts to explain these concepts and help map system behaviors.

---

1. CAP Theorem Trade-Off Diagram

Explanation

The CAP theorem states that in a distributed system, you can guarantee only two of the following three properties simultaneously:

1. Consistency (C): All nodes see the same data at the same time.
2. Availability (A): Every request gets a response, even if some nodes are down.
3. Partition Tolerance (P): The system continues to function despite network partitions.

Diagram Description

A triangle labeled with Consistency, Availability, and Partition Tolerance at the vertices. Each edge shows the trade-offs when you focus on two properties:

• Consistency + Availability = No partition tolerance.
• Availability + Partition Tolerance = Weaker consistency.
• Consistency + Partition Tolerance = Limited availability.

---

Flowchart Code for CAP Diagram

graph TD
    A[CAP Theorem]
    C[Consistency]
    P[Partition Tolerance]
    A2[Availability]

    A --> C
    A --> P
    A --> A2

    subgraph Trade-Offs
        C -- "No Partition Tolerance" --> A2
        A2 -- "Weaker Consistency" --> P
        P -- "Limited Availability" --> C
    end

---

2. PACELC Theorem Diagram

Explanation

PACELC extends CAP by adding latency trade-offs when there is no partition:

• Partition: During a network partition, you must choose between availability and consistency.
• Else: When there is no partition, you must choose between latency and consistency.

Diagram Description

The diagram is a decision flow:

1. Is there a network partition?
   • If yes, choose between availability and consistency (CAP).
   • If no, choose between latency and consistency (ELC).

---

Flowchart Code for PACELC Diagram

graph TD
    A[Start]
    B[Network Partition?]
    C[Availability]
    D[Consistency]
    E[Latency]

    A --> B
    B -- Yes --> C
    B -- Yes --> D
    B -- No --> E
    E --> D

---

3. Mapping System Metrics: Latency and Throughput Flow

Explanation

This flowchart illustrates how latency and throughput are impacted by architectural decisions in a distributed system:

1. Read/Write Requests → Enter the system.
2. If partition occurs, trade-offs affect consistency or availability.
3. Latency increases if consistency is prioritized.
4. Throughput improves if availability is prioritized.

---

Flowchart Code for Metrics Mapping

graph TD
    A[Client Request]
    B[Distributed System]
    C[Network Partition?]
    D[Increase Latency]
    E[Improve Throughput]
    F[Consistency Trade-Off]
    G[Availability Trade-Off]

    A --> B
    B --> C
    C -- Yes --> F
    C -- No --> G
    F --> D
    G --> E