Hands-On Exercises: Understanding PACELC Through Practical Simulations

To truly grasp the concepts of CAP Theorem and PACELC, nothing beats hands-on experience. In this section, we’ll focus on practical exercises to simulate partitioned systems and explore the trade-offs between availability, consistency, and latency in distributed systems.

Exercise 1: Simulate a Partitioned System

This exercise will help you experience the trade-offs between availability and consistency during network partitions.

Objective

Build a simple system where:

Tools Needed

Steps to Implement

  1. Set Up a Simple Distributed System

    • Create two servers (e.g., Server A and Server B) that replicate data between each other.
    • Each server should have an endpoint to store and fetch data (e.g., /write and /read).

  2. Simulate Data Replication

    • Write a script to ensure that whenever data is written to one server, it’s automatically sent to the other for consistency.

  3. Introduce a Network Partition

    • Add logic to simulate a network partition where Server A and Server B can’t communicate.

  4. Experiment with Trade-Offs

    • Consistency Mode:
      When a client writes data to Server A during the partition, reject the request unless Server B can also receive the update.
      • Result: System ensures consistency but sacrifices availability.
    • Availability Mode:
      Allow Server A to process writes independently of Server B during the partition.
      • Result: System remains available, but consistency is sacrificed (Server B may have outdated data).

Expected Outcome

By testing both modes, you’ll see how consistency and availability impact the system’s behavior during partitions.

PACELC goes beyond CAP by introducing the trade-off between latency and consistency during normal operations. Let’s analyze popular distributed systems in the context of PACELC.

Objective

Understand how systems like DynamoDB and Cassandra handle partition tolerance, availability, consistency, latency, and trade-offs in their designs.

Case Study: Amazon DynamoDB

  1. Overview
    DynamoDB is a NoSQL database service by Amazon Web Services (AWS) that prioritizes availability during network partitions (AP in CAP).

  2. Trade-Offs During Partition (PA)

    • DynamoDB chooses availability:
      When a partition occurs, DynamoDB allows updates to be processed independently, even if not all replicas are synchronized immediately.
      This ensures the system stays available but sacrifices consistency.

  3. Else Condition (ELC)

    • During normal operations, DynamoDB allows you to choose between:
      • Eventual Consistency: Faster responses but with potential delays in data synchronization.
      • Strong Consistency: Slower responses but guarantees all replicas are updated before returning a result.

  4. Hands-On Exercise for DynamoDB

    • Use AWS DynamoDB to create a sample database.
    • Perform a write operation with both consistency modes:
      • Eventual Consistency: Write data and immediately fetch it from a different region. Notice that the data might not be updated instantly.
      • Strong Consistency: Use DynamoDB’s strong consistency option and observe how the latency increases to ensure accuracy.

Case Study: Apache Cassandra

  1. Overview
    Cassandra is a highly available NoSQL database that prioritizes availability during partitions (AP in CAP).

  2. Trade-Offs During Partition (PA)

    • Cassandra ensures availability by allowing writes to proceed even if some replicas are unreachable.
    • However, you can configure its consistency level (e.g., ONE, QUORUM, or ALL) to adjust the balance between consistency and availability.

  3. Else Condition (ELC)

    • Cassandra trades latency for consistency during normal operations.
    • Developers can choose consistency levels:
      • ONE: Fastest but with less consistency.
      • QUORUM: A middle-ground option balancing latency and consistency.
      • ALL: Slowest but ensures all replicas are consistent.

  4. Hands-On Exercise for Cassandra

    • Install Apache Cassandra locally or use a cloud provider.
    • Configure a cluster with 3 nodes and perform the following:
      • Write data with consistency=ONE. Notice how the write is accepted even if only one node responds.
      • Write data with consistency=ALL. Observe how the latency increases, but all replicas are updated.

Exercise 3: Simulating Latency vs. Consistency Trade-Off

This exercise will help you understand how latency and consistency are related in distributed systems.

Objective

Build a system that demonstrates:

Tools Needed

Steps to Implement

  1. Set Up a Key-Value Store

    • Use a database like Redis or MongoDB to store simple key-value pairs.

  2. Simulate a Latency-Consistent System

    • Configure a distributed setup with two database nodes replicating data.
    • Write a script that performs reads and writes with:
      • Relaxed Consistency: Respond immediately after writing to one node.
      • Strong Consistency: Wait for confirmation that all nodes have updated before responding.

  3. Measure Latency

    • Use a timer to measure how long each operation takes under both modes.
    • Observe the trade-off between faster responses and consistent data.

Expected Outcome

You’ll see how prioritizing consistency can slow down a system, while prioritizing latency might allow users to see slightly outdated data.

Key Takeaways

  1. Hands-On Simulations Are Powerful
    Simulating distributed systems gives you a firsthand understanding of the challenges in maintaining consistency, availability, and low latency.

  2. PACELC Is About Realistic Trade-Offs

    • During partitions, systems must balance availability and consistency.
    • Even without partitions, systems must balance latency and consistency.

  3. Practical Learning with Popular Systems
    Systems like DynamoDB and Cassandra let you experiment with trade-offs, helping you understand how these concepts apply to real-world applications.