CAP Theorem Interactive Demo

Master Consistency, Availability, and Partition Tolerance with Fun!

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🎯 What is CAP Theorem?

The CAP Theorem (Brewer's Theorem) states that a distributed system can only guarantee two out of three properties: Consistency, Availability, and Partition Tolerance.

🔄 Consistency

Definition: All nodes show the same data at the same time. Every read gets the latest write or an error.

Example: A bank account shows $300 on all platforms (app, ATM, web) after a $200 transfer.

⚡ Availability

Definition: The system remains operational and responds to all requests, even if data isn’t the latest.

Example: A banking app responds at 2 AM, even during maintenance, never showing “Service Unavailable.”

🌐 Partition Tolerance

Definition: The system continues to operate despite network failures between nodes.

Example: Servers in different cities lose connection, but users can still access local data.

Click a demo button to explore CAP principles!

⚖️ CAP Theorem Trade-offs

Core Principle: You can only choose two out of three properties. Explore the combinations:

CP - Consistency + Partition Tolerance

Sacrifice: Availability

Use Case: Banking, Financial systems

Examples: MongoDB, Redis, HBase

May be temporarily unavailable

AP - Availability + Partition Tolerance

Sacrifice: Consistency

Use Case: Social media, Content delivery

Examples: Cassandra, DynamoDB, CouchDB

May show stale data

CA - Consistency + Availability

Sacrifice: Partition Tolerance

Use Case: Single-node or local networks

Examples: PostgreSQL, MySQL (single node)

Fails during network splits
Click a system type to see detailed examples!

🌍 Real-World CAP Theorem Applications

System Type CAP Choice Example Business Impact Trade-off Justification
Banking Systems CP Account balances, transactions Accurate data is critical Better to pause than show incorrect data
Social Media AP Social posts, feeds Prioritizes user engagement Users tolerate slightly outdated posts
E-commerce Catalog AP Product listings Uninterrupted shopping experience Slightly outdated info is acceptable
Stock Trading CP Real-time stock prices Accuracy prevents financial loss Trading halts better than wrong prices
Content Management CA Corporate wikis Reliable in controlled networks Partitions rare in local networks

🔧 Network Partition Simulator

Simulate how systems handle network partitions. Select a system type and observe the behavior:
Server A
Status: Online
Network Connection
Status: Connected
Server B
Status: Online
Simulation Status: Ready to begin

❓ CAP Theorem FAQ

What happens without a network partition?

In a healthy network, all three properties (Consistency, Availability, Partition Tolerance) can be maintained. CAP constraints apply only during partitions.

Is CAP Theorem still relevant today?

Yes, network partitions remain a challenge in modern cloud systems. CAP guides architects in designing resilient systems.

What is eventual consistency in CAP?

AP systems often use eventual consistency, where data syncs over time, unlike the strong consistency of CP systems.

How does CAP relate to ACID?

CAP addresses distributed system behavior across nodes, while ACID focuses on transaction properties within a single database.

🎓 Key Takeaways

  • Trade-off Principle: CAP Theorem limits distributed systems to two out of three properties.
  • Business-Driven Choices: CP for finance, AP for social platforms, CA for local systems.
  • Partition Tolerance is Key: Network failures are inevitable in distributed systems.
  • Tailor to Use Case: Match system design to your consistency and availability needs.
  • Modern Approaches: Hybrid systems with tunable consistency are increasingly common.