Consistency ensures that all nodes in a distributed system see the same data at the same time. When a write operation is completed, all subsequent reads will return the latest written value.

Availability

Availability guarantees that every request receives a response, whether it is successful or fails. The system remains operational and accessible even if some of its components are down.

Partition Tolerance

Partition Tolerance means that the system continues to operate despite network partitions that split communication between nodes. It ensures that the system can handle network failures gracefully.

Consistency

Single Source of Truth

In a consistent system, there is always a single source of truth. All nodes reflect the same data at any point in time.

Immediate Data Reflection

Any changes to data are immediately reflected across all nodes, ensuring that all clients see the same version of data.

Strong vs. Eventual Consistency

Strong consistency provides immediate consistency after a write operation, while eventual consistency allows for temporary discrepancies that resolve over time.


      // Example: Banking Transaction System
      // Ensures that account balances are consistent across all nodes
      public class BankingSystem {
          public synchronized void transferFunds(Account from, Account to, double amount) {
              if (from.getBalance() >= amount) {
                  from.debit(amount);
                  to.credit(amount);
              }
          }
      }

Ensuring Data Integrity

Consistency is crucial in systems where data integrity is paramount, such as financial transactions.

Trade-offs with Availability

Achieving consistency often requires trade-offs with availability, especially during network partitions.

Availability

High Availability Systems

High availability systems ensure that services remain accessible and operational, even during failures.

Redundancy and Failover

Implementing redundancy and failover mechanisms helps maintain availability by providing backup resources.

Load Balancing

Load balancing distributes incoming traffic across multiple servers to prevent any single server from becoming a bottleneck.


      // Example: Web Application Load Balancer
      // Distributes client requests across multiple servers
      public class LoadBalancer {
          private List servers;
          
          public Server getNextAvailableServer() {
              // Logic to select the next available server
          }
      }

Ensuring Continuous Service

Availability is vital for services that require continuous operation, such as online platforms and e-commerce sites.

Balancing with Consistency

Maintaining availability may involve sacrificing consistency during network issues or failures.

Partition Tolerance

Handling Network Partitions

Partition tolerance is essential for systems that must remain operational despite network failures that partition the network into isolated segments.

Resilience to Failures

Systems designed with partition tolerance can withstand network disruptions and continue to function correctly.

CAP Theorem Trade-offs

According to the CAP theorem, a distributed system can only guarantee two out of the three properties: Consistency, Availability, and Partition Tolerance.


      // Example: Distributed Database with Partition Tolerance
      // Ensures data is accessible even during network partitions
      public class DistributedDatabase {
          public void writeData(String key, String value) {
              // Logic to handle writes during network partitions
          }
      }

Ensuring Data Availability

Partition tolerance ensures that data remains available even when parts of the network are unreachable.

Design Considerations

Designing for partition tolerance involves making trade-offs between consistency and availability.