202 Strong vs Eventual Consistency PDF

202 Strong vs Eventual Consistency * * * * Strong consistency and eventual consistency are two different models used to manage data consistency in distributed systems, particularly in database systems and...

202 Strong vs Eventual Consistency * * * * Strong consistency and eventual consistency are two different models used to manage data consistency in distributed systems, particularly in database systems and data storage services. [Strong vs Eventual Consistency] ~ ~ Strong Consistency **~ Definition: In a strong consistency model, a system guarantees that once a write operation is ~** * * *** completed, any subsequent read operation will reflect that write. In other words, all users *** *** see the same data at the same time. *** **~ Characteristics: ~** *** Immediate Consistency: Ensures that all clients see the same data as soon as it's updated * ** * or written. * ** Read-Write Synchronization: Read operations might have to wait for a write operation * *** *** == to complete to ensure consistent data is returned. == *** * *  *** Example: Consider a banking system where a user transfers money between accounts. With *** * * strong consistency, as soon as the transfer is processed, any query on the account balance will reflect the transfer. There's no period where different users see different balances. * * **~ Pros: ~** ** Data Reliability: Ensures high data integrity and reliability. * *** *** *** *** Simplicity for Users: Easier for users to understand and work with. * ** * * **~ Cons: ~** ** Potential Latency: Can introduce latency, especially in distributed systems, as the system ** *** == needs to ensure data is consistent across all nodes before proceeding. == *** ** Scalability Challenges: More challenging to scale, as ensuring immediate consistency ** * * across distributed nodes can be complex. Eventual Consistency **~ Definition: In an eventual consistency model, the system guarantees that if no new updates are ~** * * *** *** *** *** *** *** * made to a given piece of data, eventually all accesses will return the last updated value. * * However, for a time after a write operation, reads might return an older value. * * * **~ Characteristics: ~** *** Delayed Consistency: The system eventually becomes consistent but allows for periods * ** * where different users might see different data. * *** Higher Performance: Typically offers higher performance and availability than strong * ** * * * * consistency. *** Example: A social media platform's distributed database that uses eventual consistency might *** show different users different versions of a post's like count for a short period after it's updated. * Over time, all users will see the correct count. * * * **~ Pros: ~** ** Scalability: Easier to scale across multiple nodes, as it doesn't require immediate ** * * * ~~ ~~ consistency across all nodes. * ** High Availability: Offers higher availability, even in the presence of network partitions. ** * * **~ Cons: ~** == ** Data Inconsistency Window: There's a window of time where data might be inconsistent. * * ** * *== == *** Complexity for Users: Users might be confused or make incorrect decisions based on * ** * outdated information. * == Key Differences ** Consistency Guarantee: ** * Strong consistency ensures that all users see the same data at the same time, * * * * * * * * * *** while eventual consistency allows for a period where data can be inconsistent but *** * * * * * eventually becomes uniform. * * * ** Performance vs. Consistency: ** * Strong consistency prioritizes consistency which can affect performance and scalability. * * ** *** * * * * * Eventual consistency prioritizes performance and availability, with a trade-off in * * * *** *** *** *** *~~ immediate data consistency. ~~ * Conclusion The choice between strong and eventual consistency depends on the specific requirements of the *** application. *** Applications that require strict data accuracy (like financial systems) typically opt for strong * * consistency, * while applications that can tolerate some temporary inconsistency for better performance and * * * * availability (like social media feeds) might choose eventual consistency. * * *