Data Consistency Models

Types of Data Consistency Models

1. Strong Consistency:
   • In a strongly consistent system, whenever you read data, you always get the most recent update or an error if the data isn't available yet. This model ensures that all operations see the same view of data at any given time.
   • It's important for applications like financial transactions or inventory management where data accuracy is critical.
   • Example: In a banking system, when you transfer money from your savings account to your checking account, the system must ensure that the deducted amount immediately reflects in your savings account and is immediately available in your checking account.
2. Eventual Consistency:
   • Eventual consistency allows for temporary differences in data replicas but guarantees that eventually, all replicas will synchronize and have the same data. It prioritizes availability and can handle temporary inconsistencies.
   • It's commonly used in distributed systems and large-scale applications like social media platforms or content delivery networks (CDNs) where immediate consistency is less critical than availability and scalability.
   • Example: On a social media platform, when you post a status update, your friends might see it immediately or with a slight delay, but eventually, all users will see the same status update.
3. Causal Consistency:
   • Causal consistency ensures that if one event causally influences another, all nodes in the system see these events in the same order. It maintains a logical order of operations based on their dependencies.
   • It's important in collaborative applications where the order of operations matters, such as document collaboration tools or real-time editing environments.
   • Example: In a collaborative document editing tool, if User A writes a sentence and User B responds to it, all users must see User A’s sentence before User B’s response to maintain the logical flow of conversation.
4. Read-your-writes Consistency:
   • This model guarantees that any data written by a user will be immediately visible to subsequent read operations by the same user. It ensures that users always see their own updates in real-time.
   • It's essential for interactive applications like online shopping carts or messaging apps where users expect to see their changes immediately after making them.
   • Example: In an online shopping cart, when you add an item to your cart, you expect to see it immediately reflected when you view your cart.
5. Monotonic Consistency:
   • Monotonic consistency ensures that once a sequence of updates has been observed, subsequent observations will always reflect those updates or a more recent state. It prevents rollback or reordering of updates.
   • It's critical in systems where maintaining the order of operations is crucial, such as version control systems or distributed databases handling sequential data.
   • Example: In a version control system, once a developer has seen a commit, any subsequent views of the repository must include that commit or a later one, ensuring the history of changes remains consistent.