Database

1. Atomicity

   • Definition: Each transaction is all-or-nothing. If any part of the transaction fails, the entire transaction fails.
   • Example: If you're transferring money between bank accounts, either both the debit and credit occur, or neither does.

2. Consistency

   • Definition: Transactions must move the database from one valid state to another, maintaining all predefined rules.
   • Example: If a transaction updates an account balance, the total amount of money should remain the same before and after the transaction.

3. Isolation

   • Definition: Transactions should not affect each other. They should behave as if they're executed sequentially, even if they're run in parallel.
   • Example: If two transactions are occurring simultaneously, one transferring funds and another checking balance, they should not interfere with each other.

4. Durability

   • Definition: Once a transaction is committed, it remains so, even in the event of a system failure.
   • Example: After a successful transfer, the system crashes. Once back up, the transaction should still be reflected in the database.

Imagine a bank transaction where Alice transfers $100 to Bob:

• Atomicity: If the debit from Alice's account fails, the credit to Bob's account won't happen.
• Consistency: The total money in Alice's and Bob's accounts remains unchanged.
• Isolation: Another transaction checking Bob's balance won’t see the transfer until it’s complete.
• Durability: Once the transfer is confirmed, it remains so despite any crashes.

These properties ensure that databases operate correctly and predictably, even in complex scenarios.

Sharding is a database architecture pattern used to horizontally partition data across multiple servers, enabling systems to handle more data and transactions. It improves scalability and performance.

• Definition: Divides a database into smaller, manageable pieces called "shards."
• Purpose: Allows distribution of data across multiple machines, balancing the load and reducing bottlenecks.

• Definition: A specific key or column used to determine how data is distributed across shards.
• Purpose: Ensures that related data is stored together and efficiently queried.

Imagine an e-commerce application with a database table Orders:

1. Choose a Sharding Key: Let's use UserID as the sharding key.

2. Shard Distribution:

   • Shard 1: Users with IDs from 100 to 199
     • Orders for UserIDs 101 and 102
   • Shard 2: Users with IDs from 200 to 299
     • Orders for UserIDs 201 and 202

• Scalability: As data grows, add more shards.
• Performance: Queries are distributed, reducing load on a single server.
• Fault Tolerance: Failure of one shard doesn't affect others.

By choosing a proper sharding key, data is balanced across shards, minimizing data transfer and optimizing query performance.

1. Scalability
   • Horizontal Scaling: Easily distribute data across multiple servers.
2. Data Model Flexibility
   • Dynamic Schemas: When your data structure is evolving or unstructured.
3. Performance
   • High Volume of Reads/Writes: Low-latency requirements for large datasets.
4. Specific Use Cases
   • Big Data Applications: Handling massive data and analytics.
   • Real-Time Applications: Such as chat apps or IoT platforms.
   • Content Management Systems: With varied and flexible content types.

• Consistency vs. Availability: Decide based on your application needs (CAP theorem).
• Data Relationships: NoSQL is better for denormalized data but less ideal for complex joins.