1. ACID Properties

ACID represents the foundational properties that guarantee reliable processing of database transactions. Each property plays an essential role in preserving data integrity, especially in multi-user environments.

Atomicity

• Definition: Atomicity ensures that a transaction is treated as a single, indivisible unit. All operations within the transaction must succeed; otherwise, the entire transaction is rolled back.
• Example: Consider a banking transaction where funds are transferred from Account A to Account B. The process involves two operations: debiting Account A and crediting Account B. If the credit operation fails after the debit has occurred, atomicity ensures that the debit is rolled back, preventing money from “disappearing.”

Consistency

• Definition: Consistency ensures that a transaction brings the database from one valid state to another, maintaining all defined rules, constraints, and triggers.
• Example: In an e-commerce system, an order placement process must update inventory, record the order, and update customer information. Consistency ensures that, if the database rules specify that inventory cannot go negative and orders require valid customer information, these conditions are met after the transaction completes.

Isolation

• Definition: Isolation ensures that concurrently executed transactions do not interfere with each other. Intermediate states of a transaction are invisible to other concurrent transactions.
• Real-World Scenario: In a ticket booking system, two users booking the last available seat should not see an intermediate state where the seat is partially reserved. Isolation helps ensure that one user's booking does not affect the other’s view of available seats.

Durability

• Definition: Durability guarantees that once a transaction has been committed, its changes are permanent, even in the event of a system failure.
• Example: Once a purchase order is confirmed in an online shopping platform, durability ensures that the order details are not lost, even if the system crashes immediately after confirmation. This often involves writing to stable storage like disk or SSD.

2. Isolation Levels

Isolation levels control how and when the changes made by one transaction become visible to other transactions. They strive to achieve a balance between strict data consistency and system performance, allowing different trade-offs between concurrency and potential anomalies like dirty reads, non-repeatable reads, and phantom reads.

Read Uncommitted

• Characteristics: The lowest isolation level where transactions may read data that has been modified by other uncommitted transactions.
• Problem: Allows "dirty reads." For example, if Transaction A has updated a record but not committed, Transaction B may see this uncommitted update. If Transaction A subsequently rolls back, Transaction B’s read would be based on data that never actually existed.
• Use Case: Rarely used in scenarios where data correctness is critical, but might be acceptable for non-critical reporting systems where slight inconsistencies are tolerable.

Read Committed

• Characteristics: Ensures that transactions only see committed data. Each statement within a transaction sees only data that was committed before the statement began.
• Advantages: Prevents dirty reads.
• Limitations: Does not protect against non-repeatable reads. For example, the same query issued twice within a transaction could yield different results if data is modified and committed by another transaction in the meantime.
• Typical Use: Common default isolation level for many RDBMS, balancing performance and data integrity.

Repeatable Read

• Characteristics: Guarantees that once data is read, it cannot change during the course of the transaction. This level prevents non-repeatable reads.
• Problem Handling: May still allow phantom reads, where a query retrieves a different set of rows if other transactions insert or delete rows matching the query's WHERE clause.
• Example: In a sales report, if a transaction reads the total sales for a product, repeatable read ensures that re-running that query within the same transaction returns the same result.

Serializable

• Characteristics: The strictest isolation level, ensuring complete isolation from other transactions. Transactions behave as if they were executed serially (one after the other).
• Impact on Performance: Highly reliable for consistency but can significantly reduce concurrency, leading to potential performance bottlenecks.
• Example: In a scenario where multiple transactions update a highly contested record (e.g., stock levels for a limited edition product), using serializable isolation ensures that all concurrent operations are executed in a manner that avoids conflicts, though possibly at the cost of throughput.

3. Locking Mechanisms and Deadlocks

Locking mechanisms are central to how a database ensures isolation among concurrent transactions. Understanding the types of locks and how deadlocks occur is critical for designing robust systems.

Types of Locks

• Shared Locks (S-Locks)

  • Purpose: Allow transactions to read a data item without modifying it.
  • Behavior: Multiple transactions can hold shared locks on the same data simultaneously.
  • Example: Two transactions reading the balance of a bank account can hold shared locks concurrently without interference.

• Exclusive Locks (X-Locks)

  • Purpose: Allow transactions to modify data.
  • Behavior: When a transaction holds an exclusive lock on a data item, no other transaction can obtain any lock (shared or exclusive) on that data until the lock is released.
  • Example: A transaction updating an employee’s salary will hold an exclusive lock on the respective record until the update is committed.

• Intention Locks

  • Purpose: Indicate a transaction’s intention to acquire locks on some lower-level resources within a table.
  • Usage: Helps prevent conflicts by signaling upcoming operations without locking entire tables immediately.

Deadlocks

• How Deadlocks Occur

  • Scenario: Occurs when two or more transactions hold locks on resources that the other transactions need to continue. For example, Transaction A locks row 1 and requires row 2 (already locked by Transaction B), while Transaction B simultaneously locks row 2 and requires row 1.
  • Detection: Most RDBMS have deadlock detection mechanisms that monitor for cycles in lock dependency graphs.

• Strategies for Prevention/Resolution

  • Timeouts: Automatically rolling back transactions that have been waiting too long.
  • Deadlock Detection Algorithms: The RDBMS periodically checks for deadlocks and aborts one of the transactions to break the cycle.
  • Transaction Design: Ordering resource requests consistently and minimizing transaction scope to reduce the likelihood of deadlocks.

4. Concurrency Control Strategies

Concurrency control mechanisms ensure that multiple transactions can operate on the database concurrently without leading to inconsistency. There are two primary approaches: optimistic and pessimistic concurrency control.

Optimistic Concurrency Control

• Concept: Assumes that multiple transactions can complete without affecting each other and only checks for conflicts when a transaction is about to commit.
• How It Works:
  • Versioning or Timestamps: Transactions may record a version number or timestamp for the data they read.
  • Conflict Checking: At commit time, the system checks if the data has been modified by another transaction during the transaction’s lifetime.
  • Example: In a web-based inventory system, multiple users might concurrently update product quantities. Each transaction will read the same version of the product record; if another transaction updates the record and commits before the first transaction, the first transaction, upon attempting to commit, will detect the conflict and may need to be restarted.
• Benefits: Higher throughput in environments with low contention.
• Drawbacks: May lead to a higher rate of transaction rolls, and re-processing may be needed if conflicts are detected.

Pessimistic Concurrency Control

• Concept: Assumes that conflicts are likely to happen and proactively locks resources as soon as they are accessed.
• How It Works:
  • Locking Resources: As soon as a transaction begins to operate on data, the system places locks (shared or exclusive) to prevent other transactions from modifying the data concurrently.
  • Example: In a banking system where account balances are being updated, a pessimistic approach might lock an individual account record immediately when the transaction begins, ensuring that no other transaction can alter that record until the current one completes.
• Benefits: Guarantees a high level of data integrity by preventing conflicts upfront.
• Drawbacks: Can lead to reduced concurrency and potential bottlenecks, as well as increased chances of deadlocks.

Techniques for Handling Concurrent Access in High-Load Environments

• Row-Level Locking: Instead of locking entire tables, row-level locking minimizes the impact on concurrent transactions by locking only the specific rows that are being modified.
• Multi-Version Concurrency Control (MVCC):
  • Working Principle: Instead of locking data for reads, MVCC allows multiple versions of data to exist simultaneously.
  • Impact: Readers can access a snapshot of the data without being blocked by writers, significantly improving read performance under high load. Many modern databases (e.g., PostgreSQL, Oracle) use MVCC.
• Deadlock Prevention Strategies:
  • Timeouts and Retry Logic: Implementing timeouts and automatic retries in application logic to gracefully handle deadlocks.
  • Lock Ordering: Ensuring that all transactions access resources in a consistent order to minimize the chances of deadlock.