Understanding Database Concurrency Control, ACID Properties, and Scalability Techniques
Concurrency Control and ACID Properties in Databases
Understanding Concurrency Control
Concurrency control is crucial in databases to prevent issues like lost updates and inconsistent retrievals (dirty reads). Lost updates occur when multiple transactions read the same value and then update it based on the old value, leading to data loss. Inconsistent retrievals happen when a transaction reads data that is being modified by another transaction, resulting in inaccurate information.
ACID Properties of Transactions
Transactions ensure data integrity through ACID properties:
- Atomicity: All operations within a transaction are treated as a single unit, either fully committed or fully aborted.
- Consistency: Transactions maintain the database’s consistency rules, ensuring data validity.
- Isolation: Concurrent transactions are isolated from each other, preventing interference and ensuring data consistency.
- Durability: Once a transaction is committed, its changes are permanent and survive system failures.
Serial Equivalence and ANSI Isolation Levels
Serial equivalence ensures that concurrent transactions have the same effect as if they were executed sequentially. ANSI isolation levels define different degrees of isolation to prevent anomalies like dirty reads, non-repeatable reads, and phantoms.
Database Scalability Techniques
Scaling Up vs. Scaling Out
Databases can be scaled vertically (scale-up) by adding more resources to a single node or horizontally (scale-out) by adding more nodes. Scale-up is limited by hardware constraints and single points of failure, while scale-out offers linear cost increases and fault tolerance through replication.
Partitioning and Replication
Partitioning involves splitting data into smaller chunks (horizontally by rows or vertically by columns) to distribute the load across multiple nodes. Replication creates copies of data on different nodes to improve read scalability and availability.
Database Replication and Availability
Replication Approaches
Replication enhances data availability and fault tolerance. Common approaches include:
- Read-One Write-All (ROWA): Reads are served from any replica, but writes must update all replicas.
- Read-One Write-All-Available (ROWA): Similar to ROWA, but allows writes even if some replicas are unavailable.
- Quorum Systems: Reads and writes require a quorum (a majority) of replicas to participate, ensuring consistency and fault tolerance.
Lazy vs. Eager Replication
Eager replication propagates updates synchronously within the transaction, ensuring consistency but increasing latency. Lazy replication propagates updates asynchronously, improving performance but potentially leading to inconsistencies that require reconciliation.
CAP Theorem and Trade-offs
The CAP theorem states that in a distributed system, it is impossible to guarantee all three of the following properties simultaneously:
- Consistency: All nodes have the same view of the data at any given time.
- Availability: Every request receives a response (success or failure).
- Partition Tolerance: The system continues to operate despite network partitions.
Database systems must choose trade-offs between these properties based on their specific requirements and priorities.