Strong consistency

Definition

Strong consistency is a data storage model where all nodes in a distributed system reflect the most recent write operations at any given time. This means that once a write operation is confirmed, any subsequent read operation will return the latest data, ensuring that all users see the same view of the data. This level of consistency is crucial for applications where accuracy and immediate data availability are paramount, such as financial transactions or critical applications that cannot tolerate stale data.

5 Must Know Facts For Your Next Test

1. Strong consistency ensures that after a write operation completes, all subsequent reads will reflect that change across all nodes in the system.
2. This consistency model is often implemented using consensus algorithms like Paxos or Raft, which help to maintain synchronization across distributed databases.
3. While strong consistency provides high accuracy, it can lead to performance bottlenecks since operations may need to wait for confirmations from multiple nodes before proceeding.
4. In real-world scenarios, systems that require strong consistency may sacrifice availability during network partitions to maintain the integrity of the data.
5. Examples of databases that offer strong consistency include Google Spanner and Amazon DynamoDB when configured for strong consistency.

Review Questions

• How does strong consistency differ from eventual consistency in distributed systems?
  • Strong consistency guarantees that all nodes reflect the most recent write operation immediately after it's confirmed, meaning any read will return the latest data. In contrast, eventual consistency allows for temporary discrepancies between nodes, where updates may take time to propagate, leading to different views of the same data until they converge. This fundamental difference affects application design; systems requiring immediate accuracy prefer strong consistency, while those that can tolerate delays may opt for eventual consistency for better performance.
• Evaluate the trade-offs between strong consistency and system availability in distributed environments.
  • The trade-off between strong consistency and availability is often captured by the CAP theorem, which states that a distributed system can only provide two out of three guarantees: Consistency, Availability, and Partition Tolerance. When prioritizing strong consistency, systems might become unavailable during network partitions because they refuse to serve reads until all nodes agree on the latest data state. This is crucial for applications where correctness is critical, but it can hinder performance and user experience during outages.
• Analyze how strong consistency impacts the design choices made in developing distributed applications.
  • Strong consistency significantly influences the design of distributed applications by necessitating complex synchronization mechanisms to ensure all nodes have the same view of data at any given time. Developers must choose consensus algorithms that facilitate this level of coordination, which can introduce latency and reduce throughput. Additionally, this requirement often dictates infrastructure choices, such as selecting databases optimized for strong consistency, influencing overall system architecture, and possibly leading to decisions against features like horizontal scalability or relaxed latency requirements.

Related terms

eventual consistency:

A consistency model in distributed systems where updates to a data item will eventually propagate to all nodes, but not necessarily immediately, allowing for temporary discrepancies.

distributed systems: A model in which components located on networked computers communicate and coordinate their actions by passing messages to achieve a common goal.

CAP theorem: A principle that states it is impossible for a distributed system to simultaneously provide all three guarantees: Consistency, Availability, and Partition Tolerance.