System Design: Consistency Patterns

TL;DR:

Distributed systems store data across multiple servers for better performance and scalability. This introduces challenges in keeping data consistent across all servers. Consistency patterns determine how updates are propagated:

• Strong Consistency: Ensures all servers have the latest data, but can be slow and resource-intensive. Used in file systems, databases, and financial services.
• Weak Consistency: Prioritizes availability over perfect data accuracy. May have temporary inconsistencies. Used in VoIP, multiplayer games, and cache servers.
• Eventual Consistency: All servers will eventually have the same data, but reads may return outdated data temporarily. Used in CDNs, email systems, and social media platforms.

Distributed Systems

You probably know by now that the website or data we access daily does not necessarily reside on a single server. Everything in today's world mostly works on Distributed Systems meaning that data is scattered across several servers/nodes, these servers can be within the same country or across multiple countries. Distributed System solves problems such as fault tolerance, Scalability, and reliability.

Consider an e-commerce website with a global customer base. To provide fast and reliable service, the website's data is distributed across multiple servers.

However, this distribution can introduce challenges related to data consistency. For instance, imagine two customers, Alice and Bob, trying to purchase the last item of a popular product. If Alice places an order on Server A, the system might initially show the item as in stock. However, before the updated stock information is propagated to Server B, Bob could also place an order, leading to a situation where both customers believe they've successfully purchased the same item.

When Bob later discovers the item is out of stock, it can negatively impact the customer experience and erode trust in the company. This scenario highlights the importance of consistency in distributed systems.

The "Consistency Patterns" will determine how the state is propagated within the distributed systems.

Consistency Patterns

Strong Consistency

As the name suggests, strong consistency prioritizes data consistency over system availability. This pattern is essential for applications where accuracy and reliability are paramount, even if it comes at the cost of performance.

Challenges of Strong Consistency:

• Reduced Performance: Ensuring strong consistency can lead to increased latency and slower response times.
• Resource Intensive: Implementing strong consistency mechanisms can be computationally expensive, consuming significant system resources.

Applications of Strong Consistency:

• File Systems: File systems typically require strong consistency to ensure data integrity and prevent corruption.
• Relational Database Management Systems (RDBMS): RDBMSs often implement strong consistency to maintain data accuracy and prevent inconsistencies.
• Financial Services: Banking systems and financial transactions demand strong consistency to prevent errors and ensure accurate record-keeping.
• Distributed Protocols: Protocols like Paxos and two-phase commit (2PC) are designed to achieve strong consistency in distributed systems.

Weak Consistency

As the name suggests, weak consistency prioritizes availability and fault tolerance over strict data consistency. This pattern is suitable for services where data accuracy is less critical than ensuring the system remains accessible and operational.

Challenges of Weak Consistency:

• Data Loss: Data might be lost or corrupted due to inconsistencies between nodes.
• Data Conflicts: Conflicting updates to the same data can occur, leading to inconsistencies.

Applications of Weak Consistency:

• Voice over IP (VoIP): While audio quality is important, occasional glitches or brief interruptions are often tolerated.
• Multiplayer Games: Real-time updates and responsiveness are prioritized over absolute consistency.
• Live Streams: Slight delays or buffering are acceptable for a smooth viewing experience. Cache Servers: Caches may contain stale data, but the goal is to provide fast access to frequently requested information.

Eventual Consistency:

Eventual consistency is a relaxed consistency model that guarantees that all replicas of data will eventually converge to the same value if no new updates are made. In other words, while reads may return stale data temporarily, the system will eventually reach a consistent state.

Challenges of Eventual Consistency:

• Debugging: Identifying and resolving inconsistencies can be challenging, especially in complex distributed systems.
• Data Loss: In rare cases, data loss can occur due to network failures or synchronization issues.
• User Experience: Stale data can negatively impact user experience, especially in applications that require real-time updates.

Applications of Eventual Consistency:

• Content Delivery Networks (CDNs): CDNs distribute content across multiple servers to improve performance and availability. Eventual consistency allows for quick updates to be propagated, but users might see slightly outdated content temporarily.
• Email Systems: Email delivery systems often use eventual consistency to ensure that emails are eventually delivered to their recipients, even if there are temporary network delays or server failures.
• Social Media Platforms: Social media feeds often rely on eventual consistency to display updates quickly, even if a user's recent posts might not appear immediately on other users' timelines.

Conclusion:

Choosing the right consistency pattern depends on your application's needs. Strong consistency guarantees accuracy but can be slow. Weak consistency offers high availability but may have temporary inconsistencies. Eventual consistency balances these concerns, ensuring data eventually becomes consistent. Understanding these patterns helps design efficient and reliable distributed systems.