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Reliability in System Design
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Consistency in System Design

Last Updated : 15 Apr, 2025
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Consistency in system design refers to the property of ensuring that all nodes in a distributed system have the same view of the data at any given point in time, despite possible concurrent operations and network delays. In simpler terms, it means that when multiple clients access or modify the same data concurrently, they all see a consistent state of that data.

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Table of Content

  • Importance of Consistency in System Design
  • Types of Consistency
  • Challenges with maintaining Consistency
  • Strategies for achieving Consistency
  • Roadmap to understand Consistency

Importance of Consistency in System Design

Consistency plays a crucial role in system design for several reasons:

  • Correctness: Consistency guarantees that the information accessible by various system components is always correct and up-to-date. This is necessary to guarantee that the system operates as planned and generates accurate outcomes.
  • Reliability: Because they lower the possibility of mistakes and inconsistencies that could result in unpredictable behavior or corrupted data, consistent systems are more dependable. Users may rely on the system to deliver reliable and accurate results.
  • Data Integrity: The integrity of the data kept in the system is preserved by consistency. Consistency aids in preventing data loss and corruption by guaranteeing that all changes are applied and distributed appropriately.
  • Concurrency Control: Consistency strategies help with access control to prevent conflicts and ensure that changes are applied in a coordinated way in distributed or multi-user systems, where multiple clients may access and modify the same data at the same time.
  • User Experience: Because it makes system interaction predictable and smooth, consistency improves the user experience. The system is reliable in providing users with current and logical information, which increases user happiness and usefulness.

Types of Consistency

Types-of-Consistency

1. Strong Consistency

Strong Consistency also known as linearizability or strict consistency, this type guarantees that every read operation receives the most recent write operation's value or an error. It ensures that all clients see the same sequence of updates and that updates appear to be instantaneous. Achieving strong consistency often requires coordination and synchronization between distributed nodes, which can impact system performance and availability.

Example:

A traditional SQL database system with a single master node and multiple replicas ensures strong consistency. When a client writes data to the master node, subsequent reads from any replica will immediately reflect the latest value written. All replicas are updated synchronously, ensuring that all clients see a consistent view of the data.

2. Eventual Consistency

Eventual consistency guarantees that data replicas will eventually converge to the same value even while it permits them to diverge briefly. It improves availability and performance in distributed systems by loosening the consistency requirements. Even though it could result in short-term inconsistencies, eventual consistency ensures that all modifications will eventually be shared and balanced.

Example:

Amazon's DynamoDB, a distributed NoSQL database, provides eventual consistency. When data is written to DynamoDB, it is initially stored locally on a single node and then asynchronously propagated to other nodes in the system. While clients may read slightly outdated values immediately after a write, all replicas eventually converge to the same value over time.

3. Causal Consistency

Causal consistency preserves the causality between related events in a distributed system. If event A causally precedes event B, all nodes in the system will agree on this ordering. Causal consistency ensures that clients observing concurrent events maintain a consistent view of their causality relationship, which is essential for maintaining application semantics and correctness.

Example:

A collaborative document editing application, where users can concurrently make edits to different sections of a document, requires causal consistency. If user A makes an edit that depends on the content written by user B, all users should observe these edits in the correct causal order. This ensures that the document remains coherent and maintains the intended meaning across all users.

4. Weak Consistency

Among consistency models, weak consistency offers the least amount of assurance. It just ensures that updates will eventually spread to every duplicate, even though it permits significant differences between them. Weak consistency does not guarantee when replicas will converge, in contrast to eventual consistency, which assures convergence. Rather, it permits simultaneous updates and could lead to short-term discrepancies. In systems where high availability and low latency are more important than tight consistency, weak consistency is frequently employed.

Example:

A distributed caching system, such as Redis or Memcached, often implements weak consistency. In such systems, data is stored and retrieved quickly from an in-memory cache, but updates may be asynchronously propagated to other nodes. This can lead to temporary inconsistencies where clients may observe old or divergent values until updates are fully propagated.

5. Read-your-Writes Consistency

This type of consistency guarantees that after a client writes a value to a data item, it will always be able to read that value or any subsequent value it has written. It provides a stronger consistency guarantee for individual clients, ensuring that they observe their own updates immediately. Read-your-writes consistency is important for maintaining session consistency in applications where users expect to see their own updates reflected immediately.

Example:

A social media platform ensures read-your-writes consistency for users' posts and comments. After a user publishes a new post or comment, they expect to immediately see their own content when viewing their timeline or profile. This consistency model ensures that users observe their own updates immediately after performing a write operation.

6. Monotonic Consistency

Monotonic consistency ensures that if a client observes a particular order of updates (reads or writes) to a data item, it will never observe a conflicting order of updates. Monotonic consistency prevents the system from reverting to previous states or seeing inconsistent sequences of updates, which helps maintain data integrity and coherence.

Example:

A distributed key-value store maintains monotonic consistency by guaranteeing that once a client observes a particular sequence of updates, it will never observe a conflicting sequence of updates. For instance, if a client reads values A, B, and C in that order, it will never later observe values C, A, and B.

7. Monotonic Reads and Writes

These consistency guarantees ensure that if a client performs a sequence of reads or writes, it will observe a monotonically increasing sequence of values or updates. Monotonic reads ensure that clients never see older values in subsequent reads, while monotonic writes guarantee that writes from a single client are applied in the same order on all replicas.

Example:

Google's Spanner, a globally distributed relational database, ensures monotonic reads and writes consistency. When a client reads or writes data, it observes a monotonically increasing sequence of values or updates. This guarantees that clients always see the most recent data and that writes are applied in the same order across all replicas.

Challenges with maintaining Consistency

  1. Coordination Overhead: Coordination between distributed nodes is frequently necessary for consistency, which adds overhead as the system grows. System scalability may be impacted by synchronous coordination techniques that create bottlenecks, such as distributed locking or two-phase commit protocols.
  2. Latency: Latency may increase if strong consistency models need to await acknowledgments from several nodes before finishing a write operation. The user experience may suffer when this delay increases as the system grows physically or in terms of the number of customers.
  3. Operational Complexity: Ensuring consistency often involves configuring and managing complex distributed systems. Human error in configuring replication settings, consistency levels, or coordination mechanisms can lead to data inconsistencies or performance issues.
  4. Data Synchronization: Strong synchronization methods are necessary to guarantee data consistency across many platforms and devices. Device-specific limitations, network latency, and asynchronous updates might make it more difficult to consistently synchronize data across platforms.
  5. Concurrency Control: Coordinating concurrent access to shared data across different platforms while maintaining consistency requires careful design and implementation of concurrency control mechanisms.

Strategies for achieving Consistency

In distributed systems, achieving consistency requires the use of a number of strategies, such as best practices, consistency models, design patterns, and dispute resolution methods. The basic outline of each is as follows:

1. Design Patterns and Best Practices

  • Single Source of Truth: Design systems with a single authoritative source of truth for critical data. This reduces the potential for inconsistencies arising from multiple conflicting sources.
  • Unchanged Operations: Design operations that can be applied multiple times without changing the result. Idempotent operations are essential for ensuring consistency in the face of network failures and retries.
  • Versioning: Implement versioning mechanisms for data objects to track changes over time. Versioning helps in detecting conflicts and resolving inconsistencies.
  • Asynchronous Updates: Use asynchronous communication patterns to decouple components. By enabling components to handle updates independently, asynchronous updates lower congestion and increase scalability.

2. Consistency Models

  • Eventual Consistency: In situations where instant consistency is not necessary, accept eventual consistency. Allow a brief variation between replicas while guaranteeing their eventual convergence to a consistent state.
  • Strong Consistency: Utilize strong consistency models when strict consistency is necessary for correctness, such as in financial transactions or critical system operations. Ensure that all updates are immediately visible to all clients.
  • Causal Consistency: Apply causal consistency for preserving causal relationships between events in distributed systems. Ensure that events causally related are observed in the correct order across all replicas.

3. Conflict Resolution Techniques

  • Last-Writer-Wins (LWW): Resolve conflicts by favoring the update with the latest timestamp or version. LWW is a simple conflict resolution strategy but may lead to data loss or inconsistency in some scenarios.
  • Merge Strategies: Use custom merge strategies or conflict resolution algorithms tailored to the specific requirements of the application domain. Merge strategies reconcile conflicting updates based on application-specific semantics and user preferences.

Roadmap to understand Consistency

1. Introduction to Consistency

  • Consistency in System Design
  • Consistency Patterns
  • CAP Theorem

2. Types of Consistency Models

  • Strong Consistency
  • Eventual Consistency
  • Weak Consistency
  • Sequential Consistency
  • Causal Consistency
  • Read-your-Writes Consistency
  • Monotonic Reads Consistency
  • Monotonic Writes Consistency

3. Techniques to Achieve Consistency

  • Quorum in System Design
  • Paxos Algorithm
  • Vector Clocks in Distributed Systems

4. Advance Concepts and Tradeoffs

  • Eventual consistency between Microservices
  • Does MongoDB use Eventual Consistency?
  • Does Redis have Eventual Consistency?
  • Consistency vs. Availability
  • Weak vs. Eventual Consistency
  • Strong vs. Eventual Consistency
  • Difference between Soft State and Eventual Consistency
  • Measurement of Eventual Consistency



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