Strong Consistency in System Design

Consistency in a system refers to the similarity and consistency of data and behavior across all its components. This ensures that users encounter the same version of the data and experience the expected system performance, regardless of which part of the system they interact with. Maintaining this uniform and reliable experience is vital for user satisfaction. Consistency in system design plays an important role in ensuring reliability, predictability, and correctness in software and hardware systems.

Importance of Data Consistency in Systems

The system works in a trustworthy and predictable manner when the data is consistent. Regardless of when or how they access it, users expect the system to provide consistent results. Maintaining data consistency helps in preserving data integrity. It can be challenging to trust the accuracy and completeness of the information stored in the system when there is inconsistent data because it can result in data corruption.

Users can trust that the information they’re interacting with is correct and current when data is consistent. This encourages positive user experiences and builds confidence in the integrity of the system. Consider an online store where prices for products vary depending on the device. Decision-making becomes difficult and user trust is undermined by these inconsistencies.

What is Strong Consistency?

The highest degree of data consistency is achieved by strong consistency, but in a distributed system, this may come at the cost of higher latency and coordination overhead.

In distributed systems, strong consistency ensures that, regardless of where a node accesses the data, it is always visible to all nodes at the same time.

This prevents any short-term inconsistencies because any changes made to the data are instantly reflected across all nodes. This indicates that, as if the system were a single, in order consistent entity, all read and write operations seem to happen instantly and in a linearizable order.

• Read consistency: Any read operation on the data will return the most recent write value or a value that satisfies specific consistency guarantees
• Write consistency: When a write operation is performed, the data will be propagated to all relevant nodes in the system, ensuring that all replicas are updated with the latest value before the operation is considered successful.

For example:

Banks and financial institutions operate on distributed databases to manage customer accounts, transactions, and other financial activities. Strong consistency ensures that all updates to these databases are immediately reflected across all nodes in the distributed system.

Characteristics of Strong Consistency

1. Linearizability

Atomic consistency, or linearizability, is the property that guarantees all operations appear to happen instantaneously and atomically at a single point in time. It provides a global order for all operations that is consistent with a linear timeline, ensuring that every read operation returns either the most recent write or an error.

2. Synchronization

strong consistency calls for synchronization mechanisms, in order to ensure that every replica or node in the distributed system is updated simultaneously before any read operation. By guaranteeing that every node has an identical representation of the data, it removes the chance of old reads or inconsistent states amongst replicas.

3. Instantaneous Visibility

Any write operation is guaranteed to become instantly visible to all following read operations with immediate visibility. It ensures that all clients or system nodes can view the updated data immediately upon the completion of a write operation.

4. Consistency Guarantees

Among the various consistency models, strong consistency offers the strongest level of consistency guarantees. Regardless of the sequence in which operations are performed or the divisions of the network, it guarantees that every replica or node in the distributed system agrees on the current state of the data.

5. Simplicity and Predictability

A clear and easy-to-understand model for reasoning about distributed systems is provided by strong consistency. It makes it simpler to understand and predict the behavior of the system by replicating the sequential execution of operations found in traditional single-node systems.

Strong Consistency Comparison with Other Consistency Models

Consistency Model	Description	Key Characteristics	Advantages	Disadvantages
Strong Consistency	Ensures that all replicas or nodes in the distributed system have the same view of the data at any given time.	Linearizability, Synchronization, Instantaneous Visibility, Consistency Guarantees, Simplicity and Predictability	Provides the strongest level of data consistency.	Higher latency due to synchronization mechanisms and Reduced availability under network partitions.
Eventual Consistency	Allows replicas to diverge temporarily and resolves conflicts asynchronously.	Eventual Convergence, Asynchronous Conflict Resolution, Weaker Consistency Guarantees	Improved availability and partition tolerance.	May lead to temporarily inconsistent states. Also Requires conflict resolution mechanisms.
Sequential Consistency	Preserves the order of operations from each client but doesn’t guarantee a global order of operations.	Client, Specific Order, No Global Order	Simplicity in reasoning about data consistency.	May allow for inconsistencies between clients and does not ensure global ordering of operations.
Causal Consistency	Preserves causal relationships between operations, allowing some operations to be reordered as long as they are causally related.	Causal Relationship Preservation, Some Operations May Be Reordered	Allows for more flexibility than strong consistency.	Requires understanding of causal relationships between operations.
Eventual Consistency	A refined version of eventual consistency that ensures convergence of replicas within a specified time frame.	Eventual Convergence within a Specified Time Frame, Asynchronous Conflict Resolution, Weaker Consistency Guarantees	Improved predictability compared to eventual consistency, also balances consistency and availability.	Still requires conflict resolution mechanisms. It may not provide strong consistency guarantees within the time frame.

Types of Strong Consistency

There are two main types of strong consistency:

Sequential consistency

This is the strictest form of consistency, and it guarantees that all reads and writes appear to have been executed in a sequential order, even if they were issued from different processes or nodes in the system. In other words, it ensures that the outcome of any execution is the same as if all operations were executed one after the other in some sequential order.

Linearizability

This is a slightly weaker form of consistency than sequential consistency. It guarantees that each operation appears to take effect instantaneously at some point in time, and that all processes agree on the order in which operations occurred. Linearizability is often considered to be the gold standard for consistency in distributed systems, but it can be more difficult to achieve than sequential consistency.

Ways to achieve Strong Consistency

These are some common types or approaches to achieving strong consistency in distributed systems. Each approach may have its own advantages, disadvantages, and trade-offs depending on the specific requirements and constraints of the system.

1. Linearizability (Atomic Consistency)

A specific type of strong consistency known as linearizability makes sure that every operation appears to happen instantly and atomically at a single point in time. It provides a global order for all operations consistent with a linear timeline, ensuring that each read operation returns either the most recent write or an error.

2. Strong Eventual Consistency

An approach that blends the ideas of eventual consistency and strong consistency is known as strong eventual consistency. It guarantees that replicas will eventually reach a consistent state in a certain period of time. Over time, it offers strong consistency guarantees, even though it might not do so immediately.

3. Synchronous Replication

By guaranteeing that all write operations are replicated to all replicas before being acknowledged to the client, synchronous replication is a technique used to achieve strong consistency. Strong consistency is ensured by this method, which makes sure that every replica always sees the data in the same way.

4. Quorum-based Protocols

Strong consistency in distributed systems is typically ensured by quorum-based protocols like Paxos or Quorum Consensus. To ensure strong consistency across replicas, these protocols require a particular quorum of replicas to agree on the ordering of operations before they are considered committed.

5. Distributed Locking

In a distributed system, mutual exclusion and preventing simultaneously access to shared resources can be achieved through distributed locking mechanisms. Nodes can coordinate their access to data and uphold strong consistency by obtaining and releasing locks.

6. Two-phase Commit

A distributed algorithm called the two-phase commit protocol is used to guarantee atomicity and consistency in distributed transactions. Strong consistency is ensured by ensuring that, prior to a transaction being committed or terminated, all replicas agree on its outcome.

Challenges with Strong Consistency

• Performance: Maintaining consistent data across multiple nodes requires additional communication and coordination, impacting performance.
• Complexity: Implementing and maintaining strong consistency protocols can be complex and require significant expertise.
• Scalability: As the number of nodes increases, the cost of maintaining consistency can become prohibitive.
• Availability: Strong consistency guarantees can be difficult to maintain in the presence of network failures or node outages.
• Increased Latency: Synchronization mechanisms required for strong consistency introduce latency in read and write operations, impacting overall system performance.
• Reduced Availability: Under network partitions or failures, ensuring strong consistency may result in reduced availability as nodes may need to wait for synchronization before serving read requests.

Example of Strong Consistency

Below is the example to understand the Strong Consistency:

• User 1 sends a write request to Node C to update Val1 to 100.
• Node C receives the request, updates the value to 100, and propagates the update to Nodes A, B and D.
• The response from Node C is sent back to User 1
• Now the User 2 immediately sends a read request to Node D to get the value of Val1.
• Since the system follows strong consistency, Node D has already received the update from Node C and reflects the latest value of Val1, which is 100.
• Node D responds to User 2 with the value 100.

Impact of Strong Consistency on System Performance, Scalability and Availability

• System Performance: Strong consistency can impact system performance due to increased latency caused by synchronization mechanisms. However, the impact may vary depending on the implementation and workload.
• Scalability: Achieving strong consistency may impose scalability challenges, especially in large-scale distributed systems, as synchronization mechanisms can introduce bottlenecks and limit scalability.
• Availability: Strong consistency may negatively impact availability, particularly in the presence of network partitions or failures. Nodes may need to wait for synchronization, leading to increased response times or unavailability during these events.