In the older days, when databases had a single source of truth for all data, the existence of replicas wasn't a problem because they simply didn't exist.
However, in today's systems, where data is replicated across multiple nodes to enhance processing and storage capacity, the challenge arises. Scaling databases across multiple nodes is common for performance, and replicating data helps eliminate single points of failure, ensuring that data is highly available for each node. But ‘How can we make sure that the data is consistent across all the nodes in a distributed system?’.
In a distributed network, we can achieve data synchronization or consistency through two approaches:
a. Strong consistency and
b. Eventual consistency.
Strong consistency
In a system offering strong consistency, every node in the distributed system observes the same data at the same time. Each read operation fetches the most recent write. This ensures that after a write is acknowledged, all subsequent reads will yield the value of that particular write.
When a client initiates a write request, the request is transmitted to a server within the distributed network. This server then disseminates the request to other servers in the distributed cluster to execute the update. After receiving acknowledgments of the update from all the other servers, the originating server sends a completion acknowledgment to the client. Following this, any client can access the latest available update. However, a downside of this protocol is that a client might experience significant delays waiting for the acknowledgment to proceed.
If ensuring data consistency is vital for your application, particularly in situations such as financial transactions, choosing strong consistency systems is recommended.
Advantages
a. Ensures a dependable and consistent data state.
b. Simplifies application logic, relieving developers from managing potential data discrepancies.
c. Enhances user experience by guaranteeing that everyone observes the same information consistently.
Disadvantages
a. May result in performance overhead because of the necessity to synchronize data across all nodes before confirming a write.
b. Could impact scalability, as maintaining strong consistency across numerous nodes becomes more challenging.
Example
Classic relational databases such as MySQL and PostgreSQL ensure strong consistency through the implementation of ACID transactions.
Eventual consistency
In a system with eventual consistency, updates eventually reach all nodes, but there might be a temporary delay between a write happening and it being reflected everywhere. This implies that read operations might not always immediately fetch the latest data.
Choosing eventual consistency systems is an option when prioritizing low-latency and high availability over immediate consistency.
When can a client receive acknowledgement in eventual consistence system?
In an eventually consistent system, after making a change to the data, the system doesn't ensure that all copies will instantly show that change. Instead, it permits time for the update to spread throughout the distributed system. The confirmation of a successful update might arrive at varying times for different copies, influenced by factors such as network latency, system load, and the specific consistency model in operation.
Pros
a. Provides better performance and scalability in contrast to strong consistency.
b. Can efficiently manage extensive data volumes and geographically distributed systems.
Cons
a. Information may be momentarily inconsistent among various nodes, introducing possible disparities.
b. Developers must write code to manage potential inconsistencies and guarantee data integrity.
c. Debugging and resolving application problems may pose greater challenges due to data inconsistencies.
Example
Several distributed NoSQL databases, including Cassandra and DynamoDB, function under the principle of eventual consistency.
How to Choose between strong and eventual consistency?
The decision between strong and eventual consistency involves balancing considerations of data accuracy, performance, and scalability. The following guidelines can assist you in selecting the option that aligns with your specific needs:
Application Requirements: Strong consistency is crucial for applications where data accuracy and consistency are paramount, such as financial transactions.
Performance Needs: Applications with high data throughput or users spread across different geographical locations might find the performance and scalability benefits of eventual consistency advantageous.
Developer Expertise: Implementing applications for eventual consistency necessitates careful consideration of potential data inconsistencies and may require additional programming effort.
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