Distributed Design Pattern: Consistent Core [Insurance Use?Case]

In the insurance industry, managing large volumes of data and critical operations such as policy management, claims processing, and premium adjustments requires high consistency, fault tolerance, and performance. When dealing with distributed systems, ensuring that data remains consistent across nodes can be challenging, especially when operations must be fault-tolerant. As these systems grow, maintaining strong consistency becomes increasingly complex. This is where the Consistent Core design pattern becomes essential.

Problem: Managing Consistency in Large Insurance Data?Clusters

As insurance companies scale, they need to handle more customer data, policy updates, and claims across distributed systems. Larger clusters of servers are necessary to manage the massive amounts of data, but these clusters also need to handle critical operations that require strong consistency and fault tolerance, such as processing claims, updating policies, and managing premium adjustments.

Problem Example:

Take the example of an insurance company, InsureX, which handles thousands of claims, policies, and customer data across a large distributed system. Let’s say a customer submits a claim:

• The claim is submitted to the system, and it must be replicated across several nodes responsible for policyholder data, claims processing, and financial information.
• The system relies on quorum-based algorithms to ensure all nodes have consistent information before processing the claim. However, as the system grows and the number of nodes increases, the performance degrades due to the time it takes for all nodes to reach consensus.
• As a result, InsureX experiences slower performance in claims processing, delays in policy updates, and overall dissatisfaction among policyholders.

In larger systems, quorum-based algorithms introduce delays, especially when a majority of nodes must agree before an operation is completed. This makes the system inefficient when dealing with high transaction volumes, as seen in large insurance data clusters. So, how do we ensure strong consistency and maintain high performance as the system scales?

Solution: Implementing a Consistent Core

The Consistent Core design pattern solves this problem by creating a smaller cluster (usually 3 to 5 nodes) that handles key tasks requiring strong consistency. This smaller cluster is responsible for ensuring consistency in operations such as policy updates, claims processing, and premium adjustments, while the larger cluster handles bulk data processing.

Solution Example:

In the InsureX example, the company implements a small, consistent core to handle the critical tasks, separating the heavy data processing load from the operations that require strong consistency. Here’s how it works:

Consistent Core for Metadata Management:

• The small consistent core handles tasks like claims updates, policyholder data, and premium adjustments. This cluster ensures that operations needing strong consistency (such as policy renewals) are processed without waiting for the entire cluster to reach a consensus.

Separation of Data and Metadata:

• The large cluster continues to handle the bulk of data processing, including the storage of customer records, claims history, and financial transactions. The consistent core ensures that metadata-related tasks, like updating claims status or policyholder information, are consistent across the system.

Fault Tolerance:

• The consistent core uses quorum-based algorithms to ensure that even if one or two nodes fail, the system can continue to process critical tasks such as claims approvals or policy renewals.

By offloading these critical consistency tasks to a smaller cluster, InsureX ensures that policy updates, claims processing, and premium calculations are completed reliably and efficiently, without relying on the performance-degrading quorum consensus across the entire system.

Using Quorum-Based Algorithms in Claims Processing

One key area where the Consistent Core pattern shines is in claims processing. When a customer files a claim, the system must ensure the information is replicated accurately across nodes responsible for financial calculations, policyholder data, and claim approvals.

Example:

Let’s say a customer submits an accident claim. The system processes this claim by sending it to multiple nodes, and a majority quorum must confirm the claim before it is approved. The system tracks how many nodes confirm the claim and waits until at least two of the three relevant nodes agree.

• Node 1 (Financial Calculations) agrees on the claim.
• Node 2 (Policyholder Data) agrees on the claim.
• Node 3 (Claims Approval) delays its response.

Once a quorum is reached, the claim is processed and approved.

This ensures that claims are processed efficiently and consistently, even if some nodes are delayed or experiencing issues. The Consistent Core ensures that these critical tasks are handled without compromising performance.

Using Leases for Premium Adjustments

Another practical application of the Consistent Core pattern is in premium adjustments and policy renewals. The system can use leases to temporarily manage premium adjustment operations across the distributed system.

Example:

When a large-scale premium adjustment is needed, the Consistent Core temporarily “holds a lease” over the operation. This allows the core to coordinate premium adjustments, ensuring that all related operations are synchronized across the system. Once the adjustment is completed, the lease is released.

The lease mechanism ensures that complex operations like premium adjustments are handled smoothly, without requiring quorum-based decisions across the entire cluster. This reduces operational delays and ensures consistency.

The Consistent Core pattern provides an ideal solution in a distributed insurance system where handling vast amounts of data efficiently and consistently is essential. By separating the management of critical metadata and operations from the bulk data processing, the insurance company can ensure that operations such as policy updates, claims processing, and premium adjustments are completed quickly, accurately, and consistently.

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