Distributed Systems Design Pattern: Fixed Partitions [Retail Banking's Account Management & Transactions Use?Case]

In retail banking, where high-frequency activities like customer account management and transaction processing are essential, maintaining data consistency and ensuring efficient access are paramount. A robust approach to achieve this involves the Fixed Partitions design pattern, which stabilizes data distribution across nodes, allowing the system to scale effectively without impacting performance. Here’s how this pattern enhances retail banking by ensuring reliable access to customer account data and transactions.

Problem:?

As banking systems grow, they face the challenge of managing more customer data, transaction histories, and real-time requests across a larger infrastructure. Efficient data handling in such distributed systems requires:

1. Even Data Distribution: Data must be evenly spread across nodes to prevent any single server from becoming a bottleneck.
2. Predictable Mapping: There should be a way to determine the location of data for quick access without repeatedly querying multiple nodes.

Consider a system where each customer account is identified by an ID and mapped to a node through hashing. If we start with a cluster of three nodes, the initial assignment might look like this:

As the customer base grows, the bank may need to add more nodes. When new nodes are introduced, the data mapping would normally change for almost every account due to the recalculated node index, requiring extensive data movement across servers. With a new cluster size of five nodes, the mapping would look like this:

Such reshuffling not only disrupts data consistency but also impacts the system’s performance.

Solution: Fixed Partitions with Logical Partitioning

The Fixed Partitions approach addresses these challenges by establishing a predefined number of logical partitions. These partitions remain constant even as physical servers are added or removed, thus ensuring data stability and consistent performance. Here’s how it works:

1. Establishing Fixed Logical Partitions: The system is initialized with a fixed number of logical partitions, such as 8 partitions. These partitions remain constant, ensuring that the mapping of data does not change. Each account or transaction is mapped to one of these partitions using a hashing algorithm, making data-to-partition assignments permanent.
2. Stabilized Data Mapping: Since each account ID is mapped to a specific partition that does not change, the system maintains stable data mapping. This stability prevents large-scale data reshuffling, even as nodes are added, allowing each customer’s data to remain easily accessible.
3. Adjustable Partition Distribution Across Servers: When the bank’s infrastructure scales by adding new nodes, only the assignment of partitions to servers changes. This means the data itself doesn’t move, only the server responsible for managing each partition. As new nodes are added, they inherit portions of the existing partitions, reducing the amount of data that needs to migrate.
4. Balanced Load Distribution: By distributing partitions evenly, the load is balanced across nodes. As additional nodes are introduced, only certain partitions are reassigned, which prevents overloading any single node, maintaining consistent performance across the system.

Example of Fixed Partitions:

Here’s a demonstration of how Fixed Partitions can be applied in a retail banking context, showing the stability of data mapping even as nodes are scaled.

Explanation of Each Column:

• Account ID: Represents individual customer accounts, each with a unique ID.
• Assigned Partition (Fixed): Each account ID is permanently mapped to a logical partition, which remains fixed regardless of changes in the cluster size.
• Initial Server Assignment: Partitions are initially distributed across the original three nodes (Node A, Node B, Node C).
• Server Assignment After Scaling: When two new nodes (Node D and Node E) are added, the system simply reassigns certain partitions to these new nodes. Importantly, the account-to-partition mapping remains unchanged, so data movement is minimized.

This setup illustrates that, by fixing partitions, the system can expand without redistributing the actual data, ensuring stable access and efficient performance.

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