Monthly Archives: October 2024

Distributed Systems Design Pattern: Fixed Partitions [Retail Banking’s Account Management &…

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

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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 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.

In a distributed insurance system where handling vast amounts of data efficiently and consistently is essential, the Consistent Core pattern provides an ideal solution. 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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Distributed Systems Design Pattern: Request Waiting List [Capital Markets Use Case]

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Problem Statement:

In a distributed capital markets system, client requests like trade executions, settlement confirmations, and clearing operations must be replicated across multiple nodes for consistency and fault tolerance. Finalizing any operation requires confirmation from all nodes or a Majority Quorum.

For instance, when a trade is executed, multiple nodes (such as trading engines, clearing systems, and settlement systems) must confirm the transaction’s key details — such as the trade price, volume, and counterparty information. This multi-node confirmation ensures that the trade is valid and can proceed. These confirmations are critical to maintaining consistency across the distributed system, helping to prevent discrepancies that could result in financial risks, errors, or regulatory non-compliance.

However, asynchronous communication between nodes means responses arrive at different times, complicating the process of tracking requests and waiting for a quorum before proceeding with the operation.

“The following diagram illustrates the problem scenario, where client trade requests are propagated asynchronously across multiple nodes. Each node processes the request at different times, and the system must wait for a majority quorum of responses to proceed with the trade confirmation.”

Solution: Request Waiting List Distributed Design Pattern

The Request Waiting List pattern addresses the challenge of tracking client requests that require responses from multiple nodes asynchronously. It maintains a list of pending requests, with each request linked to a specific condition that must be met before the system responds to the client.

Definition:

The Request Waiting List pattern is a distributed system design pattern used to track client requests that require responses from multiple nodes asynchronously. It maintains a queue of pending requests, each associated with a condition or callback that triggers when the required criteria (such as a majority quorum of responses or a specific confirmation) are met. This ensures that operations are finalized consistently and efficiently, even in systems with asynchronous communication between nodes.

Application in Capital Markets Use Case:

“The flowchart illustrates the process of handling a trade request in a distributed capital markets system using the Request Waiting List pattern. The system tracks each request and waits for a majority quorum of responses before fulfilling the client’s trade request, ensuring consistency across nodes.”

1. Asynchronous Communication in Trade Execution:

  • When a client initiates a trade, the system replicates the trade details across multiple nodes (e.g., order matching systems, clearing systems, settlement systems).
  • These nodes communicate asynchronously, meaning that responses confirming trade details or executing the order may arrive at different times.

2. Tracking with a Request Waiting List:

  • The system keeps a waiting list that maps each trade request to a unique identifier, such as a trade ID or correlation ID.
  • Each request has an associated callback function that checks if the necessary conditions are fulfilled to proceed. For example, the callback may be triggered when confirmation is received from a specific node (such as the clearing system) or when a majority of nodes have confirmed the trade.

3. Majority Quorum for Trade Confirmation:

  • The system waits until a majority quorum of confirmations is received (e.g., more than half of the relevant nodes) before proceeding with the next step, such as executing the trade or notifying the client of the outcome.

4. Callback and High-Water Mark in Settlement:

  • In the case of settlement, the High-Water Mark represents the total number of confirmations required from settlement nodes before marking the transaction as complete.
  • Once the necessary conditions — such as price match and volume confirmation — are satisfied, the callback is invoked. The system then informs the client that the trade has been successfully executed or settled.

This approach ensures that client requests in capital markets systems are efficiently tracked and processed, even in the face of asynchronous communication. By leveraging a waiting list and majority quorum, the system ensures that critical operations like trade execution and settlement are handled accurately and consistently.

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Distributed Systems Design Pattern: Clock-Bound Wait with Banking Use Case

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The following diagram provides a complete overview of how the Clock-Bound Wait pattern ensures consistent transaction processing across nodes. Node A processes a transaction and waits for 20 milliseconds to account for clock skew before committing the transaction. Node B, which receives a read request, waits for its clock to catch up before reading and returning the updated value.

In distributed banking systems, ensuring data consistency across multiple nodes is critical, especially when transactions are processed across geographically dispersed regions. One major challenge is that system clocks on different nodes may not always be synchronized, leading to inconsistent data when updates are propagated at different times. The Clock-Bound Wait pattern addresses these clock discrepancies and ensures that data is consistently ordered across all nodes.

The Problem: Time Discrepancies and Data Inconsistency

In a distributed banking system, when customer transactions such as deposits and withdrawals are processed, the local node handling the transaction uses its system clock to timestamp the operation. If the system clocks of different nodes are not perfectly aligned, it may result in inconsistencies when reading or writing data. For instance, Node A may process a transaction at 10:00 AM, but Node B, whose clock is lagging, could still show the old account balance because it hasn’t yet caught up to Node A’s time. This can lead to confusion and inaccuracies in customer-facing data.

As seen in the diagram below, the clocks of various nodes in a distributed system may not be perfectly synchronized. Even a small time difference, known as clock skew, can cause nodes to process transactions at different times, resulting in data inconsistency.

Clock-Bound Wait: Ensuring Correct Ordering of Transactions

To solve this problem, the Clock-Bound Wait pattern introduces a brief waiting period when processing transactions to ensure that all nodes have advanced past the timestamp of the transaction being written or read. Here’s how it works:

Maximum Clock Offset: The system first calculates the maximum time difference, or offset, between the fastest and slowest clocks across all nodes. For example, if the maximum offset is 20 milliseconds, this value is used as the buffer for synchronizing data.

Waiting to Guarantee Synchronization: When Node A processes a transaction, it waits for a period (based on the maximum clock offset) to ensure that all other nodes have moved beyond the transaction’s timestamp before committing the change. For example, if Node A processes a transaction at 10:00 AM, it will wait for 20 milliseconds to ensure that all nodes’ clocks are past 10:00 AM before confirming the transaction.

The diagram illustrates how a transaction is processed at Node A, with a controlled wait for 20 milliseconds to allow other nodes (Node B and Node C) to synchronize their clocks before the transaction is committed across all nodes. This ensures that no node processes outdated or incorrectly ordered transactions.

Consistent Reads and Writes: The same waiting mechanism is applied when reading data. If a node receives a read request but its clock is behind the latest transaction timestamp, it waits for its clock to synchronize before returning the correct, updated data.

The diagram illustrates how a customer request for an account balance is handled. Node B, with a clock lagging behind Node A, must wait for its clock to synchronize before returning the updated balance, ensuring that the customer sees the most accurate data.

Eventual Consistency Without Significant Delays: Although the system introduces a brief wait period to account for clock discrepancies, the Clock-Bound Wait pattern allows the system to remain eventually consistent without significant delays in transaction processing. This ensures that customers experience up-to-date information without noticeable latency.

The diagram below demonstrates how regional nodes in different locations (North America, Europe, and Asia) wait for clock synchronization to ensure that transaction updates are consistent across the entire system. Once the clocks are in sync, final consistency is achieved across all regions.

Application in Banking Systems

In a distributed banking system, the Clock-Bound Wait pattern ensures that account balances and transaction histories remain consistent across all nodes. When a customer performs a transaction, the system guarantees that the updated balance is visible across all nodes after a brief wait period, regardless of clock discrepancies. This prevents situations where one node shows an outdated balance while another node shows the updated balance.

The Clock-Bound Wait pattern is a practical solution for managing clock discrepancies in distributed banking systems. By introducing a brief wait to synchronize clocks across nodes, the pattern ensures that transactions are consistently ordered and visible, maintaining data accuracy without significant performance overhead. This approach is particularly valuable in high-stakes industries like banking, where consistency and reliability are paramount.

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