Technical Debt in Microservices Architecture

Microservices architecture has gained significant popularity in the banking sector, as it offers modularity, scalability, and independent development. However, this approach also comes with specific technical debt challenges that require careful management. Drawing from our experience with a banking client, we will discuss typical problems and their solutions.

1. Data Consistency and Synchronization Our client's CoreBanking platform consists of multiple microservices, such as Accounts, Transactions, Loans, and Payments. Each service manages its own database, and some data is duplicated across services for optimal performance. As a result, the problem of "eventual consistency" arose - a situation where data changes are not immediately reflected in all related services.

For example, when a user makes a payment through the Payments service, the transaction record is created in the Transactions microservice, but the account balance may not be updated instantly in the Accounts service. This temporary discrepancy can be problematic if the user quickly attempts to perform another transaction.

One solution is to use an Event-Driven architecture for data replication. In this case, each significant event (e.g., payment completion) will trigger the generation of a corresponding event that will propagate to all interested services, ensuring data synchronization. Event streaming platforms like Kafka or Azure Event Grid are well-suited for this purpose.

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1. Transaction Management Closely related to data consistency is the issue of transaction management. In traditional monolithic applications, transactions are often managed at the level of a single database, ensuring ACID properties. However, in a microservices architecture, when a business transaction spans multiple service boundaries, achieving this becomes more challenging.

Let's consider an example from our banking client's system - a money transfer operation. This process requires coordinated changes in several services - deducting the amount from the sender's account (Accounts), creating a new transaction record (Transactions), and crediting the amount to the recipient's account (Accounts). If any step fails, the entire operation should roll back to avoid leaving the system in an inconsistent state.

To address this challenge, the Saga pattern has been implemented - a mechanism for managing transactions using a series of local transactions coordinated by a central "saga orchestrator". If any step fails, the orchestrator initiates compensating operations to revert the system to its initial state.

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1. API Versioning and Schema Evolution Our microservices heavily use REST and gRPC interfaces for communication. Changes to API contracts, if not properly managed, can cause dependent services to malfunction. A shift to a new version by one service may break client applications if they still expect the old API.

To mitigate this problem, we employ API versioning and contract testing. Each significant change in the API is released under a new version (v1, v2, etc.), with support for the old version maintained for an appropriate transition period. This gives client services time to update. We also use tools like Pact or SwaggerHub to validate schema changes and ensure backward compatibility between different API versions.

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1. Resilience and Failure Isolation In a microservices architecture, where services depend on each other over the network, we must be prepared for partial failures. If one service crashes or becomes unavailable, it should not cause a domino effect throughout the system.

Our team has implemented patterns such as Circuit Breaker and Retry policies to protect healthy services and avoid futile transient errors. We use the Polly library for a clean and centralized application of these patterns.

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1. Testing and Monitoring Microservices, by their decentralized nature, make it challenging to fully assess the impact of changes. We have introduced formalized integration and release processes using end-to-end tests that cover typical business scenarios. We also employ simulated events and Chaos Engineering principles to verify the system's resilience and service recovery.

Fragmented logging and the absence of application telemetry make it difficult to determine performance bottlenecks and the root causes of defects. The best solution here was to use a centralized logging infrastructure and correlated request IDs to trace transactions across the system. We also implemented a Grafana and Prometheus system to monitor key metrics and send alerts for any anomalous behavior.

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1. DevOps and CI/CD Microservices architecture often require a shift in DevOps culture. When we have many independent components with different release cycles, automating the release process and having a robust CI/CD pipeline becomes crucial.

With the introduction of test automation, Continuous Integration (CI), and Continuous Delivery (CD), we have significantly improved the frequency and reliability of deployments. Tools like Jenkins, Azure DevOps, or GitLab CI help orchestrate this process.

1. Financial Impact Assessment As noted in the provided information, technical debt has a direct impact on financial outcomes. Accumulated technical debt slows down development, which ultimately delays the delivery of new features and business growth. Also, the slow pace of development due to debt increases operational costs.

Example:

Here's an example to illustrate the financial impact of technical debt in a banking Imagine a bank has a legacy core banking system written in COBOL. Over the years, as the system grew, more and more features were added without proper refactoring and modernization. Now, the system has become a monolith with high coupling and low cohesion. Making changes to one part of the system often causes unexpected issues in other parts.

Let's say the bank wants to introduce a new mobile banking feature that requires changes in the account management module of the core banking system. However, due to the accumulated technical debt, the developers are finding it hard to make these changes without breaking existing functionality.

Here's how this technical debt can translate to financial impact:

1. Delayed Time-to-Market: Due to the complexity caused by technical debt, the development and testing of the new mobile banking feature took much longer than anticipated. If it was planned to take 3 months, it might now take 6 months. This delay in launching the feature means a delay in realizing the expected benefits, such as increased customer satisfaction and potential revenue from fees associated with the feature.

2. Opportunity Cost: While the development team is struggling with the complex codebase, they are unable to work on other strategic initiatives. If the bank had plans to launch other innovative features or improve existing ones, these would have to wait, potentially causing the bank to lose its competitive edge.

3. Increased Operational Costs: The complexity of the codebase also affects the efficiency of operations. It might take longer to resolve issues, leading to increased downtime. More resources might be needed to maintain the system. These inefficiencies translate to increased operational costs.

4. Risk of Failures: With high technical debt, the system becomes more prone to failures. In the worst case, a failure in the account management module could lead to a system-wide outage, preventing customers from accessing their accounts. Such incidents can lead to direct financial losses (compensations to customers), regulatory fines, and reputational damage.

5. Increased Cost of Future Changes: As more and more features are added to the already complex system, the cost of future changes keeps growing. What could have been a simple change if the system was well-structured, now becomes a complex and risky endeavor.

Here's a hypothetical calculation:

- The new mobile banking feature was expected to bring in an additional revenue of $500,000 per month.

- Due to the delay caused by technical debt, the launch is delayed by 3 months. That's a loss of $1,500,000 in potential revenue.

- The development effort, which was budgeted at $300,000, has now doubled to $600,000 due to the complexity.

- An outage caused by the changes leads to $100,000 in compensation to customers and a $50,000 regulatory fine.

In this scenario, the total quantifiable financial impact of technical debt is $2,250,000.

Of course, this is a simplified example, and the actual costs would vary based on the specifics of the situation. However, it illustrates how technical debt, if not managed properly, can have significant financial implications for a bank.

The main goal of completely eliminating technical debt is not. The key is to achieve an optimal balance between system reliability, development speed, and financial efficiency. This requires strategic management of technical debt:

1. Creating a debt portfolio: Identifying and cataloging all significant technical debts, and assessing their potential impact.
2. Prioritizing debts: Identifying debts with the highest "interest rates", which hinder development the most or carry substantial risks.
3. Developing a repayment strategy: Deciding when and how each debt should be repaid, considering both the cost of repayment and the expected benefit.
4. Balancing investments: Along with paying off technical debt, it's necessary to invest in activities that increase revenue or reduce costs in the long term.
5. Monitoring progress: Regularly assessing the results achieved and adapting the strategy as needed.

Conclusion

Managing technical debt, especially in a microservices architecture, is a complex task that requires balancing engineering and business perspectives. It involves identifying debts, prioritizing them, strategically repaying them, and preventing new debts.

At the same time, it's important not to focus solely on eliminating debt but also to make investments that promote sustainable development, such as automation, monitoring, security, and quality control.

With the right approach, technical debt can become not a burden but a strategic tool that allows us to quickly deliver value to customers while managing the complexity of the codebase and ensuring the system's long-term health.

It's the art and science of maintaining a balance between short-term speed and long-term sustainability - a challenge that requires technical leaders to possess both technical knowledge and a deep understanding of business needs.

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