Scaling Microservice Architecture to Handle Increased Traffic

Introduction

As the popularity of Internet banking grows, financial institutions face the challenge of handling an increasing number of user requests per second while maintaining stability and meeting user expectations for fast system response times. Let's consider the example of optimizing the microservice architecture of the hypothetical bank "NeoBanking" online platform to handle increased traffic.

Problem Description

NeoBanking has recently experienced a significant increase in the number of users of its online services. This has raised several significant issues:

1. Request processing capacity - the existing infrastructure can no longer cope with the increasing load, resulting in processing delays and user dissatisfaction.

2. Inefficient resource utilization - vertical scaling of resources (scale up) is limited, and horizontal scaling (scale out) needs to be considered.

3. Database access problem - the increase in transaction intensity causes an increased load on the database, negatively affecting the efficiency of the entire system.

4. Increased network latency - communication between microservices over the network causes additional latency, which becomes particularly noticeable during peak load periods.

Solutions

To address the above problems, NeoBanking took several effective steps. Specifically:

1. Asynchronous request processing - request processing transitioned from a synchronous to an asynchronous model using a Message Queue mechanism:

2. Horizontal scaling using Kubernetes - multiple instances of the coin processing microservice were deployed on a Kubernetes cluster, with the number of instances dynamically increasing or decreasing based on load using the Kubernetes Horizontal Pod Autoscaler:

3. Database horizontal scaling and caching - NeoBanking switched to using a NoSQL database (e.g., Apache Cassandra), which is efficient for storing and processing large volumes of data. Additionally, frequently requested information was cached in a Redis in-memory database, significantly reducing the number of requests to the main database.

4. Low-latency network infrastructure - fiber-optic networks were deployed for inter-server communication, and network accelerator technologies (DPDK, SR-IOV) were utilized. SD-WAN technology was used to connect geographically distant service centers, while CDN was employed to ensure proximity to users.

Practical Example

Let's consider NeoBanking's online platform's Coin Transfer microservice, which was experiencing performance issues as load increased.

Problematic code with synchronous processing of coin transactions:

Optimized version with asynchronous processing and horizontal scaling on Kubernetes:

As a result of these changes, the coin transaction service became asynchronous, enabling efficient operation under high-load conditions. Additionally, using 5 replicas on Kubernetes allowed for serving more users concurrently.

Conclusion

The example of NeoBanking's online platform clearly demonstrates the complexity of properly scaling microservice architecture under increased traffic. Solving these problems requires a comprehensive approach that includes asynchronous request processing, horizontal scaling, data distribution and caching, and network infrastructure optimization.

Effective implementation of these measures is possible with the active use of Docker containerization, Kubernetes orchestration, message queueing systems, NoSQL, and in-memory databases. Continuous system monitoring using tools like Prometheus & Grafana is essential for timely problem identification and optimization.

In addition to technical aspects, establishing the right organizational structure and DevOps culture within the company is of utmost importance. All of this requires effort but is necessary to create a successful and competitive banking service in the era of digital transformation.