CC BY
22КОМПЬЮТЕРНЫЕ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ
(COMPUTER & INFORMATION TECHNOLOGIES)
УДК 004
Zarubaika Andrei
Senior Development Manager, Bank and ERP API connectivity, Kyriba Corp, www.kyriba.com (Miami, Florida, United States of America)
TECHNIQUES AND STRATEGIES FOR HANDLING HIGH TRAFFIC
Abstract: the global fintech market is projected to experience significant growth, with a compound annual growth rate (CAGR) of 23.8% from 2021 to 2028, reaching a market size of USD 305.7 billion. As the demand for fintech services rises, the need for fintech APIs to facilitate integration between financial applications also increases. However, handling high traffic loads presents challenges to fintech companies, including performance issues, downtime, and service disruptions. This article explores various techniques and strategies for scaling fintech APIs to address these challenges. Caching, load balancing, auto-scaling, API gateways, asynchronous processing, distributed databases, and monitoring are discussed as key approaches to handle high traffic loads. Caching, implemented at different levels, accelerates API response times by storing frequently accessed data. Load balancing distributes traffic across multiple servers, improving performance and handling increased loads. Auto-scaling adjusts infrastructure capacity based on incoming traffic patterns, ensuring optimal resource utilization. API gateways act as reverse proxies, managing authentication, rate limiting, and caching, providing a centralized entry point for APIs. Asynchronous processing techniques, such as message queues and event-driven architectures, enable servers to handle multiple requests simultaneously. Distributed databases ensure scalability and fault tolerance by distributing data across multiple servers. To make informed decisions, the article provides a comparison of leading API gateways and highlights the top databases based on a StackOverflow survey. Additionally, it emphasizes the importance of choosing a logging and monitoring solution separately from the scaling infrastructure, with a focus on long-term cost estimation and avoiding performance impact. By implementing these techniques and strategies, fintech companies can deliver seamless and reliable services to customers even during high traffic periods. Investing in scalable and reliable infrastructure becomes crucial for staying ahead in the growing fintech market.
Keywords: fintech, API scaling, caching, load balancing, auto-scaling, API gateways, asynchronous processing, distributed databases, monitoring.
According to recent studies, the global fintech market is expected to grow at a compound annual growth rate (CAGR) of 23.8% from 2021 to 2028, reaching a market size of USD 305.7 billion by 2028 [1]. As the demand for fintech services continues to grow, so does the demand for fintech APIs, which enable seamless integration between different financial applications.
However, with the increase in demand for fintech APIs comes the challenge of handling high traffic loads. Without proper scaling techniques and strategies, fintech companies may experience performance issues, downtime, and even service disruptions, leading to dissatisfied customers and lost revenue.
One of the most effective techniques for scaling fintech APIs is caching. By caching frequently accessed data, API requests can be served faster, reducing the load on the server. Caching can be implemented at various levels, including client-side, server-side, and database level.
Load balancing is another important technique for handling high traffic loads. By distributing traffic across multiple servers, fintech companies can improve performance and handle higher traffic loads. Load balancing can be implemented at the application or network level, and can be done using various techniques, including round-robin, least connections, and IP hash. Despite the fact that you will choose a leading load balancer for your solution, please pay attention to limitations, default configurations which are coming as part of the solutions, as they may decrease throughput of the entire solution. Based on statistics from the top 5 leading are [2]:
1. Azure Application Gateway
2. Citric ADC
3. KempLoadMaster
4. F5 NGINX Plus
5. HAProxy
Graph 1 - Trending API Load Balancers
Contenders Leaders
« - P
E 9 afrix
ж 1
a- Q ®— aws T Д1
< — Ф
O В t 13 с - L ¿5, |
r —
« >— tra i— 1 fik
h -
Niche High Performers
Satisfaction (?)
Auto-scaling is a technique that enables the infrastructure to automatically adjust its capacity based on the incoming traffic. It involves setting up rules that trigger the addition or removal of servers based on traffic patterns. Auto-scaling can be implemented using cloud-based infrastructure services, such as Amazon Web Services (AWS) or Microsoft Azure. Another alternative is to apply autoscaling design patterns, for example:
1. Trigger-based scaling: This pattern involves setting up triggers based on specific metrics, such as CPU usage or network traffic, and automatically scaling the system up or down based on those triggers.
2. Scheduled scaling: This pattern involves setting up a schedule for scaling, such as scaling up during peak hours and scaling down during off-peak hours.
3. Predictive scaling: This pattern involves using machine learning or other predictive algorithms to anticipate future traffic and automatically scale the system up or down accordingly.
4. Capacity-based scaling: This pattern involves setting up a minimum and maximum number of instances, and scaling up or down as needed to ensure that the system remains within those limits.
5. Service-based scaling: This pattern involves setting up different services with different scaling needs and scaling them independently of each other.
6. Hybrid scaling: This pattern involves combining multiple scaling strategies, such as trigger-based scaling and scheduled scaling, to create a more robust and flexible auto-scaling system. Fault-tolerant scaling: This pattern involves setting up a system that can automatically recover from failures by scaling up or down as needed to ensure that the system remains available and responsive.
API gateways are also essential for scaling fintech APIs. They act as a reverse proxy that routes requests to different services based on predefined rules, and can handle tasks such as authentication, rate limiting, and caching. API gateways provide a centralized entry point for APIs, making it easier to manage, monitor, and scale APIs.
Please find comparison between to 14 API gateways from geekflare [3]: Table 1 - API gateways comparison
Language/Platf
API Gateway orm Open Source Key Features Notable Users
Kong Gateway Lua/Nginx Yes
Authentication, Nasdaq, Traffic Control, Honeywell,
Analytics, Expedia,
Serverless, etc. Samsung
Apache APISIX Nginx/etcd Yes
Tyk
Go
Yes
Authentication, Self-hosted or
Quotas, Rate managed,
Limiting, available on
Monitoring, AWS
GraphQL
Ocelot
Goku
Express Gateway
Gloo
NET
Yes
Golang
Yes
Express.js
Yes
Envoy Proxy Yes
KrakenD
Golang
Yes
Fusio
PHP
Yes
WSO2
Java
Yes
Routing, Authentication, Rate Limiting, Load Balancing
.NET-based microservices or SOA
Dynamic
Routing, Service
Orchestration, EOLINK Inc.
Multi-tenancy,
etc.
Basic Features
Joyent, The Linux Foundation, Switch Media, etc.
Full-Featured, Open-source or
Developer Portal, enterprise,
WAF, Rate advanced
Limiting, etc. features
Ultra-high
Performance,
Aggregation,
Transformation,
etc.
Faster than Kong and Tyk
Monetization, Subscription Simple Support, OAI, Intuitive RAML, Backend
Documentation
and
Full Lifecycle API
Management
Deployable anywhere, Kubernetes operator
Asynchronous processing is another technique that can help fintech companies handle high traffic loads. By processing requests in a non-blocking way, the server can handle more requests simultaneously, using techniques such as message queues, event-driven architectures, and microservices. Here are some of the most common asynchronous design patterns used in APIs:
1. Callbacks: A callback is a function that is passed as an argument to another function and is called when the operation is complete. Callbacks are a simple and widely used pattern for handling asynchronous operations in APIs.
2. Promises: A promise is an object that represents the eventual completion (or failure) of an asynchronous operation and allows you to attach callbacks to handle the results. Promises provide a cleaner and more concise way of handling asynchronous operations than callbacks.
3. Async/await: Async/await is a newer syntax that simplifies the use of promises by allowing you to write asynchronous code that looks like synchronous code. Async/await makes it easier to write and maintain asynchronous code, and it is becoming increasingly popular in modern APIs.
4. Reactive programming: Reactive programming is a programming paradigm that focuses on the propagation of change and the composition of asynchronous sequences of data. Reactive programming is often used in APIs that require realtime data processing and event-driven architectures.
5. Message queues: A message queue is a software component that allows you to decouple the processing of requests from the handling of responses. Message queues are often used in APIs that need to handle large volumes of requests and need to scale horizontally to handle spikes in traffic.
Distributed databases are designed to handle large volumes of data across multiple servers, providing scalability and fault tolerance. By distributing the data across multiple servers, distributed databases can handle high traffic loads and ensure reliability and data consistency. Based on StackOverflow survey top 5 leading databases are [4]:
1. MySQL
2. PostgreSQL
3. SQLite
4. MongoDB
5. Microsoft SQL Server
Graph 2 - Trending API distributed databases
All Respondents Professional Developers Learning to Code
MySQL 46.8S%
PostgreSQL 43.99%
SQLite 32.01%
MongoOB 28 3%
Microsoft SQL Server 26.87%
Redis 22.13%
MariaOB 17.93%
Elasticseardi 12.21%
Oracle LL49%
Firebase Realtime Database 8.72%
Finally, monitoring and logging are essential for identifying issues and optimizing performance. By monitoring the API's performance, including response times, error rates, and traffic patterns, fintech companies can identify potential issues and take proactive measures to improve performance. As there are many-many solutions on the market, the suggestion is to look into the same brands for logging and monitoring and estimate the price for 3-5 years forward. Initially solutions look not expensive, the adoption costs about half of the year for the entire company. However, in a one or two-years period costs increase dramatically and automatically and you either should replace the vendor and invest about a year for adoption, or have to agree to paying an increasing price. The other suggestion is to never mix solution and observability systems. In other words, the performance of the solution shouldn't be impacted by the observability system.
In conclusion, scaling fintech APIs requires a combination of techniques and strategies to handle high traffic loads. By implementing techniques such as caching, load balancing, auto-scaling, API gateways, asynchronous processing, distributed databases, and monitoring, fintech companies can provide seamless and reliable services to their customers, even under high traffic loads. As the fintech market continues to grow, it is essential for companies to stay ahead of the curve and invest in scalable and reliable infrastructure.
REFERENCES:
1. Smart Infrastructure Market (2022) [Web resource] // Access mode: https://www.fortunebusinessinsights.com/smart-infrastructure-market-106346 (Accessed: May 16, 2023).
2. Load Balancing [Web resource] // Access mode: https://www.g2.com/categories/load-balancing#grid (Accessed: May 16, 2023).
3. Ali, A., 14 Open Source and Managed API Gateway for Modern Applications [Web resource] // Access mode: https://geekflare.com/api-gateway/ (Accessed: May 16, 2023).
4. Stack Overflow Annual Developer Survey (2023) [Web resource] // Access mode: https://survey.stackoverflow.co/ (Accessed: May 16, 2023).
