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Zarubaika Andrei
TECHNIQUES AND STRATEGIES FOR HANDLING HIGH TRAFFIC *
Аннотация:
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
Ключевые слова:
fintech, API scaling, caching, load balancing, auto-scaling, API gateways, asynchronous processing, distributed databases, monitoring
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]:
Graph 1 - Trending API Load Balancers
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:
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
API Gateway |
Language/Platform |
Open Source |
Key Features |
Notable Users |
Kong Gateway |
Lua/Nginx |
Yes |
Authentication, Traffic Control, Analytics, Serverless, etc. |
Nasdaq, Honeywell, Expedia, Samsung |
Apache APISIX |
Nginx/etcd |
Yes |
Dynamic Routing, Plug-in Hot Loading |
360, NetEase, TravelSky |
Tyk |
Go |
Yes |
Authentication, Quotas, Rate Limiting, Monitoring, GraphQL |
Self-hosted or managed, available on AWS |
Ocelot |
.NET |
Yes |
Routing, Authentication, Rate Limiting, Load Balancing |
.NET-based microservices or SOA |
Goku |
Golang |
Yes |
Dynamic Routing, Service Orchestration, Multi-tenancy, etc. |
EOLINK Inc. |
Express Gateway |
Express.js |
Yes |
Basic Features |
Joyent, The Linux Foundation, Switch Media, etc. |
Gloo |
Envoy Proxy |
Yes |
Full-Featured, Developer Portal, WAF, Rate Limiting, etc. |
Open-source or enterprise, advanced features |
KrakenD |
Golang |
Yes |
Ultra-high Performance, Aggregation, Transformation, etc. |
Faster than Kong and Tyk |
Fusio |
PHP |
Yes |
Monetization, Subscription Support, OAI, RAML, Documentation |
Simple and Intuitive Backend |
WSO2 |
Java |
Yes |
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:
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]:
Graph 2 - Trending API distributed databases
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:
Номер журнала Вестник науки №6 (63) том 5
Ссылка для цитирования:
Zarubaika Andrei TECHNIQUES AND STRATEGIES FOR HANDLING HIGH TRAFFIC // Вестник науки №6 (63) том 5. С. 323 - 330. 2023 г. ISSN 2712-8849 // Электронный ресурс: https://www.вестник-науки.рф/article/9377 (дата обращения: 19.05.2024 г.)
Вестник науки СМИ ЭЛ № ФС 77 - 84401 © 2023. 16+
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