🌀 How to Enhance Microservices with Ocelot API Gateway? Caching, Load Balancing, Request Aggregation and Rate Limiting! 🚀

Introduction

Hello, Netcode-Hub community! 👋 In today's lesson, we're diving deeper into the world of API Gateways with Ocelot in .NET 8. If you've been following our series on microservices, you know we've already covered creating microservices with Ocelot API Gateway, restricting all API access through the Gateway, and adding JWT authentication. Today, we're going to enhance our gateway even further by introducing caching, load balancing, rate limiting, and request aggregation. These features are essential for building scalable, efficient, and reliable microservices architectures. Let's get started!

Summary

1. Caching in Ocelot:

First, let's talk about caching. Imagine you have an e-commerce application, and users frequently access product details. Without caching, every request would hit your database, causing unnecessary load and increased response times. By implementing caching in Ocelot, we can store frequently requested data temporarily, reducing database load and significantly improving response times. We'll configure CacheManager to handle this efficiently.

2. Load Balancing:

Next up is load balancing. Consider a scenario where your application experiences high traffic, such as during a flash sale. To ensure your system can handle the load, you need to distribute incoming requests across multiple instances of your microservices. Ocelot's load balancing feature allows us to do just that, ensuring high availability and reliability by preventing any single instance from being overwhelmed.

3. Rate Limiting:

Now, let's discuss rate limiting. Suppose you have a public API that's prone to misuse or abuse by users sending too many requests in a short period. Rate limiting helps control the number of requests a user can make, protecting your services from potential overload and ensuring fair usage. With Ocelot, we can easily set up rate limiting to safeguard our microservices.

4. Request Aggregation:

Finally, request aggregation. In a complex microservices environment, you might have scenarios where multiple services need to be called to fulfill a single client request. For example, in a social media application, fetching a user's profile might involve calling separate services for user details, posts, and friends. Ocelot's request aggregation allows us to combine these multiple requests into a single response, simplifying client interactions and improving performance.

# Add Ocelot Configuration

  {
    "Routes": [
      {
        "DownstreamPathTemplate": "/api/user",
        "DownstreamScheme": "http",
          // Load Balancer
        "DownstreamHostAndPorts": [
          {
            "Host": "localhost",
            "Port": 7001
          },
          {
            "Host": "localhost",
            "Port": 7004
          },
          {
            "Host": "localhost",
            "Port": 7005
          }
        ],
        "UpstreamPathTemplate": "/api/user",
        "UpstreamHttpMethod": [ "GET", "POST", "PUT", "DELETE" ],
        "FileCacheOptions": {
          "TtlSeconds": 15,
          "Region": "default",
          "Header": "OC-Caching-Control",
          "EnableContentHashing": false
        },
        "RateLimitOptions": {
          "ClientWhitelist": [],
          "EnableRateLimiting": true,
          "Period": "60s",
          "PeriodTimespan": 6,
          "Limit": 2
        },
        "Key": "userService",
  
        // Load Balancer
        "LoadBalancerOptions": {
          "Type": "LeastConnection"
        }
      },
      {
        "DownstreamPathTemplate": "/api/weatherforecast",
        "DownstreamScheme": "http",
        "DownstreamHostAndPorts": [
          {
            "Host": "localhost",
            "Port": 7002
          }
        ],
        "UpstreamPathTemplate": "/api/weatherforecast",
        "UpstreamHttpMethod": [ "GET", "POST", "PUT", "DELETE" ],
  
        // Caching
        "FileCacheOptions": {
          "TtlSeconds": 15,
          "Region": "default",
          "Header": "OC-Caching-Control",
          "EnableContentHashing": false
        },
        "RateLimitOptions": {
          "ClientWhitelist": [],
          "EnableRateLimiting": true,
          "Period": "60s",
          "PeriodTimespan": 6,
          "Limit": 2
        },
        "Key": "weatherforecastService"
      },
      {
        "DownstreamPathTemplate": "/api/account/{email}/{password}",
        "DownstreamScheme": "http",
        "DownstreamHostAndPorts": [
          {
            "Host": "localhost",
            "Port": 7003
          }
        ],
        "UpstreamPathTemplate": "/api/account/{email}/{password}",
        "UpstreamHttpMethod": [ "GET", "POST", "PUT", "DELETE" ]
      }
    ],
  
    // Request Aggregation
    "Aggregates": [
      {
        "RouteKeys": [
          "userService",
          "weatherforecastService"
        ],
        "UpstreamPathTemplate": "/api/aggregate"
      }
    ],
    "GlobalConfiguration": {
      "BaseUrl": "https://localhost:7000"
    }
  }

Conclusion

By implementing caching, load balancing, rate limiting, and request aggregation in our Ocelot Gateway API, we've made our microservices architecture more robust, scalable, and efficient. These features not only enhance performance and reliability but also ensure a smoother experience for our users. I hope you found this tutorial helpful! If you did, don't forget to follow me 🚀

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