Spring Cloud Gateway is a library provided by the Spring Cloud project that allows developers to build an API gateway on top of the Spring WebFlux framework. An API gateway is an essential component in microservices architectures, responsible for request routing, composition, and request/response transformation, among other tasks.

Here are the key features and functionalities of Spring Cloud Gateway:

1. Route Requests: At its core, Spring Cloud Gateway is used to route requests to appropriate downstream services.
2. Path Rewriting: The gateway can rewrite request URLs before they reach the downstream service.
3. Filters: Filters can be applied to modify inbound and outbound requests and responses. For instance:
   • Pre filters can modify the request before it’s routed.
   • Post filters can modify the response after it’s routed.
4. Rate Limiting: Using the gateway, you can apply rate limiting to your routes to protect your services from being overwhelmed.
5. Security: Integration with Spring Cloud Security means you can use the gateway to handle authentication and authorization.
6. Resilience: Spring Cloud Gateway can be integrated with other tools like Resilience4j to add circuit breaking, retries, and more.
7. Service Discovery: Integrated with Spring Cloud’s discovery client, so services can be discovered and routes can be automatically updated.
8. WebSockets: Support for routing WebSocket traffic.
9. Load Balancing: When used alongside Spring Cloud LoadBalancer or Netflix Ribbon, it provides client-side load balancing.
10. Monitoring & Tracing: Integration with Spring Cloud Sleuth and other tools enables distributed tracing.

Basic Example:

To set up a simple route using Spring Cloud Gateway:

1. First, add the necessary dependency in your pom.xml:

<dependency>
    <groupId>org.springframework.cloud</groupId>
    <artifactId>spring-cloud-starter-gateway</artifactId>
</dependency>

2. Define a route in your application.yml or application.properties:

spring:
  cloud:
    gateway:
      routes:
      - id: my_route_id
        uri: http://downstream-service-url.com
        predicates:
        - Path=/my-service/**

3. You can also define routes programmatically:

@Bean
public RouteLocator customRouteLocator(RouteLocatorBuilder builder) {
    return builder.routes()
        .route("my_route_id", r -> r.path("/my-service/**")
                                  .uri("http://downstream-service-url.com"))
        .build();
}

In the configuration above, any request coming into the gateway with a path that starts with /my-service will be routed to http://downstream-service-url.com.

It’s worth noting that while Spring Cloud Gateway offers a lot of features out of the box, its real power comes from its extensibility, allowing developers to write custom filters, predicates, and more.

Let’s delve into examples for each of these features.

1. Route Requests:

@Bean
public RouteLocator routeRequests(RouteLocatorBuilder builder) {
    return builder.routes()
        .route("route_service", r -> r.path("/service/**")
                                     .uri("http://downstream-service-url.com"))
        .build();
}

2. Path Rewriting:

@Bean
public RouteLocator rewritePath(RouteLocatorBuilder builder) {
    return builder.routes()
        .route("rewrite_route", r -> r.path("/oldpath/**")
                                      .filters(f -> f.rewritePath("/oldpath/(?<segment>.*)", "/newpath/${segment}"))
                                      .uri("http://downstream-service-url.com"))
        .build();
}

3. Filters:

• Pre Filter:

@Bean
public RouteLocator preFilter(RouteLocatorBuilder builder) {
    return builder.routes()
        .route("pre_filter_route", r -> r.path("/service/**")
                                         .filters(f -> f.addRequestHeader("X-Request-Foo", "Bar"))
                                         .uri("http://downstream-service-url.com"))
        .build();
}

• Post Filter:

// For demonstration, using a custom filter as an example.
public class PostFilter extends AbstractGatewayFilterFactory<PostFilter.Config> {
    @Override
    public GatewayFilter apply(Config config) {
        return (exchange, chain) -> chain.filter(exchange)
            .then(Mono.fromRunnable(() -> {
                // Modify response here
            }));
    }

    public static class Config {
        // Configuration data.
    }
}

4. Rate Limiting:

@Bean
public RouteLocator rateLimiter(RouteLocatorBuilder builder) {
    return builder.routes()
        .route("rate_limit_route", r -> r.path("/service/**")
                                        .filters(f -> f.requestRateLimiter(c -> c.setRateLimiter(redisRateLimiter())))
                                        .uri("http://downstream-service-url.com"))
        .build();
}

5. Security:
   Integration with Spring Security generally involves using Spring’s WebSecurityConfigurerAdapter, which is a more complex topic.
6. Resilience:
   You’d integrate Resilience4j via its Spring Boot starter and then apply filters accordingly in the gateway.
7. Service Discovery:
   Assuming Eureka:

spring.cloud.gateway.discovery.locator.enabled=true

8. WebSockets:
   Just by defining a route to a WebSocket service, Spring Cloud Gateway would handle it.

@Bean
public RouteLocator webSocketRoute(RouteLocatorBuilder builder) {
    return builder.routes()
        .route("ws_route", r -> r.path("/ws-service/**")
                                 .uri("ws://websocket-service-url"))
        .build();
}

9. Load Balancing:
   By default, if you’re using Spring Cloud Service Discovery, URI should be lb://SERVICE-ID.

@Bean
public RouteLocator loadBalancerRoute(RouteLocatorBuilder builder) {
    return builder.routes()
        .route("load_balanced_route", r -> r.path("/lb-service/**")
                                            .uri("lb://my-service-id"))
        .build();
}

10. Monitoring & Tracing:
    For Sleuth, just including the dependency will auto-enable tracing. Zipkin can be integrated for visualization.

Note: Some of these features require additional configurations and dependencies in your pom.xml, and the examples provided are meant to give a foundational understanding. Actual implementations may require more detailed setup and additional considerations for production readiness.