Feature: gRPC Load Balancing Design in Kubernetes Environment

[cubxxw]

Checklist

• I've searched for similar issues and couldn't find anything matching
• I've discussed this feature request in the OpenIMSDK Slack and got positive feedback

Is this feature request related to a problem?

✅ Yes

Problem Description

Abstract:
This document outlines a gRPC load balancing solution tailored for Kubernetes, addressing the limitations of traditional Kubernetes Service-based load balancing for long-lived HTTP/2 connections typical of gRPC traffic. The design includes both client-side and server-side strategies to ensure efficient and dynamic load distribution across gRPC services.

Solution Description

Introduction:
Kubernetes Services typically operate at the network's fourth layer, utilizing kube-proxy for basic load balancing. However, due to gRPC's reliance on long-lived HTTP/2 connections, such a method leads to uneven load distribution, where all traffic might end up at a single pod. This necessitates a shift from connection-level to request-level balancing.

Design Challenges and Solutions:

1. Client-Side Load Balancing:

   • Approach: Utilize NameResolver, load balancing policies, and a Headless Service to distribute requests.
   • Implementation:
     • The gRPC client resolves service names into IP addresses through a resolver. For Kubernetes, this involves using a Headless Service that stores pod IPs as A records.
     • Built-in load balancing algorithms such as pick_first and round_robin are used. The round_robin policy distributes requests evenly across all available service IPs.
   • Code Example:

     svc := "mygrpc:50051"
     ctx, cancel := context.WithTimeout(context.Background(), time.Second*5)
     defer cancel()
     conn, err := grpc.DialContext(
       ctx,
       fmt.Sprintf("%s:///%s", "dns", svc),
       grpc.WithDefaultServiceConfig(`{"loadBalancingPolicy":"round_robin"}`),
       grpc.WithInsecure(),
       grpc.WithBlock(),
     )
     if err != nil {
       log.Fatal(err)
     }

2. Server-Side Load Balancing:

   • Introduce a middleware component (like Linkerd or a Service Mesh component such as Envoy in Istio) to perform layer 7 load balancing. This intermediary component handles incoming client requests and forwards them to backend pods.

Handling Kubernetes API with Kuberesolver:

• Overview: To dynamically adapt to changes in service endpoints due to pod scaling, a custom resolver, kuberesolver, is used. This resolver directly interacts with Kubernetes API to monitor and update endpoint changes.
• Configuration: Requires setting appropriate RBAC permissions for the client pod to access endpoint resources.
• Code Snippet:

  import "github.com/sercand/kuberesolver/v3"
  kuberesolver.RegisterInCluster()
  cc, err := grpc.Dial("kubernetes:///service.namespace:portname", opts...)

Benefits

Conclusion:
This design offers a robust solution for gRPC load balancing within Kubernetes, enhancing the traditional service discovery and load distribution mechanisms to effectively manage the unique challenges posed by gRPC's long-lived connections and HTTP/2 protocol.

Further Reading:

• Detailed exploration of gRPC load balancing in Kubernetes environments can be found here.

Appendix:

• YAML configurations for Kubernetes RBAC permissions are included to ensure proper authorization for the client pods to access the Kubernetes API.

Potential Drawbacks

No response

Additional Information

No response