gRPC Load Balancing Deep Dive

Is your gRPC load balancer creating server hotspots? Learn why long-lived connections undermine autoscaling and how a simple Kubernetes configuration can solve it.

#1about 4 minutes

An overview of gRPC fundamentals and its trade-offs

gRPC is a high-performance framework using Protobuf for efficiency, but it has limitations in browser support and tooling maturity compared to REST.

#2about 4 minutes

How gRPC streaming and HTTP/2 affect load balancing

gRPC supports various streaming patterns over persistent HTTP/2 connections, which can cause traffic hotspots with traditional Layer 4 load balancing.

#3about 3 minutes

Client-side versus infrastructure-based load balancing strategies

Choose client-side load balancing for low-latency internal services and infrastructure-based load balancing for external APIs that require a clear demarcation point.

#4about 7 minutes

Exploring different types of load balancing algorithms

A review of basic, load-based, and hash-based algorithms reveals that options like "least outstanding requests" can outperform simple round robin for uneven loads.

#5about 2 minutes

Why autoscaling gRPC services can be challenging

Long-lived streaming connections can prevent traffic from being distributed to newly scaled instances, making traditional CPU-based autoscaling ineffective.

#6about 4 minutes

Tools for functional and performance testing of gRPC

Use tools like grpcurl for functional API testing with proto files and ghz for comprehensive performance and load testing of your gRPC services.

#7about 3 minutes

Case study: Separating unary and streaming calls

A practical example shows how separating unary and streaming gRPC calls into different Kubernetes services and target groups solves uneven load distribution.

#8about 1 minute

Key takeaways for effective gRPC load balancing

Successfully load balance gRPC by being mindful of long-lived sessions, understanding client traffic patterns, and selecting L7-based algorithms when possible.