Rust gRPC server memory grows ~1GB per request until OOM – how to diagnose?

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Summary

The issue at hand is a Rust gRPC server experiencing memory growth of approximately 1GB per request, leading to an eventual Out of Memory (OOM) error. This behavior is unexpected in Rust, given its focus on memory safety and lack of garbage collection. The goal is to diagnose and debug this memory growth issue.

Root Cause

The root cause of this issue can be attributed to several factors, including:

  • Large per-request allocations: The RPC method in question handles a request body of ~150 MB, which can lead to significant memory allocations.
  • Async lifetimes and backpressure: The asynchronous nature of the Rust gRPC server can cause large buffers to be held in memory longer than expected, contributing to memory growth.
  • Inadequate memory deallocation: The lack of garbage collection in Rust means that memory must be manually deallocated, which can be error-prone and lead to memory leaks.

Why This Happens in Real Systems

This issue can occur in real systems due to:

  • Insufficient resource management: Failing to properly manage resources, such as memory and network connections, can lead to resource exhaustion and performance degradation.
  • Inadequate testing and debugging: Insufficient testing and debugging can make it difficult to identify and fix issues like memory growth, leading to production errors.
  • Complexity of async programming: The complexity of asynchronous programming can make it challenging to reason about memory allocation and deallocation, increasing the likelihood of memory-related issues.

Real-World Impact

The real-world impact of this issue includes:

  • Performance degradation: Memory growth can lead to slower response times and decreased throughput, negatively impacting user experience.
  • Increased resource utilization: The 1GB per request memory growth can lead to increased resource utilization, resulting in higher costs and reduced scalability.
  • System crashes and downtime: The eventual OOM error can cause system crashes and downtime, leading to lost revenue and productivity.

Example or Code

use tokio::prelude::*;
use tonic::{Request, Response, Status};

async fn handle_rpc(
    request: Request<tonic::Streaming>,
) -> Result<Response, Status> {
    // Handle the request body of ~150 MB
    let mut buffer = Vec::new();
    while let Some(chunk) = request.message().await {
        buffer.extend_from_slice(&chunk);
    }
    // Process the buffer
    // ...
    Ok(Response::new(bytes::Bytes::from(buffer)))
}

How Senior Engineers Fix It

Senior engineers can fix this issue by:

  • Using memory profiling tools: Utilize tools like dhat and Flamegraph to identify memory allocation hotspots and leaks.
  • Implementing efficient memory management: Use smart pointers and manual memory management techniques to ensure proper memory deallocation.
  • Optimizing async programming: Use async-friendly data structures and design patterns to minimize memory allocation and deallocation.

Why Juniors Miss It

Junior engineers may miss this issue due to:

  • Lack of experience with async programming: Inadequate understanding of async lifetimes and backpressure can lead to memory-related issues.
  • Insufficient knowledge of memory management: Failing to understand manual memory management and smart pointers can result in memory leaks.
  • Inadequate testing and debugging: Insufficient testing and debugging can make it difficult to identify and fix issues like memory growth, leading to production errors.