Numaflow V1.6.0: Rust Dataplane Performance Analysis

by Omar Yusuf 53 views

Introduction

In this article, we're diving into a performance analysis of the v1.6.0 Rust dataplane in Numaflow, specifically addressing a bug report where the Rust dataplane showed lower frames per second (FPS) compared to the existing Golang dataplane. This is a crucial topic for anyone invested in Numaflow performance optimization, Rust vs Golang performance, and the overall efficiency of data processing pipelines. We'll break down the issue, explore the observed behavior, and discuss potential implications. So, let's get started and see what's going on under the hood!

Background: Numaflow and Dataplanes

Before we get into the specifics, let's quickly recap what Numaflow and dataplanes are all about. Numaflow is a powerful, cloud-native platform designed for building robust and scalable data processing pipelines. It allows you to define complex workflows and process data streams efficiently. The dataplane is the engine that actually executes these pipelines. In Numaflow v1.6.0, there are two primary dataplane implementations: one in Golang and a newer one in Rust. The introduction of the Rust dataplane aims to leverage Rust's performance and safety benefits. The goal of the Rust dataplane is to provide enhanced performance, improved memory safety, and better resource utilization compared to the Golang dataplane. This makes it essential for high-throughput and low-latency applications.

The Bug: Rust Dataplane Underperforming

The core issue reported is that the Rust dataplane in Numaflow v1.6.0 is exhibiting lower performance than its Golang counterpart. Specifically, in a demo pipeline designed to process video frames, the Rust dataplane achieved 12-13 FPS, while the Golang dataplane managed around 15 FPS. This discrepancy is significant because the expectation was that the Rust dataplane, known for its performance characteristics, should at least match or exceed the Golang dataplane's performance. This performance degradation is a critical concern, as it directly impacts the throughput and efficiency of data processing tasks within Numaflow. We need to understand why this is happening and how to address it.

The Demo Pipeline: A Four-Stage Process

To understand the context of this performance issue, it's crucial to examine the pipeline used in the test. The demo pipeline consists of four key vertices, each performing a specific task:

  1. In (Source): This is the entry point of the pipeline, responsible for slicing a source video into individual frames and sending each frame downstream. Think of it as the video splitter, breaking the stream into manageable chunks.
  2. Filter Resize (Map UDF): This vertex is a Map User-Defined Function (UDF) that shrinks the frames. This pre-processing step is crucial for preparing the frames for subsequent inference, reducing computational load, and speeding up the overall process. This step is identified as the performance bottleneck, making it a key area of focus for our analysis.
  3. Inference (Map UDF): Here, object detection is performed on the resized frames. This is where the actual analysis of the video content takes place, identifying objects and extracting relevant information.
  4. Out (Sink): This is the final stage, where the results of the inference are logged into a text file. It acts as the output and storage point for the processed data.

This pipeline structure is straightforward yet representative of common video processing workflows, making it a good test case for evaluating dataplane performance. Understanding each stage helps us pinpoint where the performance differences arise.

Observed Behavior: FPS Discrepancy

The most noticeable symptom of this bug is the difference in frames per second (FPS) between the two dataplanes. As mentioned earlier, the Golang dataplane achieved approximately 15 FPS, while the Rust dataplane only managed 12-13 FPS. This represents a performance drop of around 15-20%, which is a significant difference, especially in performance-sensitive applications. The Grafana screenshots provided in the bug report visually confirm this discrepancy. The throughput graphs clearly show lower processing rates for the Rust dataplane, particularly at the "Filter Resize" vertex. This performance difference can lead to increased processing times, higher resource consumption, and potentially missed deadlines in real-time processing scenarios.

Grafana Screenshots: A Visual Analysis

The provided Grafana screenshots offer a valuable visual representation of the performance differences. Let's break down what each screenshot tells us:

Golang Dataplane (Approx. 15 FPS)

  • Average Latency of Each Vertex: This screenshot shows the average time taken for each vertex to process a frame. The latencies appear to be relatively consistent, indicating a stable processing flow.
  • Filter Resize Throughput: This graph displays the throughput of the "Filter Resize" vertex, showing a consistent rate that contributes to the overall 15 FPS.
  • Out Throughput: This shows the throughput of the final "Out" vertex, which mirrors the "Filter Resize" throughput, indicating that the pipeline is processing frames at a consistent rate from start to finish.

Rust Dataplane (12-13 FPS)

  • Average Latency of Each Vertex: Similar to the Golang dataplane, this shows the average latency for each vertex. However, a closer inspection might reveal slightly higher latencies, particularly in the "Filter Resize" stage.
  • Filter Resize Throughput: This is where the performance difference is most evident. The throughput is noticeably lower than the Golang dataplane, directly correlating to the reduced FPS.
  • Out Throughput: As with the Golang dataplane, the "Out" throughput mirrors the "Filter Resize" throughput, confirming that the bottleneck at the "Filter Resize" stage is limiting the overall pipeline performance.

By visually comparing these screenshots, the performance gap becomes clear. The lower throughput in the Rust dataplane's "Filter Resize" vertex is the primary driver of the overall FPS reduction.

Identifying the Bottleneck: Filter Resize

As the bug report correctly points out, the performance bottleneck lies within the "Filter Resize" vertex. This is where frames are being resized as a pre-processing step before inference. The Grafana screenshots clearly show that the throughput of this vertex is significantly lower in the Rust dataplane compared to the Golang dataplane. This suggests that the resizing operation, or the way it's being handled within the Rust dataplane, is the primary cause of the performance issue. Possible reasons for this bottleneck could include:

  • Inefficient Image Resizing Algorithm: The Rust implementation might be using a less optimized algorithm for image resizing compared to the Golang version.
  • Memory Management Overheads: Rust's memory safety features, while beneficial in the long run, could introduce overheads if not managed carefully, particularly with large image data.
  • Concurrency Issues: The Rust dataplane might not be utilizing concurrency as effectively as the Golang dataplane in this specific operation.
  • External Dependencies: Differences in how external libraries or dependencies are handled in Rust versus Golang could also contribute to the bottleneck.

Environment Details: Kubernetes, NVIDIA DRA, Numaflow

Understanding the environment in which this issue was observed is crucial for replication and debugging. Here's a breakdown of the key components:

  • Kubernetes: Version 1.33.2 was used, indicating a relatively recent Kubernetes environment. This ensures that the underlying infrastructure is up-to-date with the latest features and improvements.
  • NVIDIA DRA Driver: Version v25.3.0-rc.5 was employed, suggesting the use of NVIDIA's Deep Learning Accelerator (DRA) for accelerated computing. This is particularly relevant given the video processing nature of the pipeline. The interaction between the Rust dataplane and the NVIDIA DRA drivers needs to be carefully examined.
  • Numaflow: Version v1.6.0, which includes the new Rust dataplane, is the version under investigation.
  • pynumaflow: Version 0.9.1, the Python client library for Numaflow, is also part of the environment.

These details help narrow down potential compatibility issues or specific interactions within this environment that might be contributing to the performance difference.

Postscript: Missing Metrics

In addition to the performance discrepancy, the reporter also noted that some metrics were missing or unexpected on the Rust dataplane. This is another important aspect to investigate, as metrics are crucial for monitoring and troubleshooting distributed systems like Numaflow. Missing metrics can hinder the ability to diagnose performance issues and ensure the overall health of the system. A separate issue will likely be created to address these metric-related problems. The consistency and accuracy of metrics are paramount for effective monitoring and debugging.

Expected Behavior: Rust Should Excel

The expectation is that the Rust dataplane should perform at least as well as, if not better than, the Golang dataplane. Rust is renowned for its performance, memory safety, and concurrency capabilities. The goal of introducing a Rust dataplane in Numaflow is to leverage these strengths for improved efficiency and scalability. Therefore, the observed underperformance is a deviation from the expected behavior and warrants thorough investigation. The potential benefits of the Rust dataplane, including lower latency and higher throughput, should be realized in practical applications.

Impact and Next Steps

The performance discrepancy between the Rust and Golang dataplanes has significant implications for Numaflow users. If the Rust dataplane consistently underperforms, it could limit its adoption and hinder the realization of its potential benefits. It's crucial to identify the root cause of this issue and implement necessary optimizations to bring the Rust dataplane up to par. Here are some potential next steps:

  1. Profiling: Use profiling tools to analyze the performance of the Rust dataplane in detail, pinpointing the exact functions or code sections that are causing the bottleneck.
  2. Code Review: Conduct a thorough code review of the "Filter Resize" implementation in the Rust dataplane, looking for potential inefficiencies or areas for optimization.
  3. Benchmarking: Develop targeted benchmarks specifically for the "Filter Resize" operation to compare the performance of the Rust and Golang implementations under controlled conditions.
  4. Dependency Analysis: Examine the external libraries and dependencies used in the Rust dataplane, ensuring they are the most performant options and are being used efficiently.
  5. Concurrency Audit: Review the concurrency model used in the Rust dataplane to ensure it's effectively utilizing available resources and not introducing bottlenecks.

Conclusion

The lower FPS observed in the Rust dataplane compared to the Golang dataplane in Numaflow v1.6.0 is a critical issue that needs to be addressed. The "Filter Resize" vertex has been identified as the primary bottleneck, and further investigation is required to pinpoint the root cause. By systematically profiling the code, reviewing the implementation, and conducting targeted benchmarks, the Numaflow team can optimize the Rust dataplane and unlock its full potential. This will ensure that Numaflow users can benefit from the performance, safety, and efficiency advantages that Rust offers. Guys, stay tuned for further updates as this issue is investigated and resolved! The goal is to make the Rust dataplane a robust and high-performance option for all Numaflow users.