Fix Ray Dashboard Slow Loading & High Memory Usage

Introduction

Hey guys! Today, we're diving deep into a performance issue with the Ray Dashboard's Metrics page. Specifically, we're going to address the problems of slow loading times and high memory usage that some users have been experiencing. This issue can be frustrating, especially when you're trying to monitor your Ray jobs efficiently. We'll break down the problem, discuss the root cause, and explore potential solutions. So, if you've been struggling with the Ray Dashboard's Metrics page, you're in the right place!

Understanding the Problem

When you launch a simple Ray job and navigate to the "Metrics" page in the Ray Dashboard, you might notice that it takes a significant amount of time to load – sometimes as long as 15-30 seconds. To put this into perspective, the same dashboards in Grafana load in under a second. This delay can be quite disruptive, especially when you need quick access to performance metrics. Moreover, some users have reported that their Chrome tab crashes with SIGTRAP or Error code 5, adding another layer of frustration. The high memory usage is also a major concern, with the Ray Dashboard consuming around 1.4GB of memory compared to Grafana's 85MB for the Default dashboard and 102MB for the Data dashboard. This significant difference in memory consumption can impact your system's overall performance and stability.

The Root Cause: Embedded Grafana Iframes

The primary reason behind these performance issues appears to be the way the Ray Dashboard embeds Grafana panels. The dashboard embeds approximately 60 Grafana iframes, each representing an individual panel. While embedding Grafana dashboards is a great way to integrate metrics, the current implementation seems to be causing significant overhead. It's essential to understand why this approach is causing problems. Each iframe essentially loads a separate instance of Grafana within the dashboard, leading to increased memory usage and longer loading times. This is because each iframe needs to fetch its own resources, render its content, and maintain its own state. When you multiply this by 60 iframes, the performance impact becomes substantial. Unlike embedding the entire Grafana dashboard, which would consolidate these resources, the current method multiplies the load on your system. This is a critical distinction that contributes to the performance bottlenecks we're observing.

Why Individual Panels?

The core question here is why the Ray Dashboard embeds each panel individually instead of embedding the entire Default and Data dashboards. There might have been a specific reason for this design choice initially, such as the need for granular control over panel display or specific layout requirements within the Ray Dashboard. However, the performance implications of this approach are now becoming evident. Embedding individual panels can offer flexibility in terms of customization and arrangement, but this comes at the cost of increased overhead. When you embed a full dashboard, Grafana can optimize the loading and rendering process, reducing the overall resource consumption. So, while the intention behind embedding individual panels might have been valid, the current implementation is not scaling well and needs to be re-evaluated. It’s important to consider alternative solutions that can provide the same level of customization without sacrificing performance.

Verifying the Grafana Instance

It's important to note that the issue doesn't seem to stem from the Grafana instance itself. Most of the loading time is spent on static assets, which are loaded from memory or disk caches. The user who reported this issue is using the official Grafana image, which should rule out any application code issues on the Grafana side. This means the problem is likely within the Ray Dashboard's implementation of embedding these Grafana panels, not within Grafana itself. This is a crucial distinction because it narrows down the scope of the problem and helps focus the troubleshooting efforts. If the Grafana instance were the issue, we would see problems across different contexts, not just within the Ray Dashboard. Since the issue is isolated to the Ray Dashboard's Metrics page, it points to the embedding mechanism as the primary culprit.

Impact and Workarounds

While this issue might seem like a minor annoyance, it can significantly impact your workflow. Slow loading times and frequent crashes can disrupt your ability to monitor your Ray jobs effectively. Imagine you're debugging a complex distributed application and need to quickly check the metrics to identify bottlenecks. Waiting 15-30 seconds for the dashboard to load can be incredibly frustrating and time-consuming. The high memory usage also poses a risk, especially if you're running multiple resource-intensive applications simultaneously. This can lead to system instability and potentially impact other processes. While we can work around this issue by opening the dashboards directly in Grafana, this isn't ideal. The Ray Dashboard is meant to be a centralized hub for managing and monitoring Ray jobs, and having to switch to Grafana disrupts this workflow. The goal is to streamline the user experience as much as possible, and this issue detracts from that goal.

Potential Solutions and Improvements

Fortunately, there are several potential solutions and improvements that can address these performance issues. The most promising approach seems to be embedding the entire Grafana dashboards instead of individual panels. This would significantly reduce the number of iframes and the overhead associated with loading each panel separately. Embedding full dashboards allows Grafana to optimize the rendering process, reduce resource consumption, and improve loading times. Another potential optimization is to lazy-load the panels. This means only loading the panels that are currently visible in the viewport, instead of loading all 60 panels at once. Lazy-loading can significantly reduce the initial loading time and memory usage, especially if users only need to view a subset of the panels at any given time. Additionally, caching mechanisms can be implemented to store the rendered panels and reduce the need to reload them frequently. Caching can be particularly effective for panels that display static or infrequently changing data. By caching these panels, the dashboard can serve them from memory, further reducing loading times and memory usage. Finally, optimizing the iframe communication between the Ray Dashboard and Grafana can also improve performance. Minimizing the data transfer and reducing the number of messages exchanged between the iframe and the parent window can lead to significant performance gains. By implementing these solutions, we can significantly improve the performance and usability of the Ray Dashboard's Metrics page.

User Environment

To provide some context, the user who reported this issue is running Ray 2.47.1 on Python 3.10, with Ubuntu 24.04 and Grafana 12.0.1. This information is helpful for reproducing the issue and ensuring that any fixes are compatible with this environment. Understanding the user's setup allows us to tailor our troubleshooting efforts and develop solutions that are effective across different configurations. For instance, knowing the specific versions of Ray, Python, and Grafana can help identify any compatibility issues or version-specific bugs. Similarly, knowing the operating system can help us understand the underlying system resources and constraints. This information is crucial for diagnosing the problem accurately and implementing the right fixes.

Call to Action: Contributing to the Solution

The user who reported this issue has expressed a willingness to contribute a PR to address this problem, which is fantastic! Community contributions are invaluable in improving open-source projects like Ray. If embedding the entire Grafana dashboards sounds like a viable solution, a PR implementing this change would be a great step forward. We encourage anyone else who is experiencing this issue or has ideas for improvement to get involved. Contributing to open-source projects can be a rewarding experience, and it's a great way to help the Ray community. Whether it's submitting a PR, providing feedback, or simply sharing your experiences, your contributions can make a difference. Let's work together to make the Ray Dashboard's Metrics page faster, more efficient, and more user-friendly!

Conclusion

In conclusion, the performance issues with the Ray Dashboard's Metrics page, specifically the slow loading times and high memory usage, are primarily due to the way Grafana panels are embedded as individual iframes. While there are workarounds, such as opening the dashboards in Grafana directly, these are not ideal. Potential solutions include embedding the entire Grafana dashboards, lazy-loading panels, implementing caching mechanisms, and optimizing iframe communication. The Ray community is encouraged to contribute to addressing this issue, and a PR implementing the embedding of entire dashboards would be a significant step forward. By working together, we can improve the performance and usability of the Ray Dashboard, making it an even more valuable tool for managing and monitoring Ray jobs.

Next Steps

To move forward, the next steps should include:

1. Testing the proposed solution of embedding entire Grafana dashboards in a development environment.
2. Profiling the performance of the new implementation to ensure it resolves the loading time and memory usage issues.
3. Gathering feedback from other Ray users on the changes.
4. Submitting a pull request with the optimized implementation.
5. Documenting the changes and best practices for using the Ray Dashboard Metrics page.

By taking these steps, we can ensure that the fix is effective, well-tested, and meets the needs of the Ray community.