dosubot[bot] commented on issue #2516: URL: https://github.com/apache/incubator-hugegraph/issues/2516#issuecomment-2054112606
<!-- Answer --> To profile the Kneighbor algorithm in HugeGraph Server and analyze its performance, including memory and CPU usage, you can leverage the integrated Arthas tool, which includes async-profiler. This approach allows you to generate detailed flame graphs without needing to download async-profiler separately. Here's how to do it: 1. **Ensure HugeGraph Server is Running**: Your server needs to be active to attach the Arthas agent. 2. **Invoke Arthas via HugeGraph's Integrated API**: HugeGraph Server has integrated Arthas, making it accessible through an API endpoint. You can start Arthas by making a PUT request to the `/arthas` endpoint, which will initialize it with the necessary configuration. 3. **Profile with Async-Profiler**: Once Arthas is attached, use its CLI to start profiling. For CPU profiling, you might use a command like `profiler start --event cpu -d 60`, adjusting the duration as needed. 4. **Generate and View Flame Graph**: After profiling, generate a flame graph with a command like `profiler stop --format html > /path/to/flamegraph.html`. Open the generated HTML file in a browser to view the performance characteristics of the Kneighbor algorithm. For more detailed instructions, including how to configure and start Arthas, refer to the `ArthasAPI.java` file in the HugeGraph Server codebase. This file outlines the API endpoint for starting Arthas, detailing the necessary configurations for the HugeGraph environment. This method provides a comprehensive view of the algorithm's performance, enabling targeted optimizations based on observed CPU and memory usage. ## Sources - [hugegraph-server/hugegraph-api/src/main/java/org/apache/hugegraph/api/arthas/ArthasAPI.java](https://github.com/apache/incubator-hugegraph/blob/master/hugegraph-server/hugegraph-api/src/main/java/org/apache/hugegraph/api/arthas/ArthasAPI.java) <!-- Dosu Comment Footer --> <details> <summary> #### About Dosu </summary> This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant. If you want to continue the conversation, start your reply with @dosu-bot. </details> -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
