What can you do with the spark Viewer on spark.lucko.me?

Updated

The spark Viewer is the web-based interface on spark.lucko.me for opening and inspecting spark performance data. You can use it in two ways: upload a profile or heap summary generated by spark commands and open the link it produces, or drag a .sparkprofile / .sparkheap file into the drop area on the page. Only those two file formats are accepted.

What the Viewer is for

spark itself is a performance profiler for Minecraft clients, servers, and proxies. Its data is produced by three main components:

  • Profiler — diagnoses performance problems and bottlenecks.
  • Memory Inspection — produces full heap dumps, summarizes what is using the most memory, and monitors GC activity.
  • Health Reporting — monitors and reports key metrics for tracking performance over time.

The Viewer is the part of the website that displays that data. It is written in JavaScript with React and is open source on GitHub.

Two ways to open data in the Viewer

1. Generate data with spark, then follow the upload link

  1. Run the appropriate spark command to generate a profile or heap summary.
  2. After the data has been uploaded, click the link to open the Viewer.

The expected result is that the Viewer opens with your uploaded profile or heap summary loaded for inspection.

2. Drag and drop a local file

You can also generate or export a .sparkprofile or .sparkheap file and open it directly:

  1. Locate the exported .sparkprofile or .sparkheap file.
  2. Drag it into the drop box on the page, or click the box to select the file.

The page states: “Drag & drop a profile/heap file here or click to select (only .sparkprofile or .sparkheap files are accepted).”

File format rules

Format Accepted by the Viewer?
.sparkprofile Yes
.sparkheap Yes
Any other file type No

Common sticking points

  • Wrong file type. If the file is not .sparkprofile or .sparkheap, the Viewer will not accept it.
  • No data yet. The upload-link route only works after you have generated a profile or heap summary with the appropriate spark commands.
  • Local file route. If you already have an exported file, you do not need to upload first — drag and drop is enough.

For more detail on spark itself, the site points to GitHub and its Discord community.