Snakeviz 0.2

It has been over two years since Erik Bray and I made the first release of SnakeViz 0.1, a tool for visualizing performance profiles of Python code. It had multiple performance bottlenecks, but it worked just well enough that it took me a long time to prioritize making improvements. That time has finally come around and I’m happy to announce that SnakeViz 0.2 is now available!

What’s New

The look and feel of SnakeViz remains much the same (see a screenshot), but there are some new things on the screen:

SnakeViz screenshot

  • Detailed function information when hovering over the visualization
  • Call stack list for tracking where you are when zooming the visualization
  • Control the depth of the displayed call tree
  • Limit the display of functions that take up relatively little time

Under the Hood

The first release of SnakeViz had some performance bottlenecks:

  • It tried to transfer a complete call tree from the server to the client as JSON
  • It tried to display the entire call tree in the sunburst visualization

Those limited the usefulness of SnakeViz with profiles that contained calls to a lot of functions. The version 0.2 release is an almost complete rewrite in order to make SnakeViz work with larger profiles.

The first limitation is addressed by moving the building of call trees into the client application. Profile data is passed from the server to the client in close to the same form as it’s available from Python’s pstats module. Once in the client, the profile data is used to construct call trees on demand for visualization.

The second limitation is addressed by limiting how much of the profile is visualized at once. Call trees are built only to a user specified depth and users can opt to omit functions that do not use much time from display. (The “depth” and “cutoff” controls.)


I and others have tested SnakeViz 0.2 with some fairly large profiles and found it works. You can read more about SnakeViz in the updated docs. Please give it a try! Issues can be reported on GitHub.

Performance of Pandas Series vs NumPy Arrays

I recently spent a day working on the performance of a Python function and learned a bit about Pandas and NumPy array indexing. The function is iterative, looping over data and updating some row weights until it meets convergence criteria. I tried to do as much processing as I could before the loops, but some indexing (and of course arithmetic) had to stay inside the loops.

When I looked at profiles of the function almost all of the time was being spent doing indexing on Pandas Series objects. A quick investigation shows that indexing Series objects is quite slow compared to NumPy arrays. First, some setup: Read More »

SnakeViz 0.1 – A Python Profile Viewer

When profiling code it can be helpful to have a visualization of the profile to really show what areas of your program are taking the most time. The great RunSnakeRun application has been filling this roll for a while now but some of my colleagues have been turned off by the need to install wxPython. The web browsers we use every day are quickly becoming great visualization platforms so I thought it would be a nice project to make a Python profile viewer that works in the browser. Today, co-developer Erik and I are happy to announce the first official release of SnakeViz.

Example SnakeVis visualization.
An example SnakeViz visualization.

Read More »