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gaogaotiantian
viztracer
Python2020–2024
gaogaotiantian / viztracer

viztracer

A debugging and profiling tool that can trace and visualize python code execution

Developer ToolsData & DatabasesTesting & QualityObservability
Explore on GitHubRead Hacker News discussions
4Distinct people
4Mentions
0Explicit recommenders
7.8KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A debugging and profiling tool that can trace and visualize python code execution

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year4 dated mentions

Observed mentions: 2020-09-15 21:45:43 UTC to 2024-04-10 16:06:24 UTC.

0122020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC110
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC202
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC101

What Hacker News says

4 sources
IanOzsvald·Apr 10, 2024
I'm the co-author of High Performance Python, Micha and I are working on the 3rd ed (for 2025). Lots of bits of the book came from my past conference talks, they're available here (and the public talks will generally be on youtube): https://speakerdeck.com/ianozsvald Mostly that content has a scientific focus but the obvious thing that carries over to any part of Python is _profiling_ to figure out what's slow. Top tools I'd recommend are: * https://pypi.org/project/scalene/ combined cpu+memory+gpu profiling * https://github.com/gaogaotiantian/viztracer get a timeline of execution vs call-stack (great to discover what's happening deep inside pandas) * my https://pypi.org/project/ipython-memory-usage/ if you're in Jupyter Notebooks (built on https://github.com/pythonprofilers/memory_profiler which sadly is unmaintained) * https://github.co
Read the rest
m/pyutils/line_profiler/
In Ask HN: C/C++ developer wanting to learn efficient Python
Veserv·May 21, 2022
Do not use bad instrumenting profilers. A good modern tracing-based instrumenting profiler provides so much more actionable information and insights into where problems are than a sampling profiler it is ridiculous. As a example consider viztracer [1] for Python. By using a aggregate visualizer such as a flame graph you can figure out what is taking the most time then you can use a tracing visualizer to figure out the exact call stacks and system execution and state that caused it. Not only that, a tracing visualizer lets you diagnose whole system performance and makes it trivial to identify 1 in 1000 anomalous execution patterns (with a 4k screen a anomalous execution pattern stands out like a 4 pixel dead spot). In addition you also get vastly less biased information for parallel execution and get easy insights into parallel execution s
Read the rest
lowdowns, interference, contention, and blocking behaviors. The only advantages highlighted in your video that still apply to a good instrumenting profiler are: 1. Multi-language support. 2. Performance counters (though that is solved by doing manual tracking after you know the hotspots and causes). 3. Overhead (if you are using low sampling frequency). Even then a good tracing instrumentation implementation should only incur low double-digit percent overhead and maybe 100% overhead in truly pathological cases involving only small functions where the majority of the execution time is literally spent in function call overhead. 4. No need for recompilation, but you are already looking to make performance changes and test so you already intend to rebuild frequently to test those experiments. In addition, the relative difference in information is so humongous that this is not even worth contemplating unless it is a hard requirement like evaluating something in the field. [1] https://github.com/gaogaotiantian/viztracer
In GCC Profiler Internals

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