viztracer
regular-table
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viztracer | regular-table | |
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5 | 6 | |
4,363 | 322 | |
- | 1.6% | |
7.7 | 5.6 | |
3 days ago | about 1 month ago | |
Python | JavaScript | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
viztracer
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Ask HN: C/C++ developer wanting to learn efficient Python
* https://github.com/gaogaotiantian/viztracer get a timeline of execution vs call-stack (great to discover what's happening deep inside pandas)
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GCC Profiler Internals
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 slowdowns, 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
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Memray is a memory profiler for Python by Bloomberg
Actually it has explicit support for async task based reporting:
https://github.com/gaogaotiantian/viztracer#async-support
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Tracing and visualizing the Python GIL with perf and VizTracer
Let us run perf on this, similarly to what we did to example0.py. However, we add the argument -k CLOCK_MONOTONIC so that we use the same clock as VizTracer and ask VizTracer to generate a JSON, instead of an HTML file:
regular-table
- Memray is a memory profiler for Python by Bloomberg
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Show HN: Datagridxl2.js – No-nonsense fast Excel-like data table library
No reason to sacrifice accessibility and styling in the name of performance: https://github.com/jpmorganchase/regular-table
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Quadrillion Rows Example
I'm the author of regular-table, one of the alternatives mentioned in the accompanying blog. We went through a similar set of requirements to OP (and did at one point use fin-hypergrid as well, and also the excellent phosphor-datagrid which is now lumino-datagrid), and came to a slightly different conclusion regarding rendering. We ultimately decided to revert to (albeit bespoke) virtual DOM rendering for many of the same reasons mentioned by other commenters, namely:
- Ask HN: Why are developers so stingy with “rows per page”?
What are some alternatives?
pytest-austin - Python Performance Testing with Austin
react-virtualized - React components for efficiently rendering large lists and tabular data
magic-trace - magic-trace collects and displays high-resolution traces of what a process is doing
scalene - Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals
memray - Memray is a memory profiler for Python
gil_load - Utility for measuring the fraction of time the CPython GIL is held
Glide Data Grid - 🚀 Glide Data Grid is a no compromise, outrageously react fast data grid with rich rendering, first class accessibility, and full TypeScript support.
canvas-datagrid - Canvas based data grid web component. Capable of displaying millions of contiguous hierarchical rows and columns without paging or loading, on a single canvas element.
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
reactabular - A framework for building the React table you need (MIT)