memray
perspective
memray | perspective | |
---|---|---|
27 | 45 | |
12,628 | 7,589 | |
1.6% | 1.8% | |
9.0 | 9.3 | |
9 days ago | 5 days ago | |
Python | C++ | |
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.
memray
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Memray – A Memory Profiler for Python
I collected a list of profilers (also memory profilers, also specifically for Python) here: https://github.com/albertz/wiki/blob/master/profiling.md
Currently I actually need a Python memory profiler, because I want to figure out whether there is some memory leak in my application (PyTorch based training script), and where exactly (in this case, it's not a problem of GPU memory, but CPU memory).
I tried Scalene (https://github.com/plasma-umass/scalene), which seems to be powerful, but somehow the output it gives me is not useful at all? It doesn't really give me a flamegraph, or a list of the top lines with memory allocations, but instead it gives me a listing of all source code lines, and prints some (very sparse) information on each line. So I need to search through that listing now by hand to find the spots? Maybe I just don't know how to use it properly.
I tried Memray, but first ran into an issue (https://github.com/bloomberg/memray/issues/212), but after using some workaround, it worked now. I get a flamegraph out, but it doesn't really seem accurate? After a while, there don't seem to be any new memory allocations at all anymore, and I don't quite trust that this is correct.
There is also Austin (https://github.com/P403n1x87/austin), which I also wanted to try (have not yet).
Somehow this experience so far was very disappointing.
(Side node, I debugged some very strange memory allocation behavior of Python before, where all local variables were kept around after an exception, even though I made sure there is no reference anymore to the exception object, to the traceback, etc, and I even called frame.clear() for all frames to really clear it. It turns out, frame.f_locals will create another copy of all the local variables, and the exception object and all the locals in the other frame still stay alive until you access frame.f_locals again. At that point, it will sync the f_locals again with the real (fast) locals, and then it can finally free everything. It was quite annoying to find the source of this problem and to find workarounds for it. https://github.com/python/cpython/issues/113939)
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Microservice memory profiling
second time was nastier. I used https://github.com/bloomberg/memray to try to spot it - that's the tool you should try out. You load your service through memray, and it will get you some stats that you can export as a flamegraph. I can't really afford to make it run on production so I ran it in a docker image and repeatedly ran the scenario I thought was responsible. Didn't find anything. I know what I did wrong: I assumed one particular codepath was the problem. If would have find the issue if I had a really complete scenario that covers broadly every possible endpoint and condition. Can't blame memray, that tool is really promising.
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Big Data Is Dead
This is an excellent summary, but it omits part of the problem (perhaps because the author has an obvious, and often quite good solution, namely DuckDB).
The implicit problem is that even if the dataset fits in memory, the software processing that data often uses more RAM than the machine has. It's _really easy_ to use way too much memory with e.g. Pandas. And there's three ways to approach this:
* As mentioned in the article, throw more money at the problem with cloud VMs. This gets expensive at scale, and can be a pain, and (unless you pursue the next two solutions) is in some sense a workaround.
* Better data processing tools: Use a smart enough tool that it can use efficient query planning and streaming algorithms to limit data usage. There's DuckDB, obviously, and Polars; here's a writeup I did showing how Polars uses much less memory than Pandas for the same query: https://pythonspeed.com/articles/polars-memory-pandas/
* Better visibility/observability: Make it easier to actually see where memory usage is coming from, so that the problems can be fixed. It's often very difficult to get good visibility here, partially because the tooling for performance and memory is often biased towards web apps, that have different requirements than data processing. In particular, the bottleneck is _peak_ memory, which requires a particular kind of memory profiling.
In the Python world, relevant memory profilers are pretty new. The most popular open source one at this point is Memray (https://bloomberg.github.io/memray/), but I also maintain Fil (https://pythonspeed.com/fil/). Both can give you visibility into sources of memory usage that was previous painfully difficult to get. On the commercial side, I'm working on https://sciagraph.com, which does memory and also performance profiling for Python data processing applications, and is designed to support running in development but also in production.
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Check Python Memory Usage
bloomberg/memray: Memray is a memory profiler for Python
- What Python library do you wish existed?
- Modules Import and Optimisation
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The hand-picked selection of the best Python libraries and tools of 2022
Memray — a memory profiler
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Python 3.11 delivers.
Python profiling is enabled primarily through cprofile, and can be visualized with help of tools like snakeviz (output flame graph can look like this). There are also memory profilers like memray which does in-depth traces, or sampling profilers like py-spy.
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Memory Profiling for Python
I've been using this recently for memory profiling with Python, it works pretty well: https://github.com/bloomberg/memray
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What stack or tools are you using for ensuring code quality and best practices in medium and large codebases ?
great suggestions in this thread. i also recommend performance testing your codebase. these include techniques such as: - creating micro performance benchmarks - using [cProfile] (and learning how to plot / read flame graphs) - memory profiling (e.g. via memray)
perspective
- Ask HN: How Can I Make My Front End React to Database Changes in Real-Time?
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The Design Philosophy of Great Tables (Software Package)
Why do you want to render to canvas?
Perspective seems to be the most performant html table. It is more focused on extremely fast updates than styling, although it looks good.
Glide is a newcomer that also renders to canvas.
https://github.com/finos/perspective
https://github.com/glideapps/glide-data-grid
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Show HN: WhatTheDuck – open-source, in-browser SQL on CSV files
SQL workbench also uses https://perspective.finos.org/ for tables. It's a WASM table library which pairs nicely with duckdb and works well with large tables.
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React Spreadsheet 2 – Your Own Google Sheets
Yes. We are working on adding support for aggregation and pivoting using https://github.com/finos/perspective
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Show HN: DataSheetGrid, an Airtable-like React component
I haven't looked extensively at react-datasheet. It looks like it is trying to build more of a full product than the other data tables.
I have used ag-grid extensively, its an impressive product. Some pieces are a little awkward to use, particularly auto-sizing. But generally ag-grid has thought of most functionality and has a solution. The creator of ag-grid had a great interview on Javascript Jabber [1].
The other serious data table component that I have seen is FinOS Perspective [2]. This is extremely high performance, also more specialized and probably harder to customize. I think Perspective renders to a canvas element from Rust/C++ compiled to WASM (not 100% sure). It is also made for streaming updates.
AG-Grid supports streaming updates... but only in the commercial version.
Eventually the data model for these types of tables becomes tricky. I will be investigating parquet-wasm for my use case. Hit me up if you want to collaborate.
[1] https://blog.ag-grid.com/javascript-jabber-podcast/
[2] https://perspective.finos.org/
- Perspective Market Simulation
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ChDB: Embedded OLAP SQL Engine Powered by ClickHouse
Something like https://github.com/finos/perspective ? We use an OLAP(-y) WASM engine to provide query-ability to our data visualization tool, and doing the calculations in the browser is cheaper and simpler than a server-side database for datasets that fit in browser memory.
- Show HN: Udsv.js – A faster CSV parser in 5KB (min)
- Perspective 2.0, Open Source WebAssembly-Powered BI
What are some alternatives?
scalene - Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals
ag-Grid - The best JavaScript Data Table for building Enterprise Applications. Supports React / Angular / Vue / Plain JavaScript.
pyinstrument - 🚴 Call stack profiler for Python. Shows you why your code is slow!
arquero - Query processing and transformation of array-backed data tables.
MemoryProfiler - memory_profiler for ruby
datapane - Build and share data reports in 100% Python
viztracer - VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.
nocodb - 🔥 🔥 🔥 Open Source Airtable Alternative
magic-trace - magic-trace collects and displays high-resolution traces of what a process is doing
ClickHouse - ClickHouse® is a free analytics DBMS for big data
py-spy - Sampling profiler for Python programs
SandDance - Visually explore, understand, and present your data.