t-digest
pyroscope
t-digest | pyroscope | |
---|---|---|
9 | 56 | |
1,924 | 7,382 | |
- | - | |
3.3 | 9.6 | |
4 months ago | about 1 year ago | |
Java | Go | |
Apache License 2.0 | GNU Affero General Public License v3.0 |
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.
t-digest
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Ask HN: How do you deal with information and internet addiction?
> I get a lot of benefit from this information but somehow it feels shallow.
I take a longer view to this. For example, a few years ago I read about an algorithm to calculate percentiles in real time. [0]
It literally just came up at work today. I haven't used that information but maybe two times since I read it, but it was super relevant today and saved my team potential weeks of development.
So maybe it's not so shallow.
But to your actual question, I have a similar problem. The best I can say is that deadlines help. I usually put down the HN and Youtube when I have a deadline coming up. And not just at work. I make sure my hobbies have deadlines too.
I tell people when I think something will be done, so they start bugging me about it when it doesn't get done, so that I have a "deadline". Also one of my hobbies is pixel light shows for holidays, which come with excellent natural deadlines -- it has to be done by the holiday or it's useless.
So either find an "accountability buddy" who will hold you to your self imposed deadlines, or find a hobby that has natural deadlines, like certain calendar dates, or annual conventions or contests that you need to be done by.
[0] https://github.com/tdunning/t-digest
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Ask HN: What are some 'cool' but obscure data structures you know about?
I am enamored by data structures in the sketch/summary/probabilistic family: t-digest[1], q-digest[2], count-min sketch[3], matrix-sketch[4], graph-sketch[5][6], Misra-Gries sketch[7], top-k/spacesaving sketch[8], &c.
What I like about them is that they give me a set of engineering tradeoffs that I typically don't have access to: accuracy-speed[9] or accuracy-space. There have been too many times that I've had to say, "I wish I could do this, but it would take too much time/space to compute." Most of these problems still work even if the accuracy is not 100%. And furthermore, many (if not all of these) can tune accuracy to by parameter adjustment anyways. They tend to have favorable combinatorial properties ie: they form monoids or semigroups under merge operations. In short, a property of data structures that gave me the ability to solve problems I couldn't before.
I hope they are as useful or intriguing to you as they are to me.
1. https://github.com/tdunning/t-digest
2. https://pdsa.readthedocs.io/en/latest/rank/qdigest.html
3. https://florian.github.io/count-min-sketch/
4. https://www.cs.yale.edu/homes/el327/papers/simpleMatrixSketc...
5. https://www.juanlopes.net/poly18/poly18-juan-lopes.pdf
6. https://courses.engr.illinois.edu/cs498abd/fa2020/slides/20-...
7. https://people.csail.mit.edu/rrw/6.045-2017/encalgs-mg.pdf
8. https://www.sciencedirect.com/science/article/abs/pii/S00200...
9. It may better be described as error-speed and error-space, but I've avoided the term error because the term for programming audiences typically evokes the idea of logic errors and what I mean is statistical error.
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Monarch: Google’s Planet-Scale In-Memory Time Series Database
Ah, I misunderstood what you meant. If you are reporting static buckets I get how that is better than what folks typically do but how do you know the buckets a priori? Others back their histograms with things like https://github.com/tdunning/t-digest. It is pretty powerful as the buckets are dynamic based on the data and histograms can be added together.
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[Q] Estimator for pop median
Yes, but if you need to estimate median on the fly (e.g., over a stream of data) or in parallel there are better ways.
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How percentile approximation works (and why it's more useful than averages)
There are some newer data structures that take this to the next level such as T-Digest[1], which remains extremely accurate even when determining percentiles at the very tail end (like 99.999%)
[1]: https://arxiv.org/pdf/1902.04023.pdf / https://github.com/tdunning/t-digest
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Reducing fireflies in path tracing
[2] https://github.com/tdunning/t-digest
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Reliable, Scalable, and Maintainable Applications
T-Digest
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Show HN: Fast Rolling Quantiles for Python
This is pretty cool. The title would be a bit more descriptive if it were “Fast Rolling Quantile Filters for Python”, since the high-pass/low-pass filter functionality seems to be the focus.
The README mentions it uses binary heaps - if you’re willing to accept some (bounded) approximation, then it should be possible to reduce memory usage and somewhat reduce runtime by using a sketching data structure like Dunning’s t-digest: https://github.com/tdunning/t-digest/blob/main/docs/t-digest....
There is an open source Python implementation, although I haven’t used it and can’t vouch for its quality: https://github.com/CamDavidsonPilon/tdigest
pyroscope
- Grafana Phlare, open source database for continuous profiling at scale
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The pros and cons of eBPF profiling in K8s
What do you mean? pyroscope.io was slow for you? or the blog?
- Go garbage collector doesn't release memory
- Pyroscope - Continuous profiling platform
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Ask HN: What are some 'cool' but obscure data structures you know about?
Tries (or prefix trees).
We use them a lot at Pyroscope for compressing strings that have common prefixes. They are also used in databases (e.g indexes in Mongo) or file formats (e.g debug symbols in macOS/iOS Mach-O format are compressed using tries).
We have an article with some animations that go into details about tries in case anyone's interested [0].
[0] https://github.com/pyroscope-io/pyroscope/blob/main/docs/sto...
- How to add dynamic tags/labels to Java profiles (example)
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Question: How do you handle oversized heap analysis?
You could use continuous profiling with Pyroscope which uses async-profiler under the hood, but with the added functionality that you can add relevant tags to your VMs (example).
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JFR (Java Flight Recorder) Parser written in Go
Java Flight Recorder (JFR) is a format for collecting diagnostic and profiling data from Java applications. A while back someone created an issue for Pyroscope , an open source continuous profiler written in Go, to support ingesting profiles in JFR format, but there were no existing parsers that were also written in Go.
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flamegraph.com - a new website for uploading, analyzing, and sharing pprof profiles
This cloud version is actually a slimmed-down version of Pyroscope which is open source and so you can run it locally.
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We created flamegraph.com - A website for uploading, analyzing, and sharing flamegraphs
At Pyroscope (open source continuous profiling) we use flamegraphs extensively to visualize and analyze profiling data. However, one of the worst parts about using flamegraphs for analysis is that they are kind of annoying to share.
What are some alternatives?
EvoTrees.jl - Boosted trees in Julia
parca - Continuous profiling for analysis of CPU and memory usage, down to the line number and throughout time. Saving infrastructure cost, improving performance, and increasing reliability.
timescale-analytics - Extension for more hyperfunctions, fully compatible with TimescaleDB and PostgreSQL 📈
profefe - Continuous profiling for long-term postmortem analysis
tdigest - t-Digest data structure in Python. Useful for percentiles and quantiles, including distributed enviroments like PySpark
barrier - Open-source KVM software
PSI - Private Set Intersection Cardinality protocol based on ECDH and Bloom Filters
Grafana - The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.
AspNetCoreDiagnosticScenarios - This repository has examples of broken patterns in ASP.NET Core applications
SheetJS js-xlsx - 📗 SheetJS Spreadsheet Data Toolkit -- New home https://git.sheetjs.com/SheetJS/sheetjs
minisketch - Minisketch: an optimized library for BCH-based set reconciliation
Oat++ - 🌱Light and powerful C++ web framework for highly scalable and resource-efficient web application. It's zero-dependency and easy-portable.