SaaSHub helps you find the best software and product alternatives Learn more â
Tdigest Alternatives
Similar projects and alternatives to tdigest


InfluxDB
Power RealTime Data Analytics at Scale. Get realtime insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in realtime with unbounded cardinality.






SaaSHub
SaaSHub  Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

AspNetCoreDiagnosticScenarios
This repository has examples of broken patterns in ASP.NET Core applications

pyroscope
Discontinued Continuous Profiling Platform. Debug performance issues down to a single line of code [Moved to: https://github.com/grafana/pyroscope] (by pyroscopeio)

RoaringBitmap
A better compressed bitset in Java: used by Apache Spark, Netflix Atlas, Apache Pinot, Tablesaw, and many others

multiversionconcurrencycontrol
Implementation of multiversion concurrency control, Raft, Left Right concurrency Hashmaps and a multi consumer multi producer Ringbuffer, concurrent and parallel loadbalanced loops, parallel actors implementation in Main.java, Actor2.java and a parallel interpreter

timescaleanalytics
Extension for more hyperfunctions, fully compatible with TimescaleDB and PostgreSQL đ


FusionCache
FusionCache is an easy to use, fast and robust cache with advanced resiliency features and an optional distributed 2nd level.

tdigest
tDigest data structure in Python. Useful for percentiles and quantiles, including distributed enviroments like PySpark (by CamDavidsonPilon)



SaaSHub
SaaSHub  Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
tdigest discussion
tdigest reviews and mentions

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/tdigest

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: tdigest[1], qdigest[2], countmin sketch[3], matrixsketch[4], graphsketch[5][6], MisraGries sketch[7], topk/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: accuracyspeed[9] or accuracyspace. 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/tdigest
2. https://pdsa.readthedocs.io/en/latest/rank/qdigest.html
3. https://florian.github.io/countminsketch/
4. https://www.cs.yale.edu/homes/el327/papers/simpleMatrixSketc...
5. https://www.juanlopes.net/poly18/poly18juanlopes.pdf
6. https://courses.engr.illinois.edu/cs498abd/fa2020/slides/20...
7. https://people.csail.mit.edu/rrw/6.0452017/encalgsmg.pdf
8. https://www.sciencedirect.com/science/article/abs/pii/S00200...
9. It may better be described as errorspeed and errorspace, 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.

Monarch: Googleâs PlanetScale InMemory 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/tdigest. It is pretty powerful as the buckets are dynamic based on the data and histograms can be added together.

[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.

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 TDigest[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/tdigest

Reducing fireflies in path tracing
[2] https://github.com/tdunning/tdigest

Reliable, Scalable, and Maintainable Applications
TDigest

Show HN: Fast Rolling Quantiles for Python
This is pretty cool. The title would be a bit more descriptive if it were âFast Rolling Quantile Filters for Pythonâ, since the highpass/lowpass filter functionality seems to be the focus.
The README mentions it uses binary heaps  if youâre 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 Dunningâs tdigest: https://github.com/tdunning/tdigest/blob/main/docs/tdigest....
There is an open source Python implementation, although I havenât used it and canât vouch for its quality: https://github.com/CamDavidsonPilon/tdigest

A note from our sponsor  SaaSHub
www.saashub.com  22 Jun 2024
Stats
tdunning/tdigest is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of tdigest is Java.