MyDotey.QuantileEstimator
1.0.2
Commmon Library collecting multiple quantile algorithms for streaming data, concurrent and easy to use.
Quantile Estimator
Commmon Library collecting multiple quantile algorithms for streaming data, concurrent & easy to use.
Implemented in java/dotnet separately. Code is clean. Easy to read.
Usage
-
java
https://github.com/mydotey/quantile-estimator/tree/master/java -
dotnet
https://github.com/mydotey/quantile-estimator/tree/master/dotnet
Features
-
Thread Safe
-
add value: lock free
-
batch get quantiles: synchronized
-
-
Time Window
- better accuracy by time window rotate
Algorithms
-
Classic Algorithm
-
accurate
-
no data compaction
-
-
CKMS Algorithm
-
data compaction & space efficient
-
predefined quantile error
-
most used approximate algorithm
-
-
GK Algorithm
- data compaction & space efficient
-
KLL Algorithm
-
data compaction & space efficient
-
new algorithm
-
Papers
-
Greenwald and Khanna. "Space-efficient online computation of quantile summaries" in SIGMOD 2001
-
Zohar Karnin, Kevin Lang and Edo Liberty. "Optimal Quantile Approximation in Streams" in FOCS 2016
Others' Projects
-
https://github.com/umbrant/QuantileEstimation
-
https://github.com/edoliberty/streaming-quantiles
-
https://github.com/tdunning/t-digest
Developers
- Qiang Zhao <koqizhao@outlook.com>
Quantile Estimator
Commmon Library collecting multiple quantile algorithms for streaming data, concurrent & easy to use.
Implemented in java/dotnet separately. Code is clean. Easy to read.
Usage
-
java
https://github.com/mydotey/quantile-estimator/tree/master/java -
dotnet
https://github.com/mydotey/quantile-estimator/tree/master/dotnet
Features
-
Thread Safe
-
add value: lock free
-
batch get quantiles: synchronized
-
-
Time Window
- better accuracy by time window rotate
Algorithms
-
Classic Algorithm
-
accurate
-
no data compaction
-
-
CKMS Algorithm
-
data compaction & space efficient
-
predefined quantile error
-
most used approximate algorithm
-
-
GK Algorithm
- data compaction & space efficient
-
KLL Algorithm
-
data compaction & space efficient
-
new algorithm
-
Papers
-
Greenwald and Khanna. "Space-efficient online computation of quantile summaries" in SIGMOD 2001
-
Zohar Karnin, Kevin Lang and Edo Liberty. "Optimal Quantile Approximation in Streams" in FOCS 2016
Others' Projects
-
https://github.com/umbrant/QuantileEstimation
-
https://github.com/edoliberty/streaming-quantiles
-
https://github.com/tdunning/t-digest
Developers
- Qiang Zhao <koqizhao@outlook.com>
Release Notes
Commmon Library collecting multiple quantile algorithms for streaming data, concurrent and easy to use.
Dependencies
-
.NETStandard 2.0
- NLog (>= 4.5.0)
Version History
Version | Downloads | Last updated | |
---|---|---|---|
1.0.2 | 153 | 4/4/2018 |