Package0.4.0.1Data
data-sketches
Stochastic streaming algorithms for approximate computation on large datasets. Includes KLL, HLL, Theta, Count-Min, and REQ sketches.
- Version0.4.0.1
- CategoryData
- LicenceLicenseRef-Apache
- AuthorIan Duncan, Rob Bassi
- Maintainerian@
iankduncan. com - Homepagegithub.
com/ iand675/ datasketches-haskell#readme - Pinned byhackage data-sketches 0.
4. 0. 1 - Sourcedata-sketches-0.
4. 0. 1 on Hackage
Modules
- DataSketches.Distinct.HyperLogLogHyperLogLog sketch for cardinality (distinct count) estimation. "How many different things have I seen?" — not how many total, but how
- DataSketches.Distinct.ThetaTheta sketch for distinct counting with full set algebra. Like HyperLogLog, Theta counts distinct items — but it can also do set
- DataSketches.Frequencies.CountMinCount-Min sketch for approximate frequency estimation. "How many times has this particular item appeared?" — imagine counting
- DataSketches.Quantiles.KLLKLL (Karnin-Lang-Liberty) sketch for approximate quantile computation
- DataSketches.Quantiles.RelativeErrorQuantileRelative Error Quantiles (REQ) sketch for approximate quantile computation
Description
Please see the README on GitHub at https://github.com/iand675/datasketches-haskell#readme
Depends on
4 packages- base-4.22.0.0with GHC
- data-sketches-core-0.3.0.0in this set
- primitive-0.9.1.0in this set
- vector-0.13.2.0in this set