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Scalable machine learning library for Hive/Hadoop
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Hivemall: Hive scalable machine learning library

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Hivemall is a scalable machine learning library that runs on Apache Hive. Hivemall is designed to be scalable to the number of training instances as well as the number of training features.


Supported Algorithms

Hivemall provides machine learning functionality as well as feature engineering functions through UDFs/UDAFs/UDTFs of Hive.


  • Perceptron

  • Passive Aggressive (PA, PA1, PA2)

  • Confidence Weighted (CW)

  • Adaptive Regularization of Weight Vectors (AROW)

  • Soft Confidence Weighted (SCW1, SCW2)

  • AdaGradRDA (with hinge loss)

My recommendation is AROW, SCW1 and AdaGradRDA, while it depends.


  • Logistic Regression using Stochastic Gradient Descent

  • AdaGrad / AdaDelta (with logistic loss)

  • Passive Aggressive Regression (PA1, PA2)

  • AROW regression

My recommendation is AdaDelta and AdaGrad, while it depends.


  • Matrix Factorization (sgd, adagrad)

  • Minhash (LSH with jaccard index)

k-Nearest Neighbor

  • Minhash (LSH with jaccard index)

  • b-Bit minhash

  • Brute-force search using cosine similarity

Feature engineering

  • Feature hashing (MurmurHash, SHA1)

  • Feature scaling (Min-Max Normalization, Z-Score)

  • Feature instances amplifier that reduces iterations on training

  • TF-IDF vectorizer

  • Bias clause

  • Data generator for one-vs-the-rest classifiers

System requirements

  • Hive 0.11 or later

  • Hive 0.9, 0.10 [out of support]

Basic Usage


Find more examples on our wiki page and find a brief introduction to Hivemall in this slide.


Copyright (C) 2015 Makoto YUI

Copyright (C) 2013-2015 National Institute of Advanced Industrial Science and Technology (AIST)

Put the above copyrights for the services/softwares that use Hivemall.


Support is through the issue list, not by a direct e-mail. Put a question label to ask a question.


Please refer the following paper for research uses:



This work was supported in part by a JSPS grant-in-aid for young scientists (B) #24700111 and a JSPS grant-in-aid for scientific research (A) #24240015.


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