This document discusses Hivemall, an open source machine learning library for Apache Hive, Spark, and Pig. It provides concise summaries of Hivemall in 3 sentences or less: Hivemall is a scalable machine learning library built as a collection of Hive UDFs that allows users to perform machine learning tasks like classification, regression, and recommendation using SQL queries. Hivemall supports many popular machine learning algorithms and can run in parallel on large datasets using Apache Spark, Hive, Pig, and other big data frameworks. The document outlines how to run a machine learning workflow with Hivemall on Spark, including loading data, building a model, and making predictions.

1. Hivemall: Scalable machine learning
library for Apache Hive/Spark/Pig
1). Research Engineer, Treasure Data
Makoto YUI @myui
2016/10/26 Hadoop Summit '16, Tokyo
2). Research Engineer, NTT
Takashi Yamamuro @maropu
#hivemall

2. Plan of the talk
1. Introduction to Hivemall
2. Hivemall on Spark
2016/10/26 Hadoop Summit '16, Tokyo 2

3. 2016/10/26 Hadoop Summit '16, Tokyo 3
Hivemall enters Apache Incubator
on Sept 13, 2016 🎉
http://incubator.apache.org/projects/hivemall.html
@ApacheHivemall

4. • Makoto Yui <Treasure Data>
• Takeshi Yamamuro <NTT>
 Hivemall on Apache Spark
• Daniel Dai <Hortonworks>
 Hivemall on Apache Pig
 Apache Pig PMC member
• Tsuyoshi Ozawa <NTT>
Apache Hadoop PMC member
• Kai Sasaki <Treasure Data>
2016/10/26 Hadoop Summit '16, Tokyo 4
Initial committers

5. Champion
Nominated Mentors
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Project mentors
• Reynold Xin <Databricks, ASF member>
Apache Spark PMC member
• Markus Weimer <Microsoft, ASF member>
Apache REEF PMC member
• Xiangrui Meng <Databricks, ASF member>
Apache Spark PMC member
• Roman Shaposhnik <Pivotal, ASF member>
Apache Bigtop/Incubator PMC member

6. What is Apache Hivemall
Scalable machine learning library built
as a collection of Hive UDFs
2016/10/26 Hadoop Summit '16, Tokyo
github.com/myui/hivemall
hivemall.incubator.apache.org

7. Hivemall’s Vision: ML on SQL
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Classification with Mahout
CREATE TABLE lr_model AS
SELECT
feature, -- reducers perform model averaging in parallel
avg(weight) as weight
FROM (
SELECT logress(features,label,..) as (feature,weight)
FROM train
) t -- map-only task
GROUP BY feature; -- shuffled to reducers
✓ Machine Learning made easy for SQL developers
✓ Interactive and Stable APIs w/ SQL abstraction
This SQL query automatically runs in
parallel on Hadoop

8. Hadoop HDFS
MapReduce
(MRv1)
Hivemall
Apache YARN
Apache Tez
DAG processing
Machine Learning
Query Processing
Parallel Data
Processing Framework
Resource Management
Distributed File System
Cloud Storage
SparkSQL
Apache Spark
MESOS
Hive Pig
MLlib
Hivemall’s Technology Stack
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Amazon S3

9. List of supported Algorithms
Classification
✓ Perceptron
✓ Passive Aggressive (PA, PA1, PA2)
✓ Confidence Weighted (CW)
✓ Adaptive Regularization of Weight
Vectors (AROW)
✓ Soft Confidence Weighted (SCW)
✓ AdaGrad+RDA
✓ Factorization Machines
✓ RandomForest Classification
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Regression
✓Logistic Regression (SGD)
✓AdaGrad (logistic loss)
✓AdaDELTA (logistic loss)
✓PA Regression
✓AROW Regression
✓Factorization Machines
✓RandomForest Regression
SCW is a good first choice
Try RandomForest if SCW does not work
Logistic regression is good for getting a probability of
a positive class
Factorization Machines is good where features are
sparse and categorical ones

10. List of Algorithms for Recommendation
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K-Nearest Neighbor
✓ Minhash and b-Bit Minhash
(LSH variant)
✓ Similarity Search on Vector
Space
(Euclid/Cosine/Jaccard/Angular)
Matrix Completion
✓ Matrix Factorization
✓ Factorization Machines
(regression)
each_top_k function of Hivemall is useful for
recommending top-k items

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student class score
1 b 70
2 a 80
3 a 90
4 b 50
5 a 70
6 b 60
Top-k query processing
student class score
3 a 90
2 a 80
1 b 70
6 b 60
List top-2 students for each class

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student class score
1 b 70
2 a 80
3 a 90
4 b 50
5 a 70
6 b 60
Top-k query processing
List top-2 students for each class
SELECT * FROM (
SELECT
*,
rank() over (partition by class order by score desc)
as rank
FROM table
) t
WHERE rank <= 2

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student class score
1 b 70
2 a 80
3 a 90
4 b 50
5 a 70
6 b 60
Top-k query processing
List top-2 students for each class
SELECT
each_top_k(
2, class, score,
class, student
) as (rank, score, class, student)
FROM (
SELECT * FROM table
DISTRIBUTE BY class SORT BY class
) t

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Top-k query processing by RANK OVER()
partition by class
Node 1
Sort by class, score
rank over()
rank >= 2

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Top-k query processing by EACH_TOP_K
distributed by class
Node 1
Sort by class
each_top_k
OUTPUT only K
items

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Comparison between RANK and EACH_TOP_K
distributed by class
Sort by class
each_top_k
Sort by class, score
rank over()
rank >= 2
SORTING IS HEAVY
NEED TO
PROCESS ALL
OUTPUT only K
items
Each_top_k is very efficient where the number of class is large
Bounded Priority Queue
is utilized

17. Performance reported by TD customer
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•1,000 students in each class
•20 million classes
RANK over() query does not finishes in 24 hours 
EACH_TOP_K finishes in 2 hours 
Refer for detail
https://speakerdeck.com/kaky0922/hivemall-meetup-20160908

18. Other Supported Algorithms
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Anomaly Detection
✓ Local Outlier Factor (LoF)
Feature Engineering
✓Feature Hashing
✓Feature Scaling
(normalization, z-score)
✓ TF-IDF vectorizer
✓ Polynomial Expansion
(Feature Pairing)
✓ Amplifier
NLP
✓Basic Englist text Tokenizer
✓Japanese Tokenizer
(Kuromoji)

19. Industry use cases of Hivemall
CTR prediction of Ad click logs
• Algorithm: Logistic regression
• Freakout Inc., Smartnews, and more
Gender prediction of Ad click logs
• Algorithm: Classification
• Scaleout Inc.
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http://www.slideshare.net/eventdotsjp/hivemall

20. Industry use cases of Hivemall
Item/User recommendation
• Algorithm: Recommendation
• Wish.com, GMO pepabo
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Problem: Recommendation using hot-item is hard in hand-crafted
product market because each creator sells few single items (will
soon become out-of-stock)
minne.com

21. Industry use cases of Hivemall
Value prediction of Real estates
• Algorithm: Regression
• Livesense
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22. Industry use cases of Hivemall
User score calculation
• Algrorithm: Regression
• Klout
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bit.ly/klout-hivemall
Influencer marketing
klout.com

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24. Efficient algorithm for finding change point and outliers from
timeseries data
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J. Takeuchi and K. Yamanishi, “A Unifying Framework for Detecting Outliers and Change Points from Time Series,” IEEE transactions on
Knowledge and Data Engineering, pp.482-492, 2006.
Anomaly/Change-point Detection by ChangeFinder

25. Efficient algorithm for finding change point and outliers from
timeseries data
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Anomaly/Change-point Detection by ChangeFinder
J. Takeuchi and K. Yamanishi, “A Unifying Framework for Detecting Outliers and Change Points from Time Series,” IEEE transactions on
Knowledge and Data Engineering, pp.482-492, 2006.

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• T. Ide and K. Inoue, "Knowledge Discovery from Heterogeneous Dynamic Systems using Change-Point
Correlations", Proc. SDM, 2005T.
• T. Ide and K. Tsuda, "Change-point detection using Krylov subspace learning", Proc. SDM, 2007.
Change-point detection by Singular Spectrum Transformation

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Evaluation Metrics

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Feature Engineering – Feature Binning
Maps quantitative variables to fixed number of
bins based on quantiles/distribution
Map Ages into 3 bins

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Feature Selection – Signal Noise Ratio

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Feature Selection – Chi-Square

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Feature Transformation – Onehot encoding
Maps a categorical variable to a
unique number starting from 1

32.  Spark 2.0 support
 XGBoost Integration
 Field-aware Factorization Machines
 Generalized Linear Model
• Optimizer framework including ADAM
• L1/L2 regularization
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Other new features to come

33. Copyright©2016 NTT corp. All Rights Reserved.
Hivemall on
Takeshi Yamamuro @ NTT
Hadoop Summit 2016
Tokyo, Japan

34. 34Copyright©2016 NTT corp. All Rights Reserved.
Who am I?

35. 35Copyright©2016 NTT corp. All Rights Reserved.
What’s Spark?
• 1. Unified Engine
• support end-to-end APs, e.g., MLlib and Streaming
• 2. High-level APIs
• easy-to-use, rich optimization
• 3. Integrate broadly
• storages, libraries, ...

36. 36Copyright©2016 NTT corp. All Rights Reserved.
• Hivemall wrapper for Spark
• Wrapper implementations for DataFrame/SQL
• + some utilities for easy-to-use in Spark
• The wrapper makes you...
• run most of Hivemall functions in Spark
• try examples easily in your laptop
• improve some function performance in Spark
What’s Hivemall on Spark?

37. 37Copyright©2016 NTT corp. All Rights Reserved.
• Hivemall already has many fascinating ML
algorithms and useful utilities
• High barriers to add newer algorithms in MLlib
Why’s Hivemall on Spark?
https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark

38. 38Copyright©2016 NTT corp. All Rights Reserved.
• Most of Hivemall functions supported in Spark
v1.6 and v2.0
Current Status
- For Spark v2.0
$ git clone https://github.com/myui/hivemall
$ cd hivemall
$ mvn package –Pspark-2.0 –DskipTests
$ ls target/*spark*
target/hivemall-spark-2.0_2.11-XXX-with-dependencies.jar
...

39. 39Copyright©2016 NTT corp. All Rights Reserved.
• Most of Hivemall functions supported in Spark
v1.6 and v2.0
Current Status
- For Spark v2.0
$ git clone https://github.com/myui/hivemall
$ cd hivemall
$ mvn package –Pspark-2.0 –DskipTests
$ ls target/*spark*
target/hivemall-spark-2.0_2.11-XXX-with-dependencies.jar
...
-Pspark-1.6 for Spark v1.6

40. 40Copyright©2016 NTT corp. All Rights Reserved.
• 1. Download a Spark binary
• 2. Fetch training and test data
• 3. Load these data in Spark
• 4. Build a model
• 5. Do predictions
Running an Example

41. 41Copyright©2016 NTT corp. All Rights Reserved.
1. Download a Spark binary Running an Example
http://spark.apache.org/downloads.html

42. 42Copyright©2016 NTT corp. All Rights Reserved.
• E2006 tfidf regression dataset
• http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/dat
asets/regression.html#E2006-tfidf
2. Fetch training and test data Running an Example
$ wget http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/
regression/E2006.train.bz2
$ wget http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/
regression/E2006.test.bz2

43. 43Copyright©2016 NTT corp. All Rights Reserved.
3. Load data in Spark Running an Example
$ <SPARK_HOME>/bin/spark-shell
-conf spark.jars=hivemall-spark-2.0_2.11-XXX-with-dependencies.jar
// Create DataFrame from the bzip’d libsvm-formatted file
scala> val trainDf = sqlContext.sparkSession.read.format("libsvm”)
.load(“E2006.train.bz2")
scala> trainDf.printSchema
root
|-- label: double (nullable = true)
|-- features: vector (nullable = true)

44. 44Copyright©2016 NTT corp. All Rights Reserved.
3. Load data in Spark Running an Example
0.000357499151147113 6066:0.0007932706219604
8 6069:0.000311377727123504 6070:0.0003067549
34580457 6071:0.000276992485786437 6072:0.000
39663531098024 6074:0.00039663531098024 6075
:0.00032548335…
trainDf
Partition1 Partition2 Partition3 PartitionN
…
…
…
Load in parallel because
bzip2 is splittable

45. 45Copyright©2016 NTT corp. All Rights Reserved.
4. Build a model - DataFrame Running an Example
scala> paste:
val modelDf = trainDf.train_logregr($"features", $"label")
.groupBy("feature”)
.agg("weight" -> "avg”)

46. 46Copyright©2016 NTT corp. All Rights Reserved.
4. Build a model - SQL Running an Example
scala> trainDf.createOrReplaceTempView("TrainTable")
scala> paste:
val modelDf = sql("""
| SELECT feature, AVG(weight) AS weight
| FROM (
| SELECT train_logregr(features, label)
| AS (feature, weight)
| FROM TrainTable
| )
| GROUP BY feature
""".stripMargin)

47. 47Copyright©2016 NTT corp. All Rights Reserved.
5. Do predictions - DataFrame Running an Example
scala> paste:
val df = testDf.select(rowid(), $"features")
.explode_vector($"features")
.cache
# Do predictions
df.join(modelDf, df("feature") === model("feature"), "LEFT_OUTER")
.groupBy("rowid")
.avg(sigmoid(sum($"weight" * $"value")))

48. 48Copyright©2016 NTT corp. All Rights Reserved.
5. Do predictions - SQL Running an Example
scala> modelDf.createOrReplaceTempView(”ModelTable")
scala> df.createOrReplaceTempView(”TestTable”)
scala> paste:
sql("""
| SELECT rowid, sigmoid(value * weight) AS predicted
| FROM TrainTable t
| LEFT OUTER JOIN ModelTable m
| ON t.feature = m.feature
| GROUP BY rowid
""".stripMargin)

49. 49Copyright©2016 NTT corp. All Rights Reserved.
• Spark has overheads to call Hive UD*Fs
• Hivemall heavily depends on them
• ex.1) Compute a sigmoid function
Improve Some Functions in Spark
scala> val sigmoidFunc = (d: Double) => 1.0 / (1.0 + Math.exp(-d))
scala> val sparkUdf = functions.udf(sigmoidFunc)
scala> df.select(sparkUdf($“value”))

50. 50Copyright©2016 NTT corp. All Rights Reserved.
• Spark has overheads to call Hive UD*Fs
• Hivemall heavily depends on them
• ex.1) Compute a sigmoid function
Improve Some Functions in Spark
scala> val hiveUdf = HivemallOps.sigmoid
scala> df.select(hiveUdf($“value”))

51. 51Copyright©2016 NTT corp. All Rights Reserved.
• Spark has overheads to call Hive UD*Fs
• Hivemall heavily depends on them
• ex.1) Compute a sigmoid function
Improve Some Functions in Spark

52. 52Copyright©2016 NTT corp. All Rights Reserved.
• Spark has overheads to call Hive UD*Fs
• Hivemall heavily depends on them
• ex.2) Compute top-k for each key group
Improve Some Functions in Spark
scala> paste:
df.withColumn(
“rank”,
rank().over(Window.partitionBy($"key").orderBy($"score".desc)
) .where($"rank" <= topK)

53. 53Copyright©2016 NTT corp. All Rights Reserved.
• Spark has overheads to call Hive UD*Fs
• Hivemall heavily depends on them
• ex.2) Compute top-k for each key group
• Fixed the overhead issue for each_top_k
• See pr#353: “Implement EachTopK as a generator expression“ in Spark
Improve Some Functions in Spark
scala> df.each_top_k(topK, “key”, “score”, “value”)

54. 54Copyright©2016 NTT corp. All Rights Reserved.
• Spark has overheads to call Hive UD*Fs
• Hivemall heavily depends on them
• ex.2) Compute top-k for each key group
Improve Some Functions in Spark
~4 times faster than rank!!

55. 55Copyright©2016 NTT corp. All Rights Reserved.
• supports under development
• fast implementation of the gradient tree boosting
• widely used in Kaggle competitions
• This integration will make you...
• load built models and predict in parallel
• build multiple models in parallel
for cross-validation
3rd–Party Library Integration

56. Conclusion and Takeaway
Hivemall provides a collection of machine
learning algorithms as Hive UDFs/UDTFs
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We welcome your contributions to Apache Hivemall 

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Any feature request or questions?