A Hivemall wrapper for Spark
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README.md

Build Status License

NOTE: This work will be merged into hivemall in a future release!

This is a simple wrapper implementation of Hivemall for Spark. This can make highly scalable machine learning algorithms available in DataFrame and HiveContext.

Quick Installation

// Fetch an initialization script for hivemall-spark
# wget https://raw.githubusercontent.com/maropu/hivemall-spark/master/scripts/ddl/define-udfs.sh

// Download Spark-v1.5.1 from 'http://spark.apache.org/downloads.html' and
// invoke an interactive shell
# $SPARK_HOME/bin/spark-shell --packages maropu:hivemall-spark:0.0.6

scala> :load define-udfs.sh

Hivemall in DataFrame

DataFrame is a distributed collection of data with names, types, and qualifiers. To apply Hivemall fuctions in DataFrame, you type codes below;

// Assume that an input format is as follows:
//   1.0,[1:0.5,3:0.3,8:0.1]
//   0.0,[2:0.1,3:0.8,7:0.4,9:0.1]
//   ...
scala> val trainRdd = sc.textFile(...).map(HmLabeledPoint.parse)

scala> :paste

sqlContext.createDataFrame(trainRdd)
  .train_logregr(add_bias($"feature"), $"label")
  .groupby("feature")
  .agg("weight"->"avg")

More details can be found in tutorials.

Hivemall in HiveContext

For those try Hivemall in HiveContext, run a script to register the user-defined functions of Hivemall in spark-shell and say a SQL statements as follows;

scala> :paste

sql("
  SELECT feature, AVG(weight) AS weight
    FROM (
      SELECT train_logregr(add_bias(features), label AS(feature, weight)
        FROM trainTable
    ) model
    GROUP BY feature")

Hivemall in Spark Streaming

Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. A Hivemall model built from a batch of training data can be easily applied into these streams;

// Assume that an input streaming format is as follows:
//   1.0,[1:0.5,3:0.3,8:0.1]
//   0.0,[2:0.1,3:0.8,7:0.4,9:0.1]
//   ...
scala> val testData = ssc.textFileStream(...).map(LabeledPoint.parse)

scala> :paste

testData.predict { case testDf =>
  // Explode features in input streams
  val testDf_exploded = ...

  val predictDf = testDf_exploded
    .join(model, testDf_exploded("feature") === model("feature"), "LEFT_OUTER")
    .select($"rowid", ($"weight" * $"value").as("value"))
    .groupby("rowid").sum("value")
    .select($"rowid", sigmoid($"SUM(value)"))

  predictDf
}

Support Status

Hivemall v0.4.1-alpha.6 is currently incorporated in hivemall-spark and the most functions can be available in HiveContext. On the other hand, functions listed below are available in DataFrame:

  • regression

  • binary/multiclass classification

  • nearest neighbors

  • some utility functions

API Documentations

TBW

System Requirements

  • Spark 1.6.1

Presentations

TODO

  • Support python APIs for Hivemall