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MLlib: Main Guide
Machine Learning Library (MLlib) Guide

MLlib is Spark's machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. At a high level, it provides tools such as:

  • ML Algorithms: common learning algorithms such as classification, regression, clustering, and collaborative filtering
  • Featurization: feature extraction, transformation, dimensionality reduction, and selection
  • Pipelines: tools for constructing, evaluating, and tuning ML Pipelines
  • Persistence: saving and load algorithms, models, and Pipelines
  • Utilities: linear algebra, statistics, data handling, etc.

Announcement: DataFrame-based API is primary API

The MLlib RDD-based API is now in maintenance mode.

As of Spark 2.0, the RDD-based APIs in the spark.mllib package have entered maintenance mode. The primary Machine Learning API for Spark is now the DataFrame-based API in the spark.ml package.

What are the implications?

  • MLlib will still support the RDD-based API in spark.mllib with bug fixes.
  • MLlib will not add new features to the RDD-based API.
  • In the Spark 2.x releases, MLlib will add features to the DataFrames-based API to reach feature parity with the RDD-based API.
  • After reaching feature parity (roughly estimated for Spark 2.2), the RDD-based API will be deprecated.
  • The RDD-based API is expected to be removed in Spark 3.0.

Why is MLlib switching to the DataFrame-based API?

  • DataFrames provide a more user-friendly API than RDDs. The many benefits of DataFrames include Spark Datasources, SQL/DataFrame queries, Tungsten and Catalyst optimizations, and uniform APIs across languages.
  • The DataFrame-based API for MLlib provides a uniform API across ML algorithms and across multiple languages.
  • DataFrames facilitate practical ML Pipelines, particularly feature transformations. See the Pipelines guide for details.

What is "Spark ML"?

  • "Spark ML" is not an official name but occasionally used to refer to the MLlib DataFrame-based API. This is majorly due to the org.apache.spark.ml Scala package name used by the DataFrame-based API, and the "Spark ML Pipelines" term we used initially to emphasize the pipeline concept.

Is MLlib deprecated?

  • No. MLlib includes both the RDD-based API and the DataFrame-based API. The RDD-based API is now in maintenance mode. But neither API is deprecated, nor MLlib as a whole.

Dependencies

MLlib uses the linear algebra package Breeze, which depends on netlib-java for optimised numerical processing. If native libraries1 are not available at runtime, you will see a warning message and a pure JVM implementation will be used instead.

Due to licensing issues with runtime proprietary binaries, we do not include netlib-java's native proxies by default. To configure netlib-java / Breeze to use system optimised binaries, include com.github.fommil.netlib:all:1.1.2 (or build Spark with -Pnetlib-lgpl) as a dependency of your project and read the netlib-java documentation for your platform's additional installation instructions.

To use MLlib in Python, you will need NumPy version 1.4 or newer.

Migration guide

MLlib is under active development. The APIs marked Experimental/DeveloperApi may change in future releases, and the migration guide below will explain all changes between releases.

From 2.0 to 2.1

Breaking changes

Deprecated methods removed

  • setLabelCol in feature.ChiSqSelectorModel
  • numTrees in classification.RandomForestClassificationModel (This now refers to the Param called numTrees)
  • numTrees in regression.RandomForestRegressionModel (This now refers to the Param called numTrees)
  • model in regression.LinearRegressionSummary
  • validateParams in PipelineStage
  • validateParams in Evaluator

Deprecations and changes of behavior

Deprecations

  • SPARK-18592: Deprecate all Param setter methods except for input/output column Params for DecisionTreeClassificationModel, GBTClassificationModel, RandomForestClassificationModel, DecisionTreeRegressionModel, GBTRegressionModel and RandomForestRegressionModel

Changes of behavior

  • SPARK-17870: Fix a bug of ChiSqSelector which will likely change its result. Now ChiSquareSelector use pValue rather than raw statistic to select a fixed number of top features.
  • SPARK-3261: KMeans returns potentially fewer than k cluster centers in cases where k distinct centroids aren't available or aren't selected.
  • SPARK-17389: KMeans reduces the default number of steps from 5 to 2 for the k-means|| initialization mode.

Previous Spark versions

Earlier migration guides are archived on this page.

Footnotes

  1. To learn more about the benefits and background of system optimised natives, you may wish to watch Sam Halliday's ScalaX talk on High Performance Linear Algebra in Scala. ↩