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:

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 package.

What are the implications?

Why is MLlib switching to the DataFrame-based API?

What is “Spark ML”?

Is MLlib deprecated?


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.

Highlights in 2.2

The list below highlights some of the new features and enhancements added to MLlib in the 2.2 release of Spark:

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 2.1 to 2.2

Breaking changes

There are no breaking changes.

Deprecations and changes of behavior


There are no deprecations.

Changes of behavior

Previous Spark versions

Earlier migration guides are archived on this page.

  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.