Machine Learning Library (MLlib) Guide

MLlib is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.

It divides into two packages:

Using is recommended because with DataFrames the API is more versatile and flexible. But we will keep supporting spark.mllib along with the development of Users should be comfortable using spark.mllib features and expect more features coming. Developers should contribute new algorithms to if they fit the ML pipeline concept well, e.g., feature extractors and transformers.

We list major functionality from both below, with links to detailed guides.

spark.mllib: data types, algorithms, and utilities high-level APIs for ML pipelines

Some techniques are not available yet in, most notably dimensionality reduction Users can seamlessly combine the implementation of these techniques found in spark.mllib with the rest of the algorithms found in


MLlib uses the linear algebra package Breeze, which depends on netlib-java for optimised numerical processing. If natives 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 1.5 to 1.6

There are no breaking API changes in the spark.mllib or packages, but there are deprecations and changes of behavior.


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.