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.
What are the implications?
- MLlib will still support the RDD-based API in
spark.mllibwith 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 2.3), 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.mlScala 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.
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
proxies by default.
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
release of Spark:
ALSmethods for top-k recommendations for all users or items, matching the functionality in
mllib(SPARK-19535). Performance was also improved for both
mllib(SPARK-11968 and SPARK-20587)
ChiSquareTeststats functions for
DataFrames(SPARK-19636 and SPARK-19635)
FPGrowthalgorithm for frequent pattern mining (SPARK-14503)
GLMnow supports the full
Imputerfeature transformer to impute missing values in a dataset (SPARK-13568)
LinearSVCfor linear Support Vector Machine classification (SPARK-14709)
- Logistic regression now supports constraints on the coefficients during training (SPARK-20047)
MLlib is under active development.
The APIs marked
DeveloperApi may change in future releases,
and the migration guide below will explain all changes between releases.
From 2.1 to 2.2
There are no breaking changes.
Deprecations and changes of behavior
There are no deprecations.
Changes of behavior
Default value of
DeveloperApi). Note this does not affect the
ALSEstimator or Model, nor MLlib’s
Fixed inconsistency between Python and Scala APIs for
NULLvalues in the same way as unseen values. Previously an exception would always be thrown regardless of the setting of the
Previous Spark versions
Earlier migration guides are archived on this page.