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
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.
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 linear algebra packages Breeze, dev.ludovic.netlib, and netlib-java for optimised numerical processing1. Those packages may call native acceleration libraries such as Intel MKL or OpenBLAS if they are available as system libraries or in runtime library paths.
However, native acceleration libraries can’t be distributed with Spark. See MLlib Linear Algebra Acceleration Guide for how to enable accelerated linear algebra processing. If accelerated native libraries are not enabled, you will see a warning message like below and a pure JVM implementation will be used instead:
WARN BLAS: Failed to load implementation from:dev.ludovic.netlib.blas.JNIBLAS
To use MLlib in Python, you will need NumPy version 1.4 or newer.
Highlights in 3.0
The list below highlights some of the new features and enhancements added to MLlib in the
release of Spark:
- Multiple columns support was added to
StopWordsRemover(SPARK-29808) and PySpark
- Tree-Based Feature Transformation was added (SPARK-13677).
- Two new evaluators
RankingEvaluator(SPARK-28045) were added.
- Sample weights support was added in
- R API for
PowerIterationClusteringwas added (SPARK-19827).
- Added Spark ML listener for tracking ML pipeline status (SPARK-23674).
- Fit with validation set was added to Gradient Boosted Trees in Python (SPARK-24333).
RobustScalertransformer was added (SPARK-28399).
Factorization Machinesclassifier and regressor were added (SPARK-29224).
- Gaussian Naive Bayes Classifier (SPARK-16872) and Complement Naive Bayes Classifier (SPARK-29942) were added.
- ML function parity between Scala and Python (SPARK-28958).
predictRawis made public in all the Classification models.
predictProbabilityis made public in all the Classification models except
The migration guide is now archived on this page.
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. ↩