spark-instrumented-optimizer/docs/ml-guide.md

layout	title	displayTitle
global	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 2.3), 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.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.

The most popular native BLAS such as Intel MKL, OpenBLAS, can use multiple threads in a single operation, which can conflict with Spark's execution model.

Configuring these BLAS implementations to use a single thread for operations may actually improve performance (see SPARK-21305). It is usually optimal to match this to the number of cores each Spark task is configured to use, which is 1 by default and typically left at 1.

Please refer to resources like the following to understand how to configure the number of threads these BLAS implementations use: Intel MKL and OpenBLAS.

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:

• ALS methods for top-k recommendations for all users or items, matching the functionality in mllib (SPARK-19535). Performance was also improved for both ml and mllib (SPARK-11968 and SPARK-20587)
• Correlation and ChiSquareTest stats functions for DataFrames (SPARK-19636 and SPARK-19635)
• FPGrowth algorithm for frequent pattern mining (SPARK-14503)
• GLM now supports the full Tweedie family (SPARK-18929)
• Imputer feature transformer to impute missing values in a dataset (SPARK-13568)
• LinearSVC for linear Support Vector Machine classification (SPARK-14709)
• Logistic regression now supports constraints on the coefficients during training (SPARK-20047)

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 2.2 to 2.3

Breaking changes

There are no breaking changes.

Deprecations and changes of behavior

Deprecations

There are no deprecations.

Changes of behavior

• SPARK-21027: We are now setting the default parallelism used in OneVsRest to be 1 (i.e. serial), in 2.2 and earlier version, the OneVsRest parallelism would be parallelism of the default threadpool in scala.

From 2.1 to 2.2

Breaking changes

There are no breaking changes.

Deprecations and changes of behavior

Deprecations

There are no deprecations.

Changes of behavior

• SPARK-19787: Default value of regParam changed from 1.0 to 0.1 for ALS.train method (marked DeveloperApi). Note this does not affect the ALS Estimator or Model, nor MLlib's ALS class.
• SPARK-14772: Fixed inconsistency between Python and Scala APIs for Param.copy method.
• SPARK-11569: StringIndexer now handles NULL values in the same way as unseen values. Previously an exception would always be thrown regardless of the setting of the handleInvalid parameter.

Previous Spark versions

Earlier migration guides are archived on this page.

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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. ↩︎