MLLib alternatives and similar packages
Based on the "Science and Data Analysis" category.
Alternatively, view Apache Spark alternatives based on common mentions on social networks and blogs.
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PredictionIO
machine learning server for developers and data scientists. Built on Apache Spark, HBase and Spray -
Smile
Statistical Machine Intelligence and Learning Engine. Smile is a fast and comprehensive machine learning system. -
Spark Notebook
Scalable and stable Scala and Spark focused notebook bridging the gap between JVM and Data Scientists (incl. extendable, typesafe and reactive charts). -
Figaro
Figaro is a probabilistic programming language that supports development of very rich probabilistic models. -
FACTORIE
A toolkit for deployable probabilistic modeling, implemented as a software library in Scala. -
ND4S
N-Dimensional arrays and linear algebra for Scala with an API similar to Numpy. ND4S is a scala wrapper around ND4J. -
Libra
Libra is a dimensional analysis library based on shapeless, spire and singleton-ops. It contains out of the box support for SI units for all numeric types. -
Optimus * 96
Optimus is a library for Linear and Quadratic mathematical optimization written in Scala programming language. -
Clustering4Ever
Scala and Spark API to benchmark and analyse clustering algorithms on any vectorization you can generate -
rscala
The Scala interpreter is embedded in R and callbacks to R from the embedded interpreter are supported. Conversely, the R interpreter is embedded in Scala. -
Tyche
Probability distributions, stochastic & Markov processes, lattice walks, simple random sampling. A simple yet robust Scala library. -
Rings
An efficient library for polynomial rings. Commutative algebra, polynomial GCDs, polynomial factorization and other sci things at a really high speed. -
SwiftLearner
Simply written algorithms to help study Machine Learning or write your own implementations.
Get performance insights in less than 4 minutes
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README
Apache Spark
Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
./build/mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.)
More detailed documentation is available from the project site, at "Building Spark".
For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1,000,000,000:
scala> spark.range(1000 * 1000 * 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1,000,000,000:
>>> spark.range(1000 * 1000 * 1000).count()
Example Programs
Spark also comes with several sample programs in the examples
directory.
To run one of them, use ./bin/run-example <class> [params]
. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the examples
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Running Tests
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
Please see the guidance on how to run tests for a module, or individual tests.
There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at "Specifying the Hadoop Version and Enabling YARN" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.
Configuration
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Contributing
Please review the Contribution to Spark guide for information on how to get started contributing to the project.