Usable in Java, Scala, Python, and R.
MLlib fits into Spark's APIs and interoperates with NumPy in Python (as of Spark 0.9) and R libraries (as of Spark 1.5). You can use any Hadoop data source (e.g. HDFS, HBase, or local files), making it easy to plug into Hadoop workflows.
High-quality algorithms, 100x faster than MapReduce.
Spark excels at iterative computation, enabling MLlib to run fast. At the same time, we care about algorithmic performance: MLlib contains high-quality algorithms that leverage iteration, and can yield better results than the one-pass approximations sometimes used on MapReduce.
Runs on existing Hadoop clusters and data.
If you have a Hadoop 2 cluster, you can run Spark and MLlib without any pre-installation. Otherwise, Spark is easy to run standalone or on EC2 or Mesos. You can read from HDFS, HBase, or any Hadoop data source.
MLlib contains many algorithms and utilities, including:
Refer to the MLlib guide for usage examples.
MLlib is developed as part of the Apache Spark project. It thus gets tested and updated with each Spark release.
If you have questions about the library, ask on the Spark mailing lists.
MLlib is still a rapidly growing project and welcomes contributions. If you'd like to submit an algorithm to MLlib, read how to contribute to Spark and send us a patch!