PySpark
Back to glossary
Py4J is a popular library which is integrated within PySpark and allows python to dynamically interface with JVM objects.
PySpark features quite a few libraries for writing efficient programs. Furthermore, there are various external libraries that are also compatible.
Here are some of them:
PySparkSQL
A PySpark library to apply SQL-like analysis on a huge amount of structured or semi-structured data. We can also use SQL queries with PySparkSQL. It can also be connected to Apache Hive. HiveQL can be also be applied. PySparkSQL is a wrapper over the PySpark core. PySparkSQL introduced the DataFrame, a tabular representation of structured data that is similar to that of a table from a relational database management system.MLlib
MLlib is a wrapper over the PySpark and it is Spark’s machine learning (ML) library. This library uses the data parallelism technique to store and work with data. The machine-learning API provided by the MLlib library is quite easy to use. MLlib supports many machine-learning algorithms for classification, regression, clustering, collaborative filtering, dimensionality reduction, and underlying optimization primitives.GraphFrames
The GraphFrames is a purpose graph processing library that provides a set of APIs for performing graph analysis efficiently, using the PySpark core and PySparkSQL. It is optimized for fast distributed computing.Additional Resources
Back to glossary
