PySpark is a Spark library written in Python to run Python application using Apache Spark capabilities, using PySpark we can run applications parallelly on the distributed cluster (multiple nodes). In other words, PySpark is a Python API for Apache Spark. Apache Spark is an analytical processing engine for large scale powerful distributed data processing and machine learning applications. PySpark is a Python API for Spark released by the Apache Spark community to support Python with Spark. Using PySpark, one can easily integrate and work with RDDs in Python programming language too. There are numerous features that make PySpark such an amazing framework when it comes to working with huge datasets. Whether it is to perform computations on large datasets or to just analyze them, Data Engineers are switching to this tool.
Spark basically written in Scala and later on due to its industry adaptation it’s API PySpark released for Python using Py4J. Py4J is a Java library that is integrated within PySpark and allows python to dynamically interface with JVM objects, hence to run PySpark you also need Java to be installed along with Python, and Apache Spark. You can use Anaconda distribution (widely used in the Machine Learning community) which comes with a lot of useful tools like Spyder IDE, Jupyter notebook to run PySpark applications for the development. In real time, PySpark has used a lot in the machine learning, Data scientists community, thanks to vast python machine learning libraries. Spark runs operations on billions and trillions of data on distributed clusters 100 times faster than the traditional python applications.
Programming with PySpark:
RDD: Resilient Distributed Datasets – these are basically datasets that are fault-tolerant and distributed in nature. There are two types of data operations: Transformations and Actions. Transformations are the operations that work on input data set and apply a set of transform method on them. And Actions are applied by direction PySpark to work upon them.
Data frames:Data frame is a collection of structured or semi-structured data which are organized into named columns. This supports a variety of data formats such as JSON, text, CSV, existing RDDs and many other storage systems. These data are immutable and distributed in nature. Python can be used to load these data and work upon them by filtering, sorting and so on.
Machine learning:In Machine learning, there are two major types of algorithms: Transformers and Estimators. Transforms work with the input datasets and modify it to output datasets using a function called transform(). Estimators are the algorithms that take input datasets and produces a trained output model using a function named as fit(). Without Pyspark, one has to use Scala implementation to write a custom estimator or transformer. Now, with the help of PySpark, it is easier to use mixin classes instead of using scala implementation.
Who uses PySpark?
PySpark is very well used in Data Science and Machine Learning community as there are many widely used data science libraries written in Python including NumPy, TensorFlow also used due to its efficient processing of large datasets. PySpark has been used by many organizations like Walmart, Trivago, Sanofi, Runtastic, and many more.