PySpark
PySpark is a python-based API used for the Spark implementation and is written in Scala programming language. Basically, to support Python with Spark, the Apache Spark community released a tool, PySpark. With PySpark, one can work with RDDs in a python programming language also as it contains a library called Py4j for this. If one is familiar with Python and its libraries such as Pandas, then it is a good language to learn. It is used to create more scalable analyses and pipelines. One can opt for PySpark due to its fault-tolerant nature. Basically, it is a tool released to support Python with Spark.
Features of PySpark
- It shows low latency.
- It is immutable.
- It is fault tolerant.
- It supports Spark, Yarn, and Mesos cluster managers.
- It has ANSI SQL support.
- It is dynamic in nature.
Limitations of PySpark
- It is hard to express.
- Less efficient
- If one requires streaming, then the user has to switch from Python to Scala.
Some of the organizations that use PySpark:
- Amazon
- Walmart
- Trivago
- Sanofi
Difference between PySpark and Python
PySpark is the Python API that is used for Spark. Basically, it is a collection of Apache Spark, written in Scala programming language and Python programming to deal with data. Spark is a big data computational engine, whereas Python is a programming language. To work with PySpark, one needs to have basic knowledge of Python and Spark. The market trends of PySpark and Python are expected to increase in the next 2 years. Both terms have their own features, limitations, and differences. So, let’s check what aspects they differ.