Difference between PySpark and Python
Last Updated :
31 Jan, 2023
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.
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
Python
Python is a high-level, general programming, and most widely used language, developed by Guido van Rossum during 1985- 1990. It is an interactive and object-oriented language. Python has a framework like any other programming language capable of executing other programming code such as C and C++. Python is very high in demand in the market. All the major organizations look for great Python Programmers for developing websites, software components, and applications or to work and deal with technologies like Data Science, Artificial Intelligence, and Machine Learning.
Features of Python
- It is easy to learn and use.
- It is a cross-platform language.
- It is easy to maintain.
- It is dynamically typed.
- It has large community support.
- It has extensible features.
Limitations of Python
- It might be slower because it is an interpreted language.
- Threading of Python is not optimal due to Global Interpreter Lock.
- It is not supported by Android or iOS.
- It consumes a lot of memory.
Some of the Application areas of Python are:
- Web Development
- Game Development
- Artificial Intelligence and Machine Learning
- Software Development
- Enterprise-level/Business Applications
Difference between PySpark and Python
| | PySpark
| Python
|
|---|
| 1. | PySpark is easy to write and also very easy to develop parallel programming. | Python is a cross-platform programming language, and one can easily handle it. |
|---|
| 2. | One does not have proper and efficient tools for Scala implementation. | As python is a very productive language, one can easily handle data in an efficient way. |
|---|
| 3. | It provides the algorithm which is already implemented so that one can easily integrate it. | As python language is flexible, one can easily do the analysis of data. |
|---|
| 4. | It is a memory computation. | It uses internal memory and nonobjective memory as well. |
|---|
| 5. | It only provides R-related and data science-related libraries. | It supports R programming-related libraries with data science, machine learning, etc libraries too. |
|---|
| 6. | It allows distribution processing. | It allows to implementation a single thread. |
|---|
| 7. | It can process the data in real-time. | It can also process data in real-time with huge amounts. |
|---|
| 8. | Before implementation, one requires to have Spark and Python fundamental knowledge. | Before implementation, one must know the fundamentals of any programming language. |
|---|
Conclusion
Both PySpark and Python have their own advantages and disadvantages but one should consider PySpark due to its fault-tolerant nature while Python is a high programming language for all purposes. Python is having very high demand in the market nowadays to create websites and software components. It is up to the users to decide which suits them better according to their system and requirements.
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