Question

What are the key advantages and disadvantages of PySpark?

Answer 1

Following is a list of key advantages and disadvantages of PySpark:

Advantages of PySpark:

1. PySpark is an easy-to-learn language. You can learn and implement it easily if you know Python and Apache Spark.
2. PySpark is simple to use. It provides parallelized codes that are simple to write.
3. Error handling is simple in the PySpark framework. You can easily handle errors and manage synchronization points
4. PySpark is a Python API for Apache Spark. It provides great library support. Python has a huge library collection for working in data science and data visualization compared to other languages.
5. Many important algorithms are already written and implemented in Spark. It provides many algorithms in Machine Learning or Graphs.

Disadvantages of PySpark:

1. PySpark is based on Hadoop's MapReduce model, so sometimes, it becomes challenging to manage and express problems using the MapReduce model.
2. Since Apache Spark was originally written in Scala while using PySpark in Python programs, they are not as efficient as other programming models. It is approximate 10x times slower than the Scala programs. Due to this reason, it negatively impacts the performance of heavy data processing applications.
3. The Spark Streaming API in PySpark is not as efficient as Scala. It still requires improvements.
4. In PySpark, the nodes are abstracted, and it uses the abstracted network, so it cannot be used to modify the internal function of the Spark. Scala is preferred in this case.