Question

Illustrate some demerits of using Spark.

a. No support for real-time processing
b. Back pressure Handling
c. No built-in machine learning libraries
d. Ineffective handling of large files

Answer 1

Spark has limitations such as no support for real-time processing, improper back pressure handling, lack of built-in machine learning libraries, and ineffective handling of large files.

Step-by-step explanation:

Spark has several demerits that should be considered:

1. No support for real-time processing: Spark is primarily designed for batch processing and does not provide efficient real-time processing capabilities.
2. Back pressure handling: Spark does not handle back pressure well, which can lead to resource allocation issues and performance degradation.
3. No built-in machine learning libraries: Unlike other big data processing frameworks like TensorFlow and PyTorch, Spark does not have built-in machine learning libraries, making it less suitable for machine learning tasks.
4. Ineffective handling of large files: Spark can struggle with handling extremely large files, as it may require extensive memory and processing power to process them efficiently.

These limitations make it important to carefully evaluate the suitability of Spark for specific use cases and consider alternative technologies when necessary.