What is spark driver support?

Spark Driver is a Spark framework's major program that was created to communicate with the Spark cluster manager and oversee a distributed Spark application. It runs on the master node or local machine, and it uses the resources given by the executor nodes to execute the application. The Spark driver supervises the execution and manages the cluster's resources to guarantee that the job is completed.

The support for the Spark driver is constantly upgraded with each release. The latest version of Spark driver support is 3.1.2, which includes the add-on of various features such as:

1. Distributed TensorFlow (TF) Capabilities: The Spark driver can now run distributed TF models with Python and Scala.

2. Kubernetes Integration: The Spark driver and the executor feature full integration capabilities with Kubernetes, allowing for more efficient resource allocation.

3. Support for Multiple Language Libraries: The Spark driver now supports various language libraries like Jupyter notebooks, GraphX, R, Scala, Python, and more.

4. Data Sources API Extensions: The latest API offers enhanced support for distributed computing and handling more data in less time with various exchange formats.

Overall, the Spark Driver Support is continuously being improved with diverse functionalities and features for optimized usage of distributed computing resources.