@Sanjoy Sen​ :

It looks like the UDF function is making a synchronous HTTP request to the REST API for each row in the dataframe, which can cause performance issues when processing a large amount of data.

To improve the performance, you can consider the following approaches:

1. Batch the data: Instead of sending each row to the REST API, you can batch the data and send multiple rows at once. This will reduce the number of HTTP requests and improve the overall performance. You can use the foreachPartition API of Spark to send the data in batches.
2. Use an asynchronous HTTP client: You can use an asynchronous HTTP client like aiohttp or httpx in the UDF function to send the data asynchronously. This will allow you to send multiple HTTP requests in parallel and improve the overall performance.
3. Use a distributed system: If the data is too large to be processed on a single machine, you can consider using a distributed system like Apache Kafka or Apache Spark Streaming to process the data in real-time and send it to the REST API.
4. Use a dedicated data processing framework: You can use a dedicated data processing framework like Apache Beam or Apache Flink to process the data and send it to the REST API. These frameworks are designed for processing large amounts of data and can handle data ingestion, processing, and delivery.

In general, sending data row-by-row from a UDF can be inefficient and cause performance issues, especially when dealing with large datasets. It's better to use batch processing and asynchronous HTTP requests to improve the performance.