Retrieving SparkSession metrics
Until now, we created our logs to provide more information and be more useful for monitoring. Logging allows us to build customized metrics based on the necessity of our pipeline and code. However, we can also take advantage of built-in metrics from frameworks and programming languages.
When we create a SparkSession, it provides a web UI with useful metrics that can be used to monitor our pipelines. Using this, the following recipe shows you how to access and retrieve metric information from SparkSession, and use it as a tool when ingesting or processing a DataFrame.
Getting ready
You can execute this recipe using the PySpark command line or the Jupyter Notebook.
Before exploring the Spark UI metrics, let’s create a simple SparkSession using the following code:
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.master("local[1]") \
...