I am working on a data migration project using PySpark and I realized that my initial SparkSession configuration doesn't have enough shuffle partitions for the large join operation I am performing. I already created the session at the start of my notebook, but I want to change specific settings like spark.sql.shuffle.partitions and spark.executor.memory mid-script. Is it possible to modify these configurations without stopping and restarting the entire SparkSession, and what is the exact syntax to ensure the changes take effect?
3 answers
You can absolutely change many Spark SQL configurations dynamically using the spark.conf.set() method. For example, to update your shuffle partitions, you would simply use spark.conf.set("spark.sql.shuffle.partitions", "500"). This is a very common practice when you have different stages in your pipeline that require different levels of parallelism. However, it is important to note that not all configurations can be changed after the session has started. Settings related to infrastructure, such as spark.executor.memory or spark.driver.memory, are strictly "static" and must be defined when the SparkSession is first initialized or via the spark-submit flags.
If you need to check the current value before changing it, you can use spark.conf.get("config.name").
Are you running this in a shared environment like Databricks or a local Jupyter Notebook? I ask because some cluster-wide configurations might be locked by the administrator, and even if your code runs without an error, the actual execution might ignore your manual overrides in favor of the cluster's default Spark configuration.
For dynamic SQL settings, use spark.conf.set(). If you need to change core resource settings, you unfortunately have to stop the session with spark.stop() and create a new one.
Jennifer is spot on. I’ve spent hours debugging why my memory settings weren't changing, only to realize they were static. To Charles's point: the 'Environment' tab in the Spark UI often reflects startup values; check the 'SQL' tab for specific query execution details to see if your shuffle partitions actually changed.
Robert, I am currently using a standalone cluster setup. I tried using the set method you mentioned, but I'm not seeing any change in the Spark UI's 'Environment' tab. Does the UI only show the initial startup configurations, or should it update in real-time as I call the configuration set method in my PySpark code?