How do we trigger automated clean-ups in Spark?

To trigger automated clean-ups in Spark, you can use the spark.cleaner.ttl configuration property. This property specifies the maximum time (in seconds) that a task’s working data is retained after the task has completed. If the data is older than this time, it will be automatically cleaned up.

You can set the spark.cleaner.ttl property using the following code:

spark.conf.set("spark.cleaner.ttl", "3600") # Set the ttl to 3600 seconds (1 hour)

You can also trigger manual clean-ups by calling the spark.cleaner.clean method, which will immediately clean up any data that is older than the specified ttl.

spark.cleaner.clean() # Manually trigger a clean-up

Note that the spark.cleaner.cls and spark.flush properties are not related to automated clean-ups in Spark.

In Apache Spark, the spark.flush configuration property specifies whether to periodically flush the output data of a streaming query to storage. By default, this property is set to false, which means that data is not automatically flushed to storage.

To enable periodic flushing of output data, you can set the spark.flush property to true using the following code:

spark.conf.set("spark.flush", "true")

Keep in mind that enabling flushing can have a performance impact on your streaming query, as it requires additional I/O operations to write the data to storage. You should only enable flushing if you need to ensure that the output data is written to storage in a timely manner, or if you need to ensure that the data is not lost if the query fails.