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If your code uses multiple threads or processes, synchronize access to the `SparkSession` and other shared resources. Use appropriate locking mechanisms (like mutexes or semaphores) to avoid race conditions. Ensure that only one thread or process at a time can access and modify shared resources. Review your code for any potential concurrency issues, especially when working with Spark's SQLContext or DataFrame. If multiple threads are interacting with the same `HandleSession`, consider using thread-safe data structures or synchronization primitives to manage access. Make sure your concurrent processes are independent and don't try to close the same session at the same time. This is especially important if you're writing to the same data source from multiple threads. Make sure that you are using Spark's built-in features for concurrency management, such as the `spark.sql.execution.arrow.enabled` to improve concurrency when you are using Arrow for data transfer.
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