What are the most common #apache #spark #error #messages

1.       NullPointerException: This error occurs when you try to reference a null object or variable.

2.       Task not serializable: This error occurs when you try to pass a non-serializable object to a Spark task.

3.       Missing input path: This error occurs when the input path specified in the Spark job is not found.

4.       Out of memory: This error indicates that Spark has run out of memory while processing the job.

5.       IllegalArgumentException: This error occurs when one or more of the parameters passed to a Spark method are invalid.

6.       NoSuchMethodError: This error occurs when you are trying to call a method that does not exist in the Spark version you are using.

7.       ExecutorLostFailure: This error occurs when an executor node in the Spark cluster fails or is lost while processing the job.

8.       SparkException: This error message is a generic message that indicates that the Spark job failed due to an error.

9.       SparkException: This is a general exception that can occur for a variety of reasons, such as a configuration error or a problem with the Spark cluster.

10.   IllegalArgumentException: This error occurs when Spark encounters an invalid argument in the code, such as an incorrect input parameter or a missing configuration setting.

11.   NoSuchElementException: This error occurs when Spark cannot find an element in a collection or iterator.

12.   NullPointerException: This error occurs when Spark tries to use a null object reference, such as when attempting to access an object that has not been initialized.

13.   IOException: This error occurs when Spark encounters an issue reading or writing data, such as when a file is inaccessible or the Hadoop file system is down.

14.   Task failed while writing rows: This error can occur when Spark encounters a problem while writing data to an external data source, such as a database or file system.

15.   OutOfMemoryError: This error indicates that Spark has run out of memory while processing the data.

16.   ClassNotFoundException: This error occurs when Spark cannot find a class that is needed to execute the code, such as a missing dependency.