Exception in thread "main" org.apache.spark.sql.catalyst.errors.package$TreeNodeException: execute, tree:

I'm sorry, but without additional context or information, it is not possible to provide a specific solution to this error message.

However, this error message suggests that there is an issue with executing a particular part of the code in Apache Spark SQL. It could be caused by a variety of reasons, such as incorrect syntax, invalid input data, or a problem with the Spark SQL environment.

If you can provide more details about the code and the context in which the error occurred, it may be possible to provide more specific guidance.

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23/06/11 20:11:09 INFO internal.SharedState: Warehouse path is '/export/servers/spark/spark-warehouse'. Exception in thread "main" org.apache.spark.sql.AnalysisException: cannot resolve 'date_format(unix_timestamp(`timestamp`, 'yyyy-MM-dd\'T\'HH:mm:ss.SSS'), 'yyyy-MM-dd HH:mm:ss')' due to data type mismatch: argument 1 requires timestamp type, however, 'unix_timestamp(`timestamp`, 'yyyy-MM-dd\'T\'HH:mm:ss.SSS')' is of bigint type.; at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42) at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:82) at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:301) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:301) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)

这个错误是由于在你的代码中调用了 `date_format` 函数，但是该函数的参数类型不匹配所致。具体来说，你使用了 `unix_timestamp` 函数将 `timestamp` 字段转换为 Unix 时间戳，但是 `unix_timestamp` 函数返回的是 `bigint` 类型，而 `date_format` 函数要求的参数是 `timestamp` 类型。因此，你需要在调用 `date_format` 函数之前将 `unix_timestamp` 函数返回的 `bigint` 类型转换为 `timestamp` 类型。你可以使用 `from_unixtime` 函数将 Unix 时间戳转换为 `timestamp` 类型，然后再调用 `date_format` 函数进行格式化。例如：

import org.apache.spark.sql.functions._

val df = spark.read.format("csv").load("input.csv")
val formattedDf = df.withColumn("timestamp", from_unixtime(unix_timestamp($"timestamp", "yyyy-MM-dd'T'HH:mm:ss.SSS")).cast("timestamp"))
                    .withColumn("formatted_timestamp", date_format($"timestamp", "yyyy-MM-dd HH:mm:ss"))
formattedDf.show()

在上面的示例中，我们使用 `from_unixtime` 函数将 `timestamp` 列中的 Unix 时间戳转换为 `timestamp` 类型，并将结果保存到新的 `timestamp` 列中。然后，我们使用 `date_format` 函数对 `timestamp` 列进行格式化，将结果保存到 `formatted_timestamp` 列中。

ERROR Executor: Exception in task 0.0 in stage 39.0 (TID 61)2]org.apache.spark.SparkException: Failed to execute user defined function (StringIndexerModel$$Lambda$3385/9616484: (string) => double) at org.apache.spark.sql.errors.QueryExecutionErrors$.failedExecuteUserDefinedFunctionError(QueryExecutionErrors.scala:190) at org.apache.spark.sql.errors.QueryExecutionErrors.failedExecuteUserDefinedFunctionError(QueryExecutionErrors.scala) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source) at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:760)

这是一个Apache Spark的错误信息，通常在处理大规模数据时会出现。该错误信息表明在执行用户自定义函数时发生了错误。原因可能是函数的实现不正确或不完整，也可能是传递给函数的参数有误。您可以检查函数的实现并确保它符合预期，或者检查传递给函数的参数是否正确。另外，您还可以尝试查看错误信息的堆栈跟踪，以获取更多有关错误的信息。