beliefer commented on a change in pull request #31757:
URL: https://github.com/apache/spark/pull/31757#discussion_r590977493
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/errors/QueryExecutionErrors.scala
##########
@@ -322,4 +323,237 @@ object QueryExecutionErrors {
def compilerError(e: CompileException): Throwable = {
new CompileException(failedToCompileMsg(e), e.getLocation)
}
+
+ def dataPathNotSpecifiedError(): Throwable = {
+ new IllegalArgumentException("'path' is not specified")
+ }
+
+ def createStreamingSourceNotSpecifySchemaError(): Throwable = {
+ new IllegalArgumentException(
+ s"""
+ |Schema must be specified when creating a streaming source DataFrame.
If some
+ |files already exist in the directory, then depending on the file
format you
+ |may be able to create a static DataFrame on that directory with
+ |'spark.read.load(directory)' and infer schema from it.
+ """.stripMargin)
+ }
+
+ def streamedOperatorUnsupportedByDataSourceError(
+ className: String, operator: String): Throwable = {
+ new UnsupportedOperationException(
+ s"Data source $className does not support streamed $operator")
+ }
+
+ def allPathsNotExpectedExactlyOneError(allPaths: Seq[String]): Throwable = {
+ new IllegalArgumentException("Expected exactly one path to be specified,
but " +
+ s"got: ${allPaths.mkString(", ")}")
+ }
+
+ def failedFindDataSourceError(provider: String, error: Throwable): Throwable
= {
+ new ClassNotFoundException(
+ s"""
+ |Failed to find data source: $provider. Please find packages at
+ |http://spark.apache.org/third-party-projects.html
+ """.stripMargin, error)
+ }
+
+ def useSpark2RemovedClassesError(className: String, e: Throwable): Throwable
= {
+ new ClassNotFoundException(s"$className was removed in Spark 2.0. " +
+ "Please check if your library is compatible with Spark 2.0", e)
+ }
+
+ def findIncompatibleDataSourceRegisterError(e: Throwable): Throwable = {
+ new ClassNotFoundException(
+ s"""
+ |Detected an incompatible DataSourceRegister. Please remove the
incompatible
+ |library from classpath or upgrade it. Error: ${e.getMessage}
+ """.stripMargin, e)
+ }
+
+ def unrecognizedFileFormatError(format: String): Throwable = {
+ new IllegalStateException(s"unrecognized format $format")
+ }
+
+ def sparkUpgradeInReadError(
+ format: String, config: String, option: String): SparkUpgradeException =
{
+ new SparkUpgradeException("3.0",
+ s"""
+ |reading dates before 1582-10-15 or timestamps before
1900-01-01T00:00:00Z from $format
+ |files can be ambiguous, as the files may be written by Spark 2.x or
legacy versions of
+ |Hive, which uses a legacy hybrid calendar that is different from
Spark 3.0+'s Proleptic
+ |Gregorian calendar. See more details in SPARK-31404. You can set the
SQL config
+ |'$config' or the datasource option '$option' to 'LEGACY' to rebase
the datetime values
+ |w.r.t. the calendar difference during reading. To read the datetime
values as it is,
+ |set the SQL config '$config' or the datasource option '$option' to
'CORRECTED'.
+ """.stripMargin, null)
+ }
+
+ def sparkUpgradeInWriteError(format: String, config: String):
SparkUpgradeException = {
+ new SparkUpgradeException("3.0",
+ s"""
+ |writing dates before 1582-10-15 or timestamps before
1900-01-01T00:00:00Z into $format
+ |files can be dangerous, as the files may be read by Spark 2.x or
legacy versions of Hive
+ |later, which uses a legacy hybrid calendar that is different from
Spark 3.0+'s Proleptic
+ |Gregorian calendar. See more details in SPARK-31404. You can set
$config to 'LEGACY' to
+ |rebase the datetime values w.r.t. the calendar difference during
writing, to get maximum
+ |interoperability. Or set $config to 'CORRECTED' to write the
datetime values as it is,
+ |if you are 100% sure that the written files will only be read by
Spark 3.0+ or other
+ |systems that use Proleptic Gregorian calendar.
+ """.stripMargin, null)
+ }
+
+ def buildReaderUnsupportedForFileFormatError(format: String): Throwable = {
+ new UnsupportedOperationException(s"buildReader is not supported for
$format")
+ }
+
+ def jobAbortedError(cause: Throwable): Throwable = {
+ new SparkException("Job aborted.", cause)
+ }
+
+ def taskFailedWhileWriteRowsError(cause: Throwable): Throwable = {
+ new SparkException("Task failed while writing rows.", cause)
+ }
+
+ def readCurrentFileNotFoundError(e: FileNotFoundException): Throwable = {
+ new FileNotFoundException(
+ s"""
+ |${e.getMessage}\n
+ |It is possible the underlying files have been updated. You can
explicitly invalidate
+ |the cache in Spark by running 'REFRESH TABLE tableName' command in
SQL or by
+ |recreating the Dataset/DataFrame involved.
+ """.stripMargin)
+ }
+
+ def unsupportedSaveModeError(saveMode: String, pathExists: Boolean):
Throwable = {
+ new IllegalStateException(s"unsupported save mode $saveMode ($pathExists)")
+ }
+
+ def unableClearOutputDirectoryError(staticPrefixPath: Path): Throwable = {
+ new IOException(s"Unable to clear output directory $staticPrefixPath prior
to writing to it")
+ }
+
+ def unableClearPartitionDirectoryError(path: Path): Throwable = {
+ new IOException(s"Unable to clear partition directory $path prior to
writing to it")
+ }
+
+ def failedCastValueToDataTypeForPartitionColumnError(
+ value: String, dataType: DataType, columnName: String): Throwable = {
+ new RuntimeException(s"Failed to cast value `$value` to " +
+ s"`$dataType` for partition column `$columnName`")
+ }
+
+ def typeUnsupportedError(dataType: DataType): Throwable = {
Review comment:
OK
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