Major Description
The file below has a invalid value in a field:
0,2013-111_11 12:13:14 1,1983-08-04
where the timestamp 2013-111_11 12:13:14 is incorrect.
The query fails when it refers to the corrupt record column:
spark.read.format("csv") .option("header", "true") .schema(schema) .load("csv_corrupt_record.csv") .filter($"_corrupt_record".isNotNull)
with the exception:
org.apache.spark.sql.AnalysisException: Since Spark 2.3, the queries from raw JSON/CSV files are disallowed when the referenced columns only include the internal corrupt record column (named _corrupt_record by default). For example: spark.read.schema(schema).csv(file).filter($"_corrupt_record".isNotNull).count() and spark.read.schema(schema).csv(file).select("_corrupt_record").show(). Instead, you can cache or save the parsed results and then send the same query. For example, val df = spark.read.schema(schema).csv(file).cache() and then df.filter($"_corrupt_record".isNotNull).count(). at org.apache.spark.sql.errors.QueryCompilationErrors$.queryFromRawFilesIncludeCorruptRecordColumnError(QueryCompilationErrors.scala:2047) at org.apache.spark.sql.execution.datasources.csv.CSVFileFormat.buildReader(CSVFileFormat.scala:116)
