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Posted to issues@spark.apache.org by "Apache Spark (Jira)" <ji...@apache.org> on 2022/03/11 12:58:00 UTC
[jira] [Commented] (SPARK-38523) Failure on referring to the corrupt record from CSV
[ https://issues.apache.org/jira/browse/SPARK-38523?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17504910#comment-17504910 ]
Apache Spark commented on SPARK-38523:
--------------------------------------
User 'MaxGekk' has created a pull request for this issue:
https://github.com/apache/spark/pull/35817
> Failure on referring to the corrupt record from CSV
> ---------------------------------------------------
>
> Key: SPARK-38523
> URL: https://issues.apache.org/jira/browse/SPARK-38523
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.3.0
> Reporter: Max Gekk
> Priority: Major
>
> The file below has a invalid value in a field:
> {code:java}
> 0,2013-111_11 12:13:14
> 1,1983-08-04 {code}
> where the timestamp 2013-111_11 12:13:14 is incorrect.
> The query fails when it refers to the corrupt record column:
> {code:java}
> spark.read.format("csv")
> .option("header", "true")
> .schema(schema)
> .load("csv_corrupt_record.csv")
> .filter($"_corrupt_record".isNotNull) {code}
> with the exception:
> {code:java}
> org.apache.spark.sql.AnalysisException:
> Since Spark 2.3, the queries from raw JSON/CSV files are disallowed when the
> referenced columns only include the internal corrupt record column
> (named _corrupt_record by default). For example:
> spark.read.schema(schema).csv(file).filter($"_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) {code}
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