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https://issues.apache.org/jira/browse/SPARK-21610?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16116126#comment-16116126
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Jen-Ming Chung edited comment on SPARK-21610 at 8/7/17 6:33 AM:
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User 'jmchung' has created a pull request for this issue:
https://github.com/apache/spark/pull/18865[https://github.com/apache/spark/pull/18865]


was (Author: cjm):
User 'jmchung' has created a pull request for this issue:
https://github.com/apache/spark/pull/18865 
[https://github.com/apache/spark/pull/18865]

> Corrupt records are not handled properly when creating a dataframe from a file
> ------------------------------------------------------------------------------
>
>                 Key: SPARK-21610
>                 URL: https://issues.apache.org/jira/browse/SPARK-21610
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.0.2, 2.2.0
>         Environment: macOs Sierra 10.12.5
>            Reporter: dmtran
>
> Consider a jsonl file with 3 records. The third record has a value of type 
> string, instead of int.
> {code}
> echo '{"field": 1}
> {"field": 2}
> {"field": "3"}' >/tmp/sample.json
> {code}
> Create a dataframe from this file, with a schema that contains 
> "_corrupt_record" so that corrupt records are kept.
> {code}
> import org.apache.spark.sql.types._
> val schema = new StructType()
>   .add("field", ByteType)
>   .add("_corrupt_record", StringType)
> val file = "/tmp/sample.json"
> val dfFromFile = spark.read.schema(schema).json(file)
> {code}
> Run the following lines from a spark-shell:
> {code}
> scala> dfFromFile.show(false)
> +-----+---------------+
> |field|_corrupt_record|
> +-----+---------------+
> |1    |null           |
> |2    |null           |
> |null |{"field": "3"} |
> +-----+---------------+
> scala> dfFromFile.filter($"_corrupt_record".isNotNull).count()
> res1: Long = 0
> scala> dfFromFile.filter($"_corrupt_record".isNull).count()
> res2: Long = 3
> {code}
> The expected result is 1 corrupt record and 2 valid records, but the actual 
> one is 0 corrupt record and 3 valid records.
> The bug is not reproduced if we create a dataframe from a RDD:
> {code}
> scala> val rdd = sc.textFile(file)
> rdd: org.apache.spark.rdd.RDD[String] = /tmp/sample.json MapPartitionsRDD[92] 
> at textFile at <console>:28
> scala> val dfFromRdd = spark.read.schema(schema).json(rdd)
> dfFromRdd: org.apache.spark.sql.DataFrame = [field: tinyint, _corrupt_record: 
> string]
> scala> dfFromRdd.show(false)
> +-----+---------------+
> |field|_corrupt_record|
> +-----+---------------+
> |1    |null           |
> |2    |null           |
> |null |{"field": "3"} |
> +-----+---------------+
> scala> dfFromRdd.filter($"_corrupt_record".isNotNull).count()
> res5: Long = 1
> scala> dfFromRdd.filter($"_corrupt_record".isNull).count()
> res6: Long = 2
> {code}



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