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https://issues.apache.org/jira/browse/SPARK-42118?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17680419#comment-17680419
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Hyukjin Kwon commented on SPARK-42118:
--------------------------------------
As a workaround you can do:
{code}
val df = spark.read.format("json").option("multiLine", true).load("/tmp/json")
val newDF = spark.createDataFrame(df.rdd, df.schema)
{code}
then
{code}
df.show(false)
df.count
{code}
will show the consistent output.
> Wrong result when parsing a multiline JSON file with differing types for same
> column
> ------------------------------------------------------------------------------------
>
> Key: SPARK-42118
> URL: https://issues.apache.org/jira/browse/SPARK-42118
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.2.1
> Reporter: Dilip Biswal
> Priority: Major
>
> Here is a simple reproduction of the problem. We have a JSON file whose
> content looks like following and is in multiLine format.
> {code}
> [{"name":""},{"name":123.34}]
> {code}
> Here is the result of spark query when we read the above content.
> scala> val df = spark.read.format("json").option("multiLine",
> true).load("/tmp/json")
> df: org.apache.spark.sql.DataFrame = [name: double]
> scala> df.show(false)
> +----+
> |name|
> +----+
> |null|
> +----+
> scala> df.count
> res5: Long = 2
> This is quite a serious problem for us as it's causing us to master corrupt
> data in lake. If there is some issue with parsing the input, we expect spark
> set the "_corrupt_record" so that we can act on it. Please note that df.count
> is reporting 2 rows where as df.show only reports 1 row with null value.
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