[
https://issues.apache.org/jira/browse/SPARK-39731?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ivan Sadikov updated SPARK-39731:
---------------------------------
Description:
In Spark 3.x, when reading CSV data like this:
{code:java}
name,mydate
1,2020011
2,20201203{code}
and specifying date pattern as "yyyyMMdd", dates are not parsed correctly with
CORRECTED time parser policy.
For example,
{code:java}
val df = spark.read.schema("name string, mydate date").option("dateFormat",
"yyyyMMdd").option("header", "true").csv("file:/tmp/test.csv")
df.show(false){code}
Returns:
{code:java}
+----+--------------+
|name|mydate |
+----+--------------+
|1 |+2020011-01-01|
|2 |2020-12-03 |
+----+--------------+ {code}
and it used to return null instead of the invalid date in Spark 3.2 or below.
The issue appears to be caused by this PR:
[https://github.com/apache/spark/pull/32959].
A similar issue can observed in JSON data source.
test.json
{code:java}
{"date": "2020011"}
{"date": "20201203"} {code}
Running commands
{code:java}
val df = spark.read.schema("date date").option("dateFormat",
"yyyyMMdd").json("file:/tmp/test.json")
df.show(false) {code}
returns
{code:java}
+--------------+
|date |
+--------------+
|+2020011-01-01|
|2020-12-03 |
+--------------+{code}
but before the patch linked in the description it used to show:
{code:java}
+----------+
|date |
+----------+
|7500-08-09|
|2020-12-03|
+----------+{code}
which is strange either way. I will try to address it in the PR.
was:
In Spark 3.x, when reading CSV data like this:
{code:java}
name,mydate
1,2020011
2,20201203{code}
and specifying date pattern as "yyyyMMdd", dates are not parsed correctly with
CORRECTED time parser policy.
For example,
{code:java}
val df = spark.read.schema("name string, mydate date").option("dateFormat",
"yyyyMMdd").option("header", "true").csv("file:/tmp/test.csv")
df.show(false){code}
Returns:
{code:java}
+----+--------------+
|name|mydate |
+----+--------------+
|1 |+2020011-01-01|
|2 |2020-12-03 |
+----+--------------+ {code}
and it used to return null instead of the invalid date in Spark 3.2 or below.
The issue appears to be caused by this PR:
[https://github.com/apache/spark/pull/32959].
> Correctness issue when parsing dates with yyyyMMdd format in CSV and JSON
> -------------------------------------------------------------------------
>
> Key: SPARK-39731
> URL: https://issues.apache.org/jira/browse/SPARK-39731
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.4.0
> Reporter: Ivan Sadikov
> Priority: Major
>
> In Spark 3.x, when reading CSV data like this:
> {code:java}
> name,mydate
> 1,2020011
> 2,20201203{code}
> and specifying date pattern as "yyyyMMdd", dates are not parsed correctly
> with CORRECTED time parser policy.
> For example,
> {code:java}
> val df = spark.read.schema("name string, mydate date").option("dateFormat",
> "yyyyMMdd").option("header", "true").csv("file:/tmp/test.csv")
> df.show(false){code}
> Returns:
> {code:java}
> +----+--------------+
> |name|mydate |
> +----+--------------+
> |1 |+2020011-01-01|
> |2 |2020-12-03 |
> +----+--------------+ {code}
> and it used to return null instead of the invalid date in Spark 3.2 or below.
>
> The issue appears to be caused by this PR:
> [https://github.com/apache/spark/pull/32959].
>
> A similar issue can observed in JSON data source.
> test.json
> {code:java}
> {"date": "2020011"}
> {"date": "20201203"} {code}
>
> Running commands
> {code:java}
> val df = spark.read.schema("date date").option("dateFormat",
> "yyyyMMdd").json("file:/tmp/test.json")
> df.show(false) {code}
> returns
> {code:java}
> +--------------+
> |date |
> +--------------+
> |+2020011-01-01|
> |2020-12-03 |
> +--------------+{code}
> but before the patch linked in the description it used to show:
> {code:java}
> +----------+
> |date |
> +----------+
> |7500-08-09|
> |2020-12-03|
> +----------+{code}
> which is strange either way. I will try to address it in the PR.
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