MaxGekk commented on a change in pull request #23391: [SPARK-26456][SQL] Cast
date/timestamp to string by Date/TimestampFormatter
URL: https://github.com/apache/spark/pull/23391#discussion_r244957053
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
##########
@@ -230,7 +235,7 @@ object PartitioningUtils {
// Once we get the string, we try to parse it and find the partition
column and value.
val maybeColumn =
parsePartitionColumn(currentPath.getName, typeInference,
userSpecifiedDataTypes,
- validatePartitionColumns, timeZone)
+ validatePartitionColumns, timeZone, dateFormatter,
timestampFormatter)
Review comment:
I would guess the result would be the same till only textual representation
of dates is involved into a query. Let's say if you read dates from a
datasource:
```
date,id
2019-01-03,1
1019-01-03,2
1019-01-03,3
0019-01-03,4
```
```scala
scala> val df1 = spark
.read.option("header", true).option("inferSchema", true).csv("dates")
.where('date < "1582-10-01")
.groupBy('date)
.agg(max('id))
df1: org.apache.spark.sql.DataFrame = [date: string, max(id): int]
scala> df1.show
+----------+-------+
| date|max(id)|
+----------+-------+
|0019-01-03| 4|
|1019-01-03| 3|
+----------+-------+
```
```scala
scala> val df2 = Seq(("0119-01-03", 6), ("0019-01-03", 7)).toDF("date",
"id"); df2.show
+----------+---+
| date| id|
+----------+---+
|0119-01-03| 6|
|0019-01-03| 7|
+----------+---+
```
```scala
scala> val joined = df1.join(df2, df1("date") ===
df2("date")).select(df1("date"), 'id, $"max(id)"); joined.show
+----------+---+-------+
| date| id|max(id)|
+----------+---+-------+
|0019-01-03| 7| 4|
+----------+---+-------+
```
```scala
joined.write.partitionBy("date").csv("date4")
```
```shell
➜ date4 tree
.
├── _SUCCESS
└── date=0019-01-03
└── part-00001-8c7875f8-5157-4942-a240-2ef6a8cf291f.c000.csv
1 directory, 2 files
```
The same results are on Spark 2.4 and this branch.
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