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), ("1019-01-03", 7)).toDF("date", "id")
df2: org.apache.spark.sql.DataFrame = [date: string, id: int]
scala> df2.show
+----------+---+
| date| id|
+----------+---+
|0119-01-03| 6|
|1019-01-03| 7|
+----------+---+
```
```scala
scala> val joined = df1.join(df2, df1("date") ===
df2("date")).select(df1("date"), 'id, $"max(id)"); joined.show
+----------+---+-------+
| date| id|max(id)|
+----------+---+-------+
|1019-01-03| 7| 3|
+----------+---+-------+
```
```scala
joined.write.partitionBy("date").csv("date2")
```
```shell
$ tree date2
.
├── _SUCCESS
└── date=1019-01-03
├── _SUCCESS
├── _committed_2042414757166556120
├── _started_2042414757166556120
└──
part-00001-tid-2042414757166556120-5729e821-9cf3-41f1-94af-c909bd0f9972-1621.c000.csv
1 directory, 5 files
```
The same result on Spark 2.4 and this branch.
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