slfan1989 commented on code in PR #1587:
URL: https://github.com/apache/auron/pull/1587#discussion_r2484234531
##########
native-engine/auron-serde/proto/auron.proto:
##########
@@ -267,6 +267,8 @@ enum ScalarFunction {
Factorial=65;
Hex=66;
Power=67;
+
Review Comment:
Avoid blank lines.
##########
spark-extension-shims-spark/src/test/scala/org/apache/spark/sql/auron/AuronFunctionSuite.scala:
##########
@@ -319,4 +319,21 @@ class AuronFunctionSuite
val row = df.collect().head
assert(row.isNullAt(0) && row.isNullAt(1) && row.isNullAt(2))
}
+
+ test("test function make_date") {
+ withTable("t1") {
+ sql(
+ "create table t1 using parquet as select '2025'" +
+ " as year, '03' as month, '01' as day")
+ val functions =
+ """
+ |select
+ | make_date(year, month, day)
+ |from t1
+ """.stripMargin
+
+ val df = sql(functions)
+ checkAnswer(df, Seq(Row("2025-03-01")))
Review Comment:
Spark’s `make_date(year, month, day)` returns a `DateType (invalid inputs
return NULL when ANSI is disabled)`. The current test uses `checkAnswer(df,
Seq(Row("2025-03-01")))` to assert a `string` literal; a more robust approach
is to assert the type—e.g., expect `Date.valueOf("2025-03-01")`, or at least
verify `df.schema.fields(0).dataType == DateType`.
https://spark.apache.org/docs/latest/api/sql/index.html#make_date
##########
native-engine/auron-serde/src/from_proto.rs:
##########
@@ -786,6 +786,7 @@ impl From<protobuf::ScalarFunction> for Arc<ScalarUDF> {
ScalarFunction::NullIf => f::core::nullif(),
ScalarFunction::DatePart => f::datetime::date_part(),
ScalarFunction::DateTrunc => f::datetime::date_trunc(),
+
Review Comment:
Avoid blank lines.
##########
spark-extension-shims-spark/src/test/scala/org/apache/spark/sql/auron/AuronFunctionSuite.scala:
##########
@@ -319,4 +319,21 @@ class AuronFunctionSuite
val row = df.collect().head
assert(row.isNullAt(0) && row.isNullAt(1) && row.isNullAt(2))
}
+
+ test("test function make_date") {
+ withTable("t1") {
+ sql(
+ "create table t1 using parquet as select '2025'" +
+ " as year, '03' as month, '01' as day")
+ val functions =
+ """
+ |select
+ | make_date(year, month, day)
+ |from t1
+ """.stripMargin
+
+ val df = sql(functions)
+ checkAnswer(df, Seq(Row("2025-03-01")))
Review Comment:
Cover invalid inputs and NULL semantics (suggest adding test cases)
Additional cases: `make_date(2019, 13, 1) -> NULL`, `make_date(2019, 2, 30)
-> NULL`, and any argument being `NULL` -> `NULL`. These are consistent with
the official/Databricks documentation.
##########
spark-extension-shims-spark/src/test/scala/org/apache/spark/sql/auron/AuronFunctionSuite.scala:
##########
@@ -319,4 +319,21 @@ class AuronFunctionSuite
val row = df.collect().head
assert(row.isNullAt(0) && row.isNullAt(1) && row.isNullAt(2))
}
+
+ test("test function make_date") {
+ withTable("t1") {
+ sql(
+ "create table t1 using parquet as select '2025'" +
+ " as year, '03' as month, '01' as day")
+ val functions =
+ """
+ |select
+ | make_date(year, month, day)
+ |from t1
+ """.stripMargin
+
+ val df = sql(functions)
+ checkAnswer(df, Seq(Row("2025-03-01")))
Review Comment:
Nit:
Avoid using aliases that have the same names as Spark functions. It’s better
to write:
```
select '2025' as `year`, '03' as `month`, '01' as `day`
```
instead of:
```
select '2025' as year, '03' as month, '01' as day
```
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