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[SPARK-24012][SQL] Union of map and other compatible column #21100

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[SPARK-24012][SQL] Union of map and other compatible column #21100

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a422a7f
[SPARK-24012][SQL] Union of map and other compatible column
liutang123 Apr 18, 2018
cb883d9
[SPARK-24012][SQL] change map<type1, nullable type2> and map<type1, n…
liutang123 Apr 20, 2018
19b5c6a
SPARK-24012 add same type checke for map and array in TypeCoercion#fi…
liutang123 Apr 23, 2018
0845739
SPARK-24012 code style fix
liutang123 Apr 23, 2018
670824f
SPARK-24012 code style fix
liutang123 Apr 24, 2018
8cb240f
SPARK-24012 add UT for array
liutang123 Apr 24, 2018
4b1ce36
SPARK-24012 remove UT from SQLQuerySuite
liutang123 Apr 24, 2018
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12 changes: 12 additions & 0 deletions sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala
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Expand Up @@ -111,6 +111,18 @@ object TypeCoercion {
val dataType = findTightestCommonType(f1.dataType, f2.dataType).get
StructField(f1.name, dataType, nullable = f1.nullable || f2.nullable)
}))
case (a1 @ ArrayType(et1, containsNull1), a2 @ ArrayType(et2, containsNull2))
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@cloud-fan cloud-fan Apr 23, 2018

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we can shorten the name here: hasNull1 hasNull2

if a1.sameType(a2) =>
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@cloud-fan cloud-fan Apr 23, 2018

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after shortening the name, can we merge the if to the case ... line?

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@cloud-fan cloud-fan Apr 23, 2018

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also we need blank line between these cases

findTightestCommonType(et1, et2).map(ArrayType(_, containsNull1 || containsNull2))
case (m1 @ MapType(keyType1, valueType1, n1), m2 @ MapType(keyType2, valueType2, n2))
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@cloud-fan cloud-fan Apr 23, 2018

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ditto: kt1, vt1, hasNull1

if m1.sameType(m2) =>
val keyType = findTightestCommonType(keyType1, keyType2)
val valueType = findTightestCommonType(valueType1, valueType2)
if(keyType.isEmpty || valueType.isEmpty) {
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@cloud-fan cloud-fan Apr 23, 2018

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We don't need this, it's guaranteed by m1.sameType(m2)

None
} else {
Some(MapType(keyType.get, valueType.get, n1 || n2))
}

case _ => None
}
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5 changes: 5 additions & 0 deletions sql/core/src/test/resources/sql-tests/inputs/union.sql
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Expand Up @@ -35,6 +35,11 @@ FROM (SELECT col AS col
SELECT col
FROM p3) T1) T2;

-- SPARK-24012 Union of map and other compatible columns.
SELECT map(1, 2), 'str'
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@cloud-fan cloud-fan Apr 23, 2018

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shall we also add a test for array?

UNION ALL
SELECT map(1, 2, 3, NULL), 1;

-- Clean-up
DROP VIEW IF EXISTS t1;
DROP VIEW IF EXISTS t2;
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27 changes: 19 additions & 8 deletions sql/core/src/test/resources/sql-tests/results/union.sql.out
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@@ -1,5 +1,5 @@
-- Automatically generated by SQLQueryTestSuite
-- Number of queries: 14
-- Number of queries: 15


-- !query 0
Expand Down Expand Up @@ -105,40 +105,51 @@ struct<x:int,col:int>


-- !query 9
DROP VIEW IF EXISTS t1
SELECT map(1, 2), 'str'
UNION ALL
SELECT map(1, 2, 3, NULL), 1
-- !query 9 schema
struct<>
struct<map(1, 2):map<int,int>,str:string>
-- !query 9 output

{1:2,3:null} 1
{1:2} str


-- !query 10
DROP VIEW IF EXISTS t2
DROP VIEW IF EXISTS t1
-- !query 10 schema
struct<>
-- !query 10 output



-- !query 11
DROP VIEW IF EXISTS p1
DROP VIEW IF EXISTS t2
-- !query 11 schema
struct<>
-- !query 11 output



-- !query 12
DROP VIEW IF EXISTS p2
DROP VIEW IF EXISTS p1
-- !query 12 schema
struct<>
-- !query 12 output



-- !query 13
DROP VIEW IF EXISTS p3
DROP VIEW IF EXISTS p2
-- !query 13 schema
struct<>
-- !query 13 output



-- !query 14
DROP VIEW IF EXISTS p3
-- !query 14 schema
struct<>
-- !query 14 output

13 changes: 13 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
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Expand Up @@ -896,6 +896,19 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
}
}

test("SPARK-24012 Union of map and other compatible columns") {
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@cloud-fan cloud-fan Apr 24, 2018

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cc @gatorsmile , what's the policy for end-to-end tests? Shall we add it in both the sql golden file and SQLQuerySuite?

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@gatorsmile gatorsmile Apr 24, 2018

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yes. please add them to SQLQueryTestSuite

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@cloud-fan cloud-fan Apr 24, 2018

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discussed with @gatorsmile , we should put end-to-end test in a single place, and currently we encourage people to put SQL related end-to-end test in the SQL golden files. That is to say, we should remove this test from SQLQuerySuite.

In the meanwhile, a bug fix should also have a unit test. For this case, we should add a test case in TypeCoercionSuite. @liutang123 if you are not familiar with that test suite, please let us know, we can merge your PR first and add UT in TypeCoercionSuite in a followup.

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@liutang123 liutang123 Apr 24, 2018

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@cloud-fan , Yes, I am not familiar with TypeCoercionSuite. In order to save time, in my opinion, this PR can be merged first. Thanks a lot.

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@cloud-fan cloud-fan Apr 24, 2018

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OK, please remove this test and it's ready to go.

checkAnswer(
sql(
"""
|SELECT map(1, 2), 'str'
|UNION ALL
|SELECT map(1, 2, 3, NULL), 1""".stripMargin),
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@cloud-fan cloud-fan Apr 20, 2018

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can you give some insight about why it doesn't work? I'd expect Spark first do type coercion for map(1, 2, 3, NULL), and the result is map<int, nullable int>, then Union should accept nullability difference and pass the analysis.

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@liutang123 liutang123 Apr 20, 2018

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map<int, nullable int> and map<int, not nullable int> are accepted by Union, but, string and int are not.

If types of one column can not be accepted by Union, TCWSOT(TypeCoercion.WidenSetOperationTypes) will try to coerce them to a completely identical type. TCWSOT works when all of the columns can be coerced and not work when columns can not be coerced exist.
map<int, nullable int> and map<int, not nullable int> can not be coerced, so, TCWSOT didn't work and string and int will not be coerced.

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@cloud-fan cloud-fan Apr 20, 2018

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Shall we make map<int, nullable int> and map<int, not nullable int> coerce-able?

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@liutang123 liutang123 Apr 20, 2018 •
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Of course we can.
two solution：

  1. Try cast two map types to one no matter key types are not the same or value types are not the same.
    select map(1, 2) union all map(1, 'str') will work.
  2. Cast two map types to one only when the key types are the same and value types are the same. This solution just resolve the problem that map<t1, nullable t2> and map<t1, not nullable t2> can't be union.

Hive doesn't support select map(1, 2) union all map(1, 'str'), should spark be compatible with hive?

Row.fromSeq(Seq(Map(1 -> 2), "str"))::
Row.fromSeq(Seq(Map(1 -> 2, 3 -> null), "1"))::
Nil
)
}

test("EXCEPT") {
checkAnswer(
sql("SELECT * FROM lowerCaseData EXCEPT SELECT * FROM upperCaseData"),
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