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[SPARK-26540][SQL] Support PostgreSQL numeric arrays without precision/scale #23458

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[SPARK-26540][SQL] Support PostgreSQL numeric arrays without precision/scale #23458

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[SPARK-26540][SQL] Support PostgreSQL numeric arrays without precisio…
dongjoon-hyun Jan 5, 2019
74215de
Add a test at PostgresIntegrationSuite
dongjoon-hyun Jan 5, 2019
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12 changes: 8 additions & 4 deletions ...integration-tests/src/test/scala/org/apache/spark/sql/jdbc/PostgresIntegrationSuite.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -46,14 +46,15 @@ class PostgresIntegrationSuite extends DockerJDBCIntegrationSuite {
conn.prepareStatement("CREATE TABLE bar (c0 text, c1 integer, c2 double precision, c3 bigint, "
+ "c4 bit(1), c5 bit(10), c6 bytea, c7 boolean, c8 inet, c9 cidr, "
+ "c10 integer[], c11 text[], c12 real[], c13 numeric(2,2)[], c14 enum_type, "
+ "c15 float4, c16 smallint)").executeUpdate()
+ "c15 float4, c16 smallint, c17 numeric[])").executeUpdate()
conn.prepareStatement("INSERT INTO bar VALUES ('hello', 42, 1.25, 123456789012345, B'0', "
+ "B'1000100101', E'\\\\xDEADBEEF', true, '172.16.0.42', '192.168.0.0/16', "
+ """'{1, 2}', '{"a", null, "b"}', '{0.11, 0.22}', '{0.11, 0.22}', 'd1', 1.01, 1)"""
+ """'{1, 2}', '{"a", null, "b"}', '{0.11, 0.22}', '{0.11, 0.22}', 'd1', 1.01, 1, """
+ "'{111.2222, 333.4444}')"
).executeUpdate()
conn.prepareStatement("INSERT INTO bar VALUES (null, null, null, null, null, "
+ "null, null, null, null, null, "
+ "null, null, null, null, null, null, null)"
+ "null, null, null, null, null, null, null, null)"
).executeUpdate()

conn.prepareStatement("CREATE TABLE ts_with_timezone " +
Expand Down Expand Up @@ -85,7 +86,7 @@ class PostgresIntegrationSuite extends DockerJDBCIntegrationSuite {
assert(rows.length == 2)
// Test the types, and values using the first row.
val types = rows(0).toSeq.map(x => x.getClass)
assert(types.length == 17)
assert(types.length == 18)
assert(classOf[String].isAssignableFrom(types(0)))
assert(classOf[java.lang.Integer].isAssignableFrom(types(1)))
assert(classOf[java.lang.Double].isAssignableFrom(types(2)))
Expand All @@ -103,6 +104,7 @@ class PostgresIntegrationSuite extends DockerJDBCIntegrationSuite {
assert(classOf[String].isAssignableFrom(types(14)))
assert(classOf[java.lang.Float].isAssignableFrom(types(15)))
assert(classOf[java.lang.Short].isAssignableFrom(types(16)))
assert(classOf[Seq[BigDecimal]].isAssignableFrom(types(17)))
assert(rows(0).getString(0).equals("hello"))
assert(rows(0).getInt(1) == 42)
assert(rows(0).getDouble(2) == 1.25)
Expand All @@ -123,6 +125,8 @@ class PostgresIntegrationSuite extends DockerJDBCIntegrationSuite {
assert(rows(0).getString(14) == "d1")
assert(rows(0).getFloat(15) == 1.01f)
assert(rows(0).getShort(16) == 1)
assert(rows(0).getSeq(17) ==
Seq("111.222200000000000000", "333.444400000000000000").map(BigDecimal(_).bigDecimal))

// Test reading null values using the second row.
assert(0.until(16).forall(rows(1).isNullAt(_)))
Expand Down
5 changes: 4 additions & 1 deletion sql/core/src/main/scala/org/apache/spark/sql/jdbc/PostgresDialect.scala
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Expand Up @@ -60,7 +60,10 @@ private object PostgresDialect extends JdbcDialect {
case "bytea" => Some(BinaryType)
case "timestamp" | "timestamptz" | "time" | "timetz" => Some(TimestampType)
case "date" => Some(DateType)
case "numeric" | "decimal" => Some(DecimalType.bounded(precision, scale))
case "numeric" | "decimal" if precision != 0 || scale != 0 =>
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@maropu maropu Jan 5, 2019

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We need to check scale != 0, too?

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@dongjoon-hyun dongjoon-hyun Jan 5, 2019

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Yes. Please see non-array type logic here https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala#L224-L226.

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@maropu maropu Jan 5, 2019

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Aha, I see. Thanks.

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@mgaido91 mgaido91 Jan 5, 2019

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Not a very important point, but I think we best use precision > 0 || scale > 0. I mean, there may happen that scale is negative. In that case, precision must be > 0. In the above code a numeric(0, -10) which is an invalid combination is parsed as DECIMAL(0, -10). Probably, we can rely that this case never happens, but I think having code robust also for error conditions is better.

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@dongjoon-hyun dongjoon-hyun Jan 5, 2019 •
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Good point. If we consider negative scale cases, we should change all of the existing code together to be consistent.

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@mgaido91 mgaido91 Jan 5, 2019

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+1 on this. We can probably also do that in a followup PR.

Some(DecimalType.bounded(precision, scale))
case "numeric" | "decimal" =>
Some(DecimalType.SYSTEM_DEFAULT)
case _ => None
}

Expand Down