PetarVasiljevic-DB commented on code in PR #48625:
URL: https://github.com/apache/spark/pull/48625#discussion_r1822571707
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sql/core/src/main/scala/org/apache/spark/sql/jdbc/PostgresDialect.scala:
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@@ -382,18 +382,45 @@ private case class PostgresDialect()
case Types.ARRAY =>
val tableName = rsmd.getTableName(columnIdx)
val columnName = rsmd.getColumnName(columnIdx)
- val query =
- s"""
- |SELECT pg_attribute.attndims
- |FROM pg_attribute
- | JOIN pg_class ON pg_attribute.attrelid = pg_class.oid
- | JOIN pg_namespace ON pg_class.relnamespace = pg_namespace.oid
- |WHERE pg_class.relname = '$tableName' and pg_attribute.attname =
'$columnName'
- |""".stripMargin
+
+ /*
+ Spark does not support different dimensionality per row, therefore we
retrieve the
+ dimensionality of any row from Postgres. This might fail later on as
Postgres allows
+ different dimensions per row for arrays.
+ */
+ val query = s"SELECT array_ndims($columnName) FROM $tableName LIMIT 1"
+ var arrayDimensionalityResolveNeedsFallback = true
+
try {
Using.resource(conn.createStatement()) { stmt =>
Using.resource(stmt.executeQuery(query)) { rs =>
- if (rs.next()) metadata.putLong("arrayDimension", rs.getLong(1))
+ if (rs.next()) {
Review Comment:
Well rs.next() returns false if the resultset is empty so we can just reuse
it. I have updated the PR.
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