kevinjmh edited a comment on issue #3357: [CARBONDATA-3491] Return updated/deleted rows count when execute update/delete sql URL: https://github.com/apache/carbondata/pull/3357#issuecomment-520737311 I try and your test case pass. Modification content I did in mentioned class: ``` override def output: Seq[Attribute] = Seq(AttributeReference("Total Rows Updated", LongType, nullable = false)()) ``` plan is similar to yours but has one more line `Total Rows Updated: bigint`: ``` == Parsed Logical Plan == UpdateTable 'UnresolvedRelation `test_return_row_count`, [b], select 'ddd' from test_return_row_count, test_return_row_count, where a = 'ccc' == Analyzed Logical Plan == Total Rows Updated: bigint ProjectForUpdate 'UnresolvedRelation `test_return_row_count`, [b] +- Project [a#103, b#104, c#105, tupleId#282, b-updatedColumn#283] +- Filter (a#103 = ccc) +- SubqueryAlias test_return_row_count +- SubqueryAlias test_return_row_count +- Project [a#103, b#104, c#105, tupleId#282, ddd AS b-updatedColumn#283] +- SubqueryAlias test_return_row_count +- Project [a#103, b#104, c#105, UDF:getTupleId() AS tupleId#282] +- SubqueryAlias test_return_row_count +- SubqueryAlias test_return_row_count +- Relation[a#103,b#104,c#105] CarbonDatasourceHadoopRelation == Optimized Logical Plan == CarbonProjectForUpdateCommand Project [a#103, c#105, UDF:getTupleId() AS tupleId#282, ddd AS b-updatedColumn#283], test_return_row_count, [b] == Physical Plan == Execute CarbonProjectForUpdateCommand +- CarbonProjectForUpdateCommand Project [a#103, c#105, UDF:getTupleId() AS tupleId#282, ddd AS b-updatedColumn#283], test_return_row_count, [b] ``` For Spark2.3, please check about following codes in `Dataset.scala` ``` private[sql] def showString( _numRows: Int, truncate: Int = 20, vertical: Boolean = false): String = { val numRows = _numRows.max(0).min(Int.MaxValue - 1) val newDf = toDF() val castCols = newDf.logicalPlan.output.map { col => // Since binary types in top-level schema fields have a specific format to print, // so we do not cast them to strings here. if (col.dataType == BinaryType) { Column(col) } else { Column(col).cast(StringType) } } val takeResult = newDf.select(castCols: _*).take(numRows + 1) ... } def select(cols: Column*): DataFrame = withPlan { Project(cols.map(_.named), planWithBarrier) } ``` see https://github.com/apache/spark/pull/20214/
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