[GitHub] [spark] yaooqinn commented on a diff in pull request #36373: [SPARK-39041][SQL] Mapping Spark Query ResultSet/Schema to TRowSet/TTableSchema directly

2022-05-12 Thread GitBox 


yaooqinn commented on code in PR #36373:
URL: https://github.com/apache/spark/pull/36373#discussion_r871502997


##
sql/hive-thriftserver/src/main/java/org/apache/hive/service/cli/session/HiveSession.java:
##
@@ -182,11 +184,11 @@ OperationHandle getCrossReference(String primaryCatalog,
 
   void closeOperation(OperationHandle opHandle) throws HiveSQLException;
 
-  TableSchema getResultSetMetadata(OperationHandle opHandle)
+  TTableSchema getResultSetMetadata(OperationHandle opHandle)
   throws HiveSQLException;
 
-  RowSet fetchResults(OperationHandle opHandle, FetchOrientation orientation,
-  long maxRows, FetchType fetchType) throws HiveSQLException;
+  TRowSet fetchResults(OperationHandle opHandle, FetchOrientation orientation,
+   long maxRows, FetchType fetchType) throws 
HiveSQLException;

Review Comment:
   ```suggestion
 long maxRows, FetchType fetchType) throws HiveSQLException;
   ```



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[GitHub] [spark] yaooqinn commented on a diff in pull request #36373: [SPARK-39041][SQL] Mapping Spark Query ResultSet/Schema to TRowSet/TTableSchema directly

2022-05-12 Thread GitBox 


yaooqinn commented on code in PR #36373:
URL: https://github.com/apache/spark/pull/36373#discussion_r871496811


##
sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala:
##
@@ -373,17 +306,69 @@ private[hive] class SparkExecuteStatementOperation(
 }
 
 object SparkExecuteStatementOperation {
-  def getTableSchema(structType: StructType): TableSchema = {
-val schema = structType.map { field =>
-  val attrTypeString = field.dataType match {
-case CalendarIntervalType => StringType.catalogString
-case _: YearMonthIntervalType => "interval_year_month"
-case _: DayTimeIntervalType => "interval_day_time"
-case _: TimestampNTZType => "timestamp"
-case other => other.catalogString
-  }
-  new FieldSchema(field.name, attrTypeString, 
field.getComment.getOrElse(""))
+
+  def toTTypeId(typ: DataType): TTypeId = typ match {
+case NullType => TTypeId.NULL_TYPE
+case BooleanType => TTypeId.BOOLEAN_TYPE
+case ByteType => TTypeId.TINYINT_TYPE
+case ShortType => TTypeId.SMALLINT_TYPE
+case IntegerType => TTypeId.INT_TYPE
+case LongType => TTypeId.BIGINT_TYPE
+case FloatType => TTypeId.FLOAT_TYPE
+case DoubleType => TTypeId.DOUBLE_TYPE
+case StringType => TTypeId.STRING_TYPE
+case _: DecimalType => TTypeId.DECIMAL_TYPE
+case DateType => TTypeId.DATE_TYPE
+// TODO: Shall use TIMESTAMPLOCALTZ_TYPE, keep AS-IS now for
+// unnecessary behavior change
+case TimestampType => TTypeId.TIMESTAMP_TYPE
+case TimestampNTZType => TTypeId.TIMESTAMP_TYPE

Review Comment:
   this is for rowset schema. jdbc client side can call getString to get the 
raw string or getObject to get an java object where it is used
   



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[GitHub] [spark] yaooqinn commented on a diff in pull request #36373: [SPARK-39041][SQL] Mapping Spark Query ResultSet/Schema to TRowSet/TTableSchema directly

2022-04-28 Thread GitBox 


yaooqinn commented on code in PR #36373:
URL: https://github.com/apache/spark/pull/36373#discussion_r861445508


##
sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/RowSetUtils.scala:
##
@@ -0,0 +1,233 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import java.nio.ByteBuffer
+
+import scala.collection.JavaConverters._
+import scala.language.implicitConversions
+
+import org.apache.hive.service.rpc.thrift._
+
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.execution.HiveResult.{toHiveString, TimeFormatters}
+import org.apache.spark.sql.types.{BinaryType, BooleanType, ByteType, 
DataType, DoubleType, FloatType, IntegerType, LongType, ShortType, StringType}
+
+object RowSetUtils {
+
+  implicit def bitSetToBuffer(bitSet: java.util.BitSet): ByteBuffer = {
+ByteBuffer.wrap(bitSet.toByteArray)
+  }
+
+  def toTRowSet(
+  startRowOffSet: Long,
+  rows: Seq[Row],
+  schema: Array[DataType],
+  protocolVersion: TProtocolVersion,
+  timeFormatters: TimeFormatters): TRowSet = {
+if (protocolVersion.getValue < 
TProtocolVersion.HIVE_CLI_SERVICE_PROTOCOL_V6.getValue) {
+  toRowBasedSet(startRowOffSet, rows, schema, timeFormatters)
+} else {
+  toColumnBasedSet(startRowOffSet, rows, schema, timeFormatters)
+}
+  }
+
+  private def toRowBasedSet(
+  startRowOffSet: Long,
+  rows: Seq[Row],
+  schema: Array[DataType],
+  timeFormatters: TimeFormatters): TRowSet = {
+var i = 0
+val rowSize = rows.length
+val tRows = new java.util.ArrayList[TRow](rowSize)
+while (i < rowSize) {
+  val row = rows(i)
+  val tRow = new TRow()
+  var j = 0
+  val columnSize = row.length
+  while (j < columnSize) {
+val columnValue = toTColumnValue(j, row, schema(j), timeFormatters)
+tRow.addToColVals(columnValue)
+j += 1
+  }
+  i += 1
+  tRows.add(tRow)
+}
+new TRowSet(startRowOffSet, tRows)
+  }
+
+  private def toColumnBasedSet(
+  startRowOffSet: Long,
+  rows: Seq[Row],
+  schema: Array[DataType],
+  timeFormatters: TimeFormatters): TRowSet = {
+val rowSize = rows.length
+val tRowSet = new TRowSet(startRowOffSet, new 
java.util.ArrayList[TRow](rowSize))
+var i = 0
+val columnSize = schema.length
+while (i < columnSize) {
+  val tColumn = toTColumn(rows, i, schema(i), timeFormatters)
+  tRowSet.addToColumns(tColumn)
+  i += 1
+}
+tRowSet
+  }
+
+  private def toTColumn(
+  rows: Seq[Row], ordinal: Int, typ: DataType, timeFormatters: 
TimeFormatters): TColumn = {
+val nulls = new java.util.BitSet()
+typ match {
+  case BooleanType =>
+val values = getOrSetAsNull[java.lang.Boolean](rows, ordinal, nulls, 
true)
+TColumn.boolVal(new TBoolColumn(values, nulls))
+
+  case ByteType =>
+val values = getOrSetAsNull[java.lang.Byte](rows, ordinal, nulls, 
0.toByte)
+TColumn.byteVal(new TByteColumn(values, nulls))
+
+  case ShortType =>
+val values = getOrSetAsNull[java.lang.Short](rows, ordinal, nulls, 
0.toShort)
+TColumn.i16Val(new TI16Column(values, nulls))
+
+  case IntegerType =>
+val values = getOrSetAsNull[java.lang.Integer](rows, ordinal, nulls, 0)
+TColumn.i32Val(new TI32Column(values, nulls))
+
+  case LongType =>
+val values = getOrSetAsNull[java.lang.Long](rows, ordinal, nulls, 0L)
+TColumn.i64Val(new TI64Column(values, nulls))
+
+  case FloatType =>
+val values = getOrSetAsNull[java.lang.Float](rows, ordinal, nulls, 
0.toFloat)
+  .asScala.map(n => java.lang.Double.valueOf(n.toString)).asJava
+TColumn.doubleVal(new TDoubleColumn(values, nulls))
+
+  case DoubleType =>
+val values = getOrSetAsNull[java.lang.Double](rows, ordinal, nulls, 
0.toDouble)
+TColumn.doubleVal(new TDoubleColumn(values, nulls))
+
+  case StringType =>
+val values = getOrSetAsNull[java.lang.String](rows, ordinal, nulls, "")
+TColumn.stringVal(new TStringColumn(values, nulls))
+
+  case BinaryType =>
+val values =

[GitHub] [spark] yaooqinn commented on a diff in pull request #36373: [SPARK-39041][SQL] Mapping Spark Query ResultSet/Schema to TRowSet/TTableSchema directly

2022-04-28 Thread GitBox 


yaooqinn commented on code in PR #36373:
URL: https://github.com/apache/spark/pull/36373#discussion_r860912047


##
sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/RowSetUtils.scala:
##
@@ -0,0 +1,233 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+import java.nio.ByteBuffer
+
+import scala.collection.JavaConverters._
+import scala.language.implicitConversions
+
+import org.apache.hive.service.rpc.thrift._
+
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.execution.HiveResult.{toHiveString, TimeFormatters}
+import org.apache.spark.sql.types.{BinaryType, BooleanType, ByteType, 
DataType, DoubleType, FloatType, IntegerType, LongType, ShortType, StringType}
+
+object RowSetUtils {
+
+  implicit def bitSetToBuffer(bitSet: java.util.BitSet): ByteBuffer = {
+ByteBuffer.wrap(bitSet.toByteArray)
+  }
+
+  def toTRowSet(
+  startRowOffSet: Long,
+  rows: Seq[Row],
+  schema: Array[DataType],
+  protocolVersion: TProtocolVersion,
+  timeFormatters: TimeFormatters): TRowSet = {
+if (protocolVersion.getValue < 
TProtocolVersion.HIVE_CLI_SERVICE_PROTOCOL_V6.getValue) {
+  toRowBasedSet(startRowOffSet, rows, schema, timeFormatters)
+} else {
+  toColumnBasedSet(startRowOffSet, rows, schema, timeFormatters)
+}
+  }
+
+  private def toRowBasedSet(
+  startRowOffSet: Long,
+  rows: Seq[Row],
+  schema: Array[DataType],
+  timeFormatters: TimeFormatters): TRowSet = {
+var i = 0
+val rowSize = rows.length
+val tRows = new java.util.ArrayList[TRow](rowSize)
+while (i < rowSize) {
+  val row = rows(i)
+  val tRow = new TRow()
+  var j = 0
+  val columnSize = row.length
+  while (j < columnSize) {
+val columnValue = toTColumnValue(j, row, schema(j), timeFormatters)
+tRow.addToColVals(columnValue)
+j += 1
+  }
+  i += 1
+  tRows.add(tRow)
+}
+new TRowSet(startRowOffSet, tRows)
+  }
+
+  private def toColumnBasedSet(
+  startRowOffSet: Long,
+  rows: Seq[Row],
+  schema: Array[DataType],
+  timeFormatters: TimeFormatters): TRowSet = {
+val rowSize = rows.length
+val tRowSet = new TRowSet(startRowOffSet, new 
java.util.ArrayList[TRow](rowSize))
+var i = 0
+val columnSize = schema.length
+while (i < columnSize) {
+  val tColumn = toTColumn(rows, i, schema(i), timeFormatters)
+  tRowSet.addToColumns(tColumn)
+  i += 1
+}
+tRowSet
+  }
+
+  private def toTColumn(
+  rows: Seq[Row], ordinal: Int, typ: DataType, timeFormatters: 
TimeFormatters): TColumn = {
+val nulls = new java.util.BitSet()
+typ match {
+  case BooleanType =>
+val values = getOrSetAsNull[java.lang.Boolean](rows, ordinal, nulls, 
true)
+TColumn.boolVal(new TBoolColumn(values, nulls))
+
+  case ByteType =>
+val values = getOrSetAsNull[java.lang.Byte](rows, ordinal, nulls, 
0.toByte)
+TColumn.byteVal(new TByteColumn(values, nulls))
+
+  case ShortType =>
+val values = getOrSetAsNull[java.lang.Short](rows, ordinal, nulls, 
0.toShort)
+TColumn.i16Val(new TI16Column(values, nulls))
+
+  case IntegerType =>
+val values = getOrSetAsNull[java.lang.Integer](rows, ordinal, nulls, 0)
+TColumn.i32Val(new TI32Column(values, nulls))
+
+  case LongType =>
+val values = getOrSetAsNull[java.lang.Long](rows, ordinal, nulls, 0L)
+TColumn.i64Val(new TI64Column(values, nulls))
+
+  case FloatType =>
+val values = getOrSetAsNull[java.lang.Float](rows, ordinal, nulls, 
0.toFloat)
+  .asScala.map(n => java.lang.Double.valueOf(n.toString)).asJava
+TColumn.doubleVal(new TDoubleColumn(values, nulls))
+
+  case DoubleType =>
+val values = getOrSetAsNull[java.lang.Double](rows, ordinal, nulls, 
0.toDouble)
+TColumn.doubleVal(new TDoubleColumn(values, nulls))
+
+  case StringType =>
+val values = getOrSetAsNull[java.lang.String](rows, ordinal, nulls, "")
+TColumn.stringVal(new TStringColumn(values, nulls))
+
+  case BinaryType =>
+val values =

[GitHub] [spark] yaooqinn commented on a diff in pull request #36373: [SPARK-39041][SQL] Mapping Spark Query ResultSet/Schema to TRowSet/TTableSchema directly

2022-04-27 Thread GitBox 


yaooqinn commented on code in PR #36373:
URL: https://github.com/apache/spark/pull/36373#discussion_r859583552


##
sql/hive-thriftserver/src/main/java/org/apache/hive/service/cli/CLIServiceClient.java:
##
@@ -35,7 +36,7 @@ public SessionHandle openSession(String username, String 
password)
   }
 
   @Override
-  public RowSet fetchResults(OperationHandle opHandle) throws HiveSQLException 
{
+  public TRowSet fetchResults(OperationHandle opHandle) throws 
HiveSQLException {

Review Comment:
   CLIServiceClient and its child is used in our test only, changes here will 
not have impacts on real-world clients



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[GitHub] [spark] yaooqinn commented on a diff in pull request #36373: [SPARK-39041][SQL] Mapping Spark Query ResultSet/Schema to TRowSet/TTableSchema directly

2022-04-27 Thread GitBox 


yaooqinn commented on code in PR #36373:
URL: https://github.com/apache/spark/pull/36373#discussion_r859578656


##
sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala:
##
@@ -373,17 +306,69 @@ private[hive] class SparkExecuteStatementOperation(
 }
 
 object SparkExecuteStatementOperation {
-  def getTableSchema(structType: StructType): TableSchema = {
-val schema = structType.map { field =>
-  val attrTypeString = field.dataType match {
-case CalendarIntervalType => StringType.catalogString
-case _: YearMonthIntervalType => "interval_year_month"
-case _: DayTimeIntervalType => "interval_day_time"
-case _: TimestampNTZType => "timestamp"
-case other => other.catalogString
-  }
-  new FieldSchema(field.name, attrTypeString, 
field.getComment.getOrElse(""))
+
+  def toTTypeId(typ: DataType): TTypeId = typ match {
+case NullType => TTypeId.NULL_TYPE
+case BooleanType => TTypeId.BOOLEAN_TYPE
+case ByteType => TTypeId.TINYINT_TYPE
+case ShortType => TTypeId.SMALLINT_TYPE
+case IntegerType => TTypeId.INT_TYPE
+case LongType => TTypeId.BIGINT_TYPE
+case FloatType => TTypeId.FLOAT_TYPE
+case DoubleType => TTypeId.DOUBLE_TYPE
+case StringType => TTypeId.STRING_TYPE
+case _: DecimalType => TTypeId.DECIMAL_TYPE
+case DateType => TTypeId.DATE_TYPE
+// TODO: Shall use TIMESTAMPLOCALTZ_TYPE, keep AS-IS now for

Review Comment:
   TimestampType keeps being converted to TTypeId.TIMESTAMP_TYPE in this PR



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[GitHub] [spark] yaooqinn commented on a diff in pull request #36373: [SPARK-39041][SQL] Mapping Spark Query ResultSet/Schema to TRowSet/TTableSchema directly

2022-04-27 Thread GitBox 


yaooqinn commented on code in PR #36373:
URL: https://github.com/apache/spark/pull/36373#discussion_r859577346


##
sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala:
##
@@ -373,17 +306,69 @@ private[hive] class SparkExecuteStatementOperation(
 }
 
 object SparkExecuteStatementOperation {
-  def getTableSchema(structType: StructType): TableSchema = {
-val schema = structType.map { field =>
-  val attrTypeString = field.dataType match {
-case CalendarIntervalType => StringType.catalogString
-case _: YearMonthIntervalType => "interval_year_month"
-case _: DayTimeIntervalType => "interval_day_time"
-case _: TimestampNTZType => "timestamp"
-case other => other.catalogString
-  }
-  new FieldSchema(field.name, attrTypeString, 
field.getComment.getOrElse(""))
+
+  def toTTypeId(typ: DataType): TTypeId = typ match {
+case NullType => TTypeId.NULL_TYPE
+case BooleanType => TTypeId.BOOLEAN_TYPE
+case ByteType => TTypeId.TINYINT_TYPE
+case ShortType => TTypeId.SMALLINT_TYPE
+case IntegerType => TTypeId.INT_TYPE
+case LongType => TTypeId.BIGINT_TYPE
+case FloatType => TTypeId.FLOAT_TYPE
+case DoubleType => TTypeId.DOUBLE_TYPE
+case StringType => TTypeId.STRING_TYPE
+case _: DecimalType => TTypeId.DECIMAL_TYPE
+case DateType => TTypeId.DATE_TYPE
+// TODO: Shall use TIMESTAMPLOCALTZ_TYPE, keep AS-IS now for
+// unnecessary behavior change
+case TimestampType => TTypeId.TIMESTAMP_TYPE
+case TimestampNTZType => TTypeId.TIMESTAMP_TYPE
+case BinaryType => TTypeId.BINARY_TYPE
+case CalendarIntervalType => TTypeId.STRING_TYPE
+case _: DayTimeIntervalType => TTypeId.INTERVAL_DAY_TIME_TYPE
+case _: YearMonthIntervalType => TTypeId.INTERVAL_YEAR_MONTH_TYPE
+case _: ArrayType => TTypeId.ARRAY_TYPE
+case _: MapType => TTypeId.MAP_TYPE
+case _: StructType => TTypeId.STRUCT_TYPE
+case other =>
+  throw new IllegalArgumentException(s"Unrecognized type name: 
${other.catalogString}")
+  }
+
+  private def toTTypeQualifiers(typ: DataType): TTypeQualifiers = {
+val ret = new TTypeQualifiers()
+val qualifiers = typ match {
+  case d: DecimalType =>
+Map(
+  TCLIServiceConstants.PRECISION -> 
TTypeQualifierValue.i32Value(d.precision),
+  TCLIServiceConstants.SCALE -> 
TTypeQualifierValue.i32Value(d.scale)).asJava
+  case _ => Collections.emptyMap[String, TTypeQualifierValue]()

Review Comment:
   if char/varchar can be seen via resultset metadata, we shall also provide 
the char length here for client to take advantage of it



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