Re: [PR] [spark] Eliminate the De/serialization process when writing to the ap… [paimon]

["Zouxxyy (via GitHub)" <gi...@apache.org>]

Zouxxyy commented on code in PR #5159:
URL: https://github.com/apache/paimon/pull/5159#discussion_r1987487366


##########
paimon-spark/paimon-spark-common/src/main/scala/org/apache/paimon/spark/SparkTableWrite.scala:
##########
@@ -67,8 +73,12 @@ class SparkTableWrite(writeBuilder: BatchWriteBuilder, rowType: RowType, rowKind
     ioManager.close()
   }
 
-  private def toPaimonRow(row: Row) =
-    new SparkRow(rowType, row, SparkRowUtils.getRowKind(row, rowKindColIdx))
+  private def toPaimonRow(row: InternalRow) =
+    new SparkInternalRowWrapper(

Review Comment:
   We can use like this to reduce the cost of initialization.
   ```
     SparkInternalRowWrapper wrap(row internalRow) {
       this.row = internalRow;
       return this;
     }
   ```



##########
paimon-spark/paimon-spark-common/src/main/java/org/apache/paimon/spark/SparkInternalRowWrapper.java:
##########
@@ -0,0 +1,361 @@
+/*
+ * 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.paimon.spark;
+
+import org.apache.paimon.data.BinaryString;
+import org.apache.paimon.data.Decimal;
+import org.apache.paimon.data.InternalArray;
+import org.apache.paimon.data.InternalMap;
+import org.apache.paimon.data.InternalRow;
+import org.apache.paimon.data.Timestamp;
+import org.apache.paimon.data.variant.Variant;
+import org.apache.paimon.spark.util.shim.TypeUtils$;
+import org.apache.paimon.types.RowKind;
+
+import org.apache.spark.sql.catalyst.util.ArrayData;
+import org.apache.spark.sql.catalyst.util.DateTimeUtils;
+import org.apache.spark.sql.catalyst.util.MapData;
+import org.apache.spark.sql.paimon.shims.SparkShimLoader;
+import org.apache.spark.sql.types.ArrayType;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.MapType;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.types.TimestampNTZType;
+import org.apache.spark.sql.types.TimestampType;
+
+import java.math.BigDecimal;
+
+/** Wrapper to fetch value from the spark internal row. */
+public class SparkInternalRowWrapper implements InternalRow {
+
+    private final org.apache.spark.sql.catalyst.InternalRow internalRow;
+    private final RowKind rowKind;
+    private final int length;
+    private final StructType structType;
+
+    public SparkInternalRowWrapper(

Review Comment:
   We can only keep these in `SparkInternalRowWrapper`
   ```
   private org.apache.spark.sql.catalyst.InternalRow internalRow;
   private final StructType structType; // or paimon rowType
   private final rowKindColIdx
   ```
   
   and get `FieldCount` or `RowKind` by static method



##########
paimon-spark/paimon-spark-3.2/src/main/scala/org/apache/paimon/spark/catalyst/Compatibility.scala:
##########
@@ -50,4 +52,8 @@ object Compatibility {
       ansiEnabled: Boolean = SQLConf.get.ansiEnabled): Cast = {
     Cast(child, dataType, timeZoneId, ansiEnabled)
   }
+
+  def callFunction(name: String, args: Seq[Column]): Column = {
+    call_udf(name, args: _*)

Review Comment:
   What is the difference between this and the `call_function` in Spark 3.5+. It seems that new Compatibility have been added for it.



##########
paimon-spark/paimon-spark-common/src/main/scala/org/apache/paimon/spark/commands/BucketExpression.scala:
##########
@@ -0,0 +1,95 @@
+/*
+ * 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.paimon.spark.commands
+
+import org.apache.paimon.data.serializer.InternalRowSerializer
+import org.apache.paimon.spark.SparkInternalRowWrapper
+import org.apache.paimon.spark.SparkTypeUtils.toPaimonType
+import org.apache.paimon.table.sink.KeyAndBucketExtractor.{bucket, bucketKeyHashCode}
+import org.apache.paimon.types.{RowKind, RowType}
+
+import org.apache.spark.sql.catalyst.{FunctionIdentifier, InternalRow => SparkInternalRow}
+import org.apache.spark.sql.catalyst.analysis.FunctionRegistry.FunctionBuilder
+import org.apache.spark.sql.catalyst.analysis.FunctionRegistryBase
+import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionInfo, Literal}
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
+import org.apache.spark.sql.types.{DataType, DataTypes, StructField, StructType}
+
+case class FixedBucketExpression(_children: Seq[Expression])

Review Comment:
   Let's add some comments to the args



##########
paimon-spark/paimon-spark-common/src/main/scala/org/apache/paimon/spark/commands/PaimonSparkWriter.scala:
##########
@@ -86,33 +91,43 @@ case class PaimonSparkWriter(table: FileStoreTable) {
     }
 
     def writeWithoutBucket(dataFrame: DataFrame): Dataset[Array[Byte]] = {
-      dataFrame.mapPartitions {
-        iter =>
-          {
-            val write = newWrite()
-            try {
-              iter.foreach(row => write.write(row))
-              write.finish()
-            } finally {
-              write.close()
+      val schema = dataFrame.schema
+      dataFrame.queryExecution.toRdd

Review Comment:
   Have you tested the performance difference with this modification for unaware bucket scenario



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