viirya commented on a change in pull request #33695:
URL: https://github.com/apache/spark/pull/33695#discussion_r688857071
##########
File path:
sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/ParquetReadState.java
##########
@@ -131,9 +157,10 @@ long currentRangeEnd() {
* Advance the current offset and rowId to the new values.
*/
void advanceOffsetAndRowId(int newOffset, long newRowId) {
- valuesToReadInBatch -= (newOffset - offset);
+ rowsToReadInBatch -= (newOffset - levelOffset);
Review comment:
Hmm, for repeated values, is offset difference the same as row
difference? I think one row could contain many values (offsets), no?
##########
File path:
sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/ParquetColumn.java
##########
@@ -0,0 +1,239 @@
+/*
+ * 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.execution.datasources.parquet;
+
+import org.apache.spark.memory.MemoryMode;
+import org.apache.spark.sql.execution.vectorized.OffHeapColumnVector;
+import org.apache.spark.sql.execution.vectorized.OnHeapColumnVector;
+import org.apache.spark.sql.execution.vectorized.WritableColumnVector;
+import org.apache.spark.sql.types.ArrayType;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.MapType;
+import org.apache.spark.sql.types.StructType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * Contains necessary information representing a Parquet column, either of
primitive or nested type.
+ */
+final class ParquetColumn {
+ private final ParquetTypeInfo columnInfo;
+ private final List<ParquetColumn> children;
+ private final WritableColumnVector vector;
+
+ /**
+ * repetition & definition levels
+ * these are allocated only for leaf columns; for non-leaf columns, they
simply maintain
+ * references to that of the former.
+ */
+ private WritableColumnVector repetitionLevels;
+ private WritableColumnVector definitionLevels;
+
+ /** whether this column is primitive (i.e., leaf column) */
+ private final boolean isPrimitive;
+
+ /** reader for this column - only set if 'isPrimitive' is true */
+ private VectorizedColumnReader columnReader;
+
+ ParquetColumn(
+ ParquetTypeInfo columnInfo,
+ WritableColumnVector vector,
+ int capacity,
+ MemoryMode memoryMode) {
+ DataType sparkType = columnInfo.sparkType();
+ if (!sparkType.sameType(vector.dataType())) {
+ throw new IllegalArgumentException("Spark type: " +
columnInfo.sparkType() +
Review comment:
columnInfo.sparkType() -> sparkType?
##########
File path:
sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/ParquetColumn.java
##########
@@ -0,0 +1,239 @@
+/*
+ * 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.execution.datasources.parquet;
+
+import org.apache.spark.memory.MemoryMode;
+import org.apache.spark.sql.execution.vectorized.OffHeapColumnVector;
+import org.apache.spark.sql.execution.vectorized.OnHeapColumnVector;
+import org.apache.spark.sql.execution.vectorized.WritableColumnVector;
+import org.apache.spark.sql.types.ArrayType;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.MapType;
+import org.apache.spark.sql.types.StructType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * Contains necessary information representing a Parquet column, either of
primitive or nested type.
+ */
+final class ParquetColumn {
+ private final ParquetTypeInfo columnInfo;
+ private final List<ParquetColumn> children;
+ private final WritableColumnVector vector;
+
+ /**
+ * repetition & definition levels
+ * these are allocated only for leaf columns; for non-leaf columns, they
simply maintain
+ * references to that of the former.
+ */
+ private WritableColumnVector repetitionLevels;
+ private WritableColumnVector definitionLevels;
+
+ /** whether this column is primitive (i.e., leaf column) */
+ private final boolean isPrimitive;
+
+ /** reader for this column - only set if 'isPrimitive' is true */
+ private VectorizedColumnReader columnReader;
+
+ ParquetColumn(
+ ParquetTypeInfo columnInfo,
+ WritableColumnVector vector,
+ int capacity,
+ MemoryMode memoryMode) {
+ DataType sparkType = columnInfo.sparkType();
+ if (!sparkType.sameType(vector.dataType())) {
+ throw new IllegalArgumentException("Spark type: " +
columnInfo.sparkType() +
+ " doesn't match the type: " + vector.dataType() + " in column vector");
+ }
+ this.columnInfo = columnInfo;
+ this.vector = vector;
+ this.children = new ArrayList<>();
+ this.isPrimitive = columnInfo.isPrimitive();
+
+ if (isPrimitive) {
+ repetitionLevels = allocateLevelsVector(capacity, memoryMode);
+ definitionLevels = allocateLevelsVector(capacity, memoryMode);
+ } else {
+ ParquetGroupTypeInfo groupInfo = (ParquetGroupTypeInfo) columnInfo;
+ if (sparkType instanceof ArrayType) {
+ ParquetColumn childState = new
ParquetColumn(groupInfo.children().apply(0),
+ vector.getChild(0), capacity, memoryMode);
+ this.repetitionLevels = childState.repetitionLevels;
+ this.definitionLevels = childState.definitionLevels;
+ children.add(childState);
+ } else if (sparkType instanceof MapType) {
+ ParquetColumn childState = new
ParquetColumn(groupInfo.children().apply(0),
+ vector.getChild(0), capacity, memoryMode);
+ this.repetitionLevels = childState.repetitionLevels;
+ this.definitionLevels = childState.definitionLevels;
+ children.add(childState);
+ children.add(new ParquetColumn(groupInfo.children().apply(1),
vector.getChild(1),
+ capacity, memoryMode));
+ } else if (sparkType instanceof StructType) {
+ for (int i = 0; i < groupInfo.children().length(); i++) {
+ ParquetColumn childState = new
ParquetColumn(groupInfo.children().apply(i),
+ vector.getChild(i), capacity, memoryMode);
+ this.repetitionLevels = childState.repetitionLevels;
+ this.definitionLevels = childState.definitionLevels;
+ children.add(childState);
+ }
+ }
+ }
+ }
+
+ /**
+ * Get all the leaf columns in depth-first order.
+ */
+ List<ParquetColumn> getLeaves() {
+ List<ParquetColumn> result = new ArrayList<>();
+ getLeavesHelper(this, result);
+ return result;
+ }
+
+ /**
+ * Assemble this column and calculate collection offsets recursively.
+ * This is a no-op for primitive columns.
+ */
+ void assemble() {
+ DataType type = columnInfo.sparkType();
+ if (type instanceof ArrayType || type instanceof MapType) {
+ for (ParquetColumn child : children) {
+ child.assemble();
+ }
+ calculateCollectionOffsets();
+ } else if (type instanceof StructType) {
+ for (ParquetColumn child : children) {
+ child.assemble();
+ }
+ calculateStructOffsets();
+ }
+ }
+
+ ParquetTypeInfo getColumnInfo() {
+ return this.columnInfo;
+ }
+
+ WritableColumnVector getValueVector() {
+ return this.vector;
+ }
+
+ WritableColumnVector getRepetitionLevelVector() {
+ return this.repetitionLevels;
+ }
+
+ WritableColumnVector getDefinitionLevelVector() {
+ return this.definitionLevels;
+ }
+
+ VectorizedColumnReader getColumnReader() {
+ return this.columnReader;
+ }
+
+ void setColumnReader(VectorizedColumnReader reader) {
+ if (!isPrimitive) {
+ throw new IllegalStateException("can't set reader for non-primitive
column");
+ }
+ this.columnReader = reader;
+ }
+
+ private static void getLeavesHelper(ParquetColumn column,
List<ParquetColumn> coll) {
+ if (column.isPrimitive) {
+ coll.add(column);
+ } else {
+ for (ParquetColumn childCol : column.children) {
+ getLeavesHelper(childCol, coll);
+ }
+ }
+ }
+
+ private void calculateCollectionOffsets() {
+ int maxDefinitionLevel = columnInfo.definitionLevel();
+ int maxElementRepetitionLevel = columnInfo.repetitionLevel();
+
+ // `i` is the index over all leaf elements of this array, while `offset`
is the index over
+ // all top-level elements of this array.
+ for (int i = 0, rowId = 0, offset = 0; i <
definitionLevels.getElementsAppended();
+ i = getNextCollectionStart(maxElementRepetitionLevel, i), rowId++) {
+ vector.reserve(rowId + 1);
+ int definitionLevel = definitionLevels.getInt(i);
+ if (definitionLevel == maxDefinitionLevel - 1) {
+ // the collection is null
+ vector.putNull(rowId);
+ } else if (definitionLevel == maxDefinitionLevel) {
+ // collection is defined but empty
+ vector.putNotNull(rowId);
+ vector.putArray(rowId, offset, 0);
+ } else {
+ // collection is defined and non-empty: find out how many top element
there is till the
+ // start of the next array.
+ vector.putNotNull(rowId);
+ int length = getCollectionSize(maxElementRepetitionLevel, i + 1);
+ vector.putArray(rowId, offset, length);
+ offset += length;
+ }
+ }
+ }
+
+ private void calculateStructOffsets() {
+ int maxDefinitionLevel = columnInfo.definitionLevel();
+ vector.reserve(definitionLevels.getElementsAppended());
+ for (int i = 0, rowId = 0; i < definitionLevels.getElementsAppended();
i++, rowId++) {
Review comment:
BTW, isn't `rowId` as same as `i` in this loop?
##########
File path:
sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/ParquetReadState.java
##########
@@ -131,9 +157,10 @@ long currentRangeEnd() {
* Advance the current offset and rowId to the new values.
*/
void advanceOffsetAndRowId(int newOffset, long newRowId) {
- valuesToReadInBatch -= (newOffset - offset);
+ rowsToReadInBatch -= (newOffset - levelOffset);
valuesToReadInPage -= (newRowId - rowId);
- offset = newOffset;
+ levelOffset = newOffset;
+ valueOffset = newOffset;
Review comment:
So we always advance same range to level and value offsets? But I think
they are not always the same, right?
##########
File path:
sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/ParquetColumn.java
##########
@@ -0,0 +1,239 @@
+/*
+ * 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.execution.datasources.parquet;
+
+import org.apache.spark.memory.MemoryMode;
+import org.apache.spark.sql.execution.vectorized.OffHeapColumnVector;
+import org.apache.spark.sql.execution.vectorized.OnHeapColumnVector;
+import org.apache.spark.sql.execution.vectorized.WritableColumnVector;
+import org.apache.spark.sql.types.ArrayType;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.MapType;
+import org.apache.spark.sql.types.StructType;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * Contains necessary information representing a Parquet column, either of
primitive or nested type.
+ */
+final class ParquetColumn {
+ private final ParquetTypeInfo columnInfo;
+ private final List<ParquetColumn> children;
+ private final WritableColumnVector vector;
+
+ /**
+ * repetition & definition levels
+ * these are allocated only for leaf columns; for non-leaf columns, they
simply maintain
+ * references to that of the former.
+ */
+ private WritableColumnVector repetitionLevels;
+ private WritableColumnVector definitionLevels;
+
+ /** whether this column is primitive (i.e., leaf column) */
+ private final boolean isPrimitive;
+
+ /** reader for this column - only set if 'isPrimitive' is true */
+ private VectorizedColumnReader columnReader;
+
+ ParquetColumn(
+ ParquetTypeInfo columnInfo,
+ WritableColumnVector vector,
+ int capacity,
+ MemoryMode memoryMode) {
+ DataType sparkType = columnInfo.sparkType();
+ if (!sparkType.sameType(vector.dataType())) {
+ throw new IllegalArgumentException("Spark type: " +
columnInfo.sparkType() +
+ " doesn't match the type: " + vector.dataType() + " in column vector");
+ }
+ this.columnInfo = columnInfo;
+ this.vector = vector;
+ this.children = new ArrayList<>();
+ this.isPrimitive = columnInfo.isPrimitive();
+
+ if (isPrimitive) {
+ repetitionLevels = allocateLevelsVector(capacity, memoryMode);
+ definitionLevels = allocateLevelsVector(capacity, memoryMode);
+ } else {
+ ParquetGroupTypeInfo groupInfo = (ParquetGroupTypeInfo) columnInfo;
+ if (sparkType instanceof ArrayType) {
+ ParquetColumn childState = new
ParquetColumn(groupInfo.children().apply(0),
+ vector.getChild(0), capacity, memoryMode);
+ this.repetitionLevels = childState.repetitionLevels;
+ this.definitionLevels = childState.definitionLevels;
+ children.add(childState);
+ } else if (sparkType instanceof MapType) {
+ ParquetColumn childState = new
ParquetColumn(groupInfo.children().apply(0),
+ vector.getChild(0), capacity, memoryMode);
+ this.repetitionLevels = childState.repetitionLevels;
+ this.definitionLevels = childState.definitionLevels;
+ children.add(childState);
+ children.add(new ParquetColumn(groupInfo.children().apply(1),
vector.getChild(1),
+ capacity, memoryMode));
+ } else if (sparkType instanceof StructType) {
+ for (int i = 0; i < groupInfo.children().length(); i++) {
+ ParquetColumn childState = new
ParquetColumn(groupInfo.children().apply(i),
+ vector.getChild(i), capacity, memoryMode);
+ this.repetitionLevels = childState.repetitionLevels;
+ this.definitionLevels = childState.definitionLevels;
+ children.add(childState);
+ }
+ }
+ }
+ }
+
+ /**
+ * Get all the leaf columns in depth-first order.
+ */
+ List<ParquetColumn> getLeaves() {
+ List<ParquetColumn> result = new ArrayList<>();
+ getLeavesHelper(this, result);
+ return result;
+ }
+
+ /**
+ * Assemble this column and calculate collection offsets recursively.
+ * This is a no-op for primitive columns.
+ */
+ void assemble() {
+ DataType type = columnInfo.sparkType();
+ if (type instanceof ArrayType || type instanceof MapType) {
+ for (ParquetColumn child : children) {
+ child.assemble();
+ }
+ calculateCollectionOffsets();
+ } else if (type instanceof StructType) {
+ for (ParquetColumn child : children) {
+ child.assemble();
+ }
+ calculateStructOffsets();
+ }
+ }
+
+ ParquetTypeInfo getColumnInfo() {
+ return this.columnInfo;
+ }
+
+ WritableColumnVector getValueVector() {
+ return this.vector;
+ }
+
+ WritableColumnVector getRepetitionLevelVector() {
+ return this.repetitionLevels;
+ }
+
+ WritableColumnVector getDefinitionLevelVector() {
+ return this.definitionLevels;
+ }
+
+ VectorizedColumnReader getColumnReader() {
+ return this.columnReader;
+ }
+
+ void setColumnReader(VectorizedColumnReader reader) {
+ if (!isPrimitive) {
+ throw new IllegalStateException("can't set reader for non-primitive
column");
+ }
+ this.columnReader = reader;
+ }
+
+ private static void getLeavesHelper(ParquetColumn column,
List<ParquetColumn> coll) {
+ if (column.isPrimitive) {
+ coll.add(column);
+ } else {
+ for (ParquetColumn childCol : column.children) {
+ getLeavesHelper(childCol, coll);
+ }
+ }
+ }
+
+ private void calculateCollectionOffsets() {
+ int maxDefinitionLevel = columnInfo.definitionLevel();
+ int maxElementRepetitionLevel = columnInfo.repetitionLevel();
+
+ // `i` is the index over all leaf elements of this array, while `offset`
is the index over
+ // all top-level elements of this array.
+ for (int i = 0, rowId = 0, offset = 0; i <
definitionLevels.getElementsAppended();
+ i = getNextCollectionStart(maxElementRepetitionLevel, i), rowId++) {
+ vector.reserve(rowId + 1);
+ int definitionLevel = definitionLevels.getInt(i);
+ if (definitionLevel == maxDefinitionLevel - 1) {
+ // the collection is null
+ vector.putNull(rowId);
+ } else if (definitionLevel == maxDefinitionLevel) {
+ // collection is defined but empty
+ vector.putNotNull(rowId);
+ vector.putArray(rowId, offset, 0);
+ } else {
+ // collection is defined and non-empty: find out how many top element
there is till the
+ // start of the next array.
+ vector.putNotNull(rowId);
+ int length = getCollectionSize(maxElementRepetitionLevel, i + 1);
+ vector.putArray(rowId, offset, length);
+ offset += length;
+ }
+ }
+ }
+
+ private void calculateStructOffsets() {
+ int maxDefinitionLevel = columnInfo.definitionLevel();
+ vector.reserve(definitionLevels.getElementsAppended());
+ for (int i = 0, rowId = 0; i < definitionLevels.getElementsAppended();
i++, rowId++) {
Review comment:
nit: definitionLevels.getElementsAppended() is called twice.
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