[SPARK-20960][SQL] make ColumnVector public

sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedParquetRecordReader.java

@@ -31,10 +31,10 @@
 import org.apache.spark.memory.MemoryMode;
 import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.sql.execution.vectorized.ColumnVectorUtils;
-import org.apache.spark.sql.execution.vectorized.ColumnarBatch;
 import org.apache.spark.sql.execution.vectorized.WritableColumnVector;
 import org.apache.spark.sql.execution.vectorized.OffHeapColumnVector;
 import org.apache.spark.sql.execution.vectorized.OnHeapColumnVector;
+import org.apache.spark.sql.vectorized.ColumnarBatch;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 
@@ -248,7 +248,10 @@ public void enableReturningBatches() {
    * Advances to the next batch of rows. Returns false if there are no more.
    */
   public boolean nextBatch() throws IOException {
-    columnarBatch.reset();
+    for (WritableColumnVector vector : columnVectors) {
+      vector.reset();
+    }
+    columnarBatch.setNumRows(0);
     if (rowsReturned >= totalRowCount) return false;
     checkEndOfRowGroup();
 

sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnVectorUtils.java

@@ -28,6 +28,8 @@
 import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.sql.catalyst.util.DateTimeUtils;
 import org.apache.spark.sql.types.*;
+import org.apache.spark.sql.vectorized.ColumnarArray;
+import org.apache.spark.sql.vectorized.ColumnarBatch;
 import org.apache.spark.unsafe.types.CalendarInterval;
 import org.apache.spark.unsafe.types.UTF8String;
 

sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/MutableColumnarRow.java

@@ -23,6 +23,10 @@
 import org.apache.spark.sql.catalyst.expressions.GenericInternalRow;
 import org.apache.spark.sql.catalyst.util.MapData;
 import org.apache.spark.sql.types.*;
+import org.apache.spark.sql.vectorized.ColumnarArray;
+import org.apache.spark.sql.vectorized.ColumnarBatch;
+import org.apache.spark.sql.vectorized.ColumnarRow;
+import org.apache.spark.sql.vectorized.ColumnVector;
 import org.apache.spark.unsafe.types.CalendarInterval;
 import org.apache.spark.unsafe.types.UTF8String;
 

sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/WritableColumnVector.java

@@ -23,6 +23,7 @@
 
 import org.apache.spark.sql.internal.SQLConf;
 import org.apache.spark.sql.types.*;
+import org.apache.spark.sql.vectorized.ColumnVector;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.types.UTF8String;
 
@@ -585,11 +586,11 @@ public final int appendArray(int length) {
   public final int appendStruct(boolean isNull) {
     if (isNull) {
       appendNull();
-      for (ColumnVector c: childColumns) {
+      for (WritableColumnVector c: childColumns) {
         if (c.type instanceof StructType) {
-          ((WritableColumnVector) c).appendStruct(true);
+          c.appendStruct(true);
         } else {
-          ((WritableColumnVector) c).appendNull();
+          c.appendNull();
         }
       }
     } else {

sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ArrowColumnVector.java → sql/core/src/main/java/org/apache/spark/sql/vectorized/ArrowColumnVector.java

@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.execution.vectorized;
+package org.apache.spark.sql.vectorized;
 
 import org.apache.arrow.vector.*;
 import org.apache.arrow.vector.complex.*;
@@ -34,11 +34,7 @@ public final class ArrowColumnVector extends ColumnVector {
   private ArrowColumnVector[] childColumns;
 
   private void ensureAccessible(int index) {
-    int valueCount = accessor.getValueCount();
-    if (index < 0 || index >= valueCount) {
-      throw new IndexOutOfBoundsException(
-        String.format("index: %d, valueCount: %d", index, valueCount));
-    }
+    ensureAccessible(index, 1);
   }
 
   private void ensureAccessible(int index, int count) {
@@ -64,20 +60,12 @@ public void close() {
     accessor.close();
   }
 
-  //
-  // APIs dealing with nulls
-  //
-
   @Override
   public boolean isNullAt(int rowId) {
     ensureAccessible(rowId);
     return accessor.isNullAt(rowId);
   }
 
-  //
-  // APIs dealing with Booleans
-  //
-
   @Override
   public boolean getBoolean(int rowId) {
     ensureAccessible(rowId);
@@ -94,10 +82,6 @@ public boolean[] getBooleans(int rowId, int count) {
     return array;
   }
 
-  //
-  // APIs dealing with Bytes
-  //
-
   @Override
   public byte getByte(int rowId) {
     ensureAccessible(rowId);
@@ -114,10 +98,6 @@ public byte[] getBytes(int rowId, int count) {
     return array;
   }
 
-  //
-  // APIs dealing with Shorts
-  //
-
   @Override
   public short getShort(int rowId) {
     ensureAccessible(rowId);
@@ -134,10 +114,6 @@ public short[] getShorts(int rowId, int count) {
     return array;
   }
 
-  //
-  // APIs dealing with Ints
-  //
-
   @Override
   public int getInt(int rowId) {
     ensureAccessible(rowId);
@@ -154,10 +130,6 @@ public int[] getInts(int rowId, int count) {
     return array;
   }
 
-  //
-  // APIs dealing with Longs
-  //
-
   @Override
   public long getLong(int rowId) {
     ensureAccessible(rowId);
@@ -174,10 +146,6 @@ public long[] getLongs(int rowId, int count) {
     return array;
   }
 
-  //
-  // APIs dealing with floats
-  //
-
   @Override
   public float getFloat(int rowId) {
     ensureAccessible(rowId);
@@ -194,10 +162,6 @@ public float[] getFloats(int rowId, int count) {
     return array;
   }
 
-  //
-  // APIs dealing with doubles
-  //
-
   @Override
   public double getDouble(int rowId) {
     ensureAccessible(rowId);
@@ -214,10 +178,6 @@ public double[] getDoubles(int rowId, int count) {
     return array;
   }
 
-  //
-  // APIs dealing with Arrays
-  //
-
   @Override
   public int getArrayLength(int rowId) {
     ensureAccessible(rowId);
@@ -230,45 +190,27 @@ public int getArrayOffset(int rowId) {
     return accessor.getArrayOffset(rowId);
   }
 
-  //
-  // APIs dealing with Decimals
-  //
-
   @Override
   public Decimal getDecimal(int rowId, int precision, int scale) {
     ensureAccessible(rowId);
     return accessor.getDecimal(rowId, precision, scale);
   }
 
-  //
-  // APIs dealing with UTF8Strings
-  //
-
   @Override
   public UTF8String getUTF8String(int rowId) {
     ensureAccessible(rowId);
     return accessor.getUTF8String(rowId);
   }
 
-  //
-  // APIs dealing with Binaries
-  //
-
   @Override
   public byte[] getBinary(int rowId) {
     ensureAccessible(rowId);
     return accessor.getBinary(rowId);
   }
 
-  /**
-   * Returns the data for the underlying array.
-   */
   @Override
   public ArrowColumnVector arrayData() { return childColumns[0]; }
 
-  /**
-   * Returns the ordinal's child data column.
-   */
   @Override
   public ArrowColumnVector getChildColumn(int ordinal) { return childColumns[ordinal]; }
 

sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnVector.java → sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnVector.java

@@ -14,32 +14,39 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-package org.apache.spark.sql.execution.vectorized;
+package org.apache.spark.sql.vectorized;
 
 import org.apache.spark.sql.catalyst.util.MapData;
 import org.apache.spark.sql.types.DataType;
 import org.apache.spark.sql.types.Decimal;
 import org.apache.spark.unsafe.types.UTF8String;
 
 /**
- * This class represents in-memory values of a column and provides the main APIs to access the data.
- * It supports all the types and contains get APIs as well as their batched versions. The batched
- * versions are considered to be faster and preferable whenever possible.
+ * An interface representing in-memory columnar data in Spark. This interface defines the main APIs
+ * to access the data, as well as their batched versions. The batched versions are considered to be
+ * faster and preferable whenever possible.
  *
- * To handle nested schemas, ColumnVector has two types: Arrays and Structs. In both cases these
- * columns have child columns. All of the data are stored in the child columns and the parent column
- * only contains nullability. In the case of Arrays, the lengths and offsets are saved in the child
- * column and are encoded identically to INTs.
+ * Most of the APIs take the rowId as a parameter. This is the batch local 0-based row id for values
+ * in this ColumnVector.
  *
- * Maps are just a special case of a two field struct.
+ * ColumnVector supports all the data types including nested types. To handle nested types,
+ * ColumnVector can have children and is a tree structure. For struct type, it stores the actual
+ * data of each field in the corresponding child ColumnVector, and only stores null information in
+ * the parent ColumnVector. For array type, it stores the actual array elements in the child
+ * ColumnVector, and stores null information, array offsets and lengths in the parent ColumnVector.
  *
- * Most of the APIs take the rowId as a parameter. This is the batch local 0-based row id for values
- * in the current batch.
+ * ColumnVector is expected to be reused during the entire data loading process, to avoid allocating
+ * memory again and again.
+ *
+ * ColumnVector is meant to maximize CPU efficiency but not to minimize storage footprint.
+ * Implementations should prefer computing efficiency over storage efficiency when design the
+ * format. Since it is expected to reuse the ColumnVector instance while loading data, the storage
+ * footprint is negligible.
  */
 public abstract class ColumnVector implements AutoCloseable {
 
   /**
-   * Returns the data type of this column.
+   * Returns the data type of this column vector.
    */
   public final DataType dataType() { return type; }
 

sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnarArray.java → sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnarArray.java

@@ -14,7 +14,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-package org.apache.spark.sql.execution.vectorized;
+package org.apache.spark.sql.vectorized;
 
 import org.apache.spark.sql.catalyst.util.ArrayData;
 import org.apache.spark.sql.catalyst.util.MapData;
@@ -23,8 +23,7 @@
 import org.apache.spark.unsafe.types.UTF8String;
 
 /**
- * Array abstraction in {@link ColumnVector}. The instance of this class is intended
- * to be reused, callers should copy the data out if it needs to be stored.
+ * Array abstraction in {@link ColumnVector}.
  */
 public final class ColumnarArray extends ArrayData {
   // The data for this array. This array contains elements from
@@ -33,7 +32,7 @@ public final class ColumnarArray extends ArrayData {
   private final int offset;
   private final int length;
 
-  ColumnarArray(ColumnVector data, int offset, int length) {
+  public ColumnarArray(ColumnVector data, int offset, int length) {
     this.data = data;
     this.offset = offset;
     this.length = length;

sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnarBatch.java → sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnarBatch.java

@@ -14,26 +14,18 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-package org.apache.spark.sql.execution.vectorized;
+package org.apache.spark.sql.vectorized;
 
 import java.util.*;
 
 import org.apache.spark.sql.catalyst.InternalRow;
+import org.apache.spark.sql.execution.vectorized.MutableColumnarRow;
 import org.apache.spark.sql.types.StructType;
 
 /**
- * This class is the in memory representation of rows as they are streamed through operators. It
- * is designed to maximize CPU efficiency and not storage footprint. Since it is expected that
- * each operator allocates one of these objects, the storage footprint on the task is negligible.
- *
- * The layout is a columnar with values encoded in their native format. Each RowBatch contains
- * a horizontal partitioning of the data, split into columns.
- *
- * The ColumnarBatch supports either on heap or offheap modes with (mostly) the identical API.
- *
- * TODO:
- *  - There are many TODOs for the existing APIs. They should throw a not implemented exception.
- *  - Compaction: The batch and columns should be able to compact based on a selection vector.
+ * This class wraps multiple ColumnVectors as a row-wise table. It provides a row view of this
+ * batch so that Spark can access the data row by row. Instance of it is meant to be reused during
+ * the entire data loading process.
  */
 public final class ColumnarBatch {
   public static final int DEFAULT_BATCH_SIZE = 4 * 1024;
@@ -57,7 +49,7 @@ public void close() {
   }
 
   /**
-   * Returns an iterator over the rows in this batch. This skips rows that are filtered out.
+   * Returns an iterator over the rows in this batch.
    */
   public Iterator<InternalRow> rowIterator() {
     final int maxRows = numRows;
@@ -87,19 +79,7 @@ public void remove() {
   }
 
   /**
-   * Resets the batch for writing.
-   */
-  public void reset() {
-    for (int i = 0; i < numCols(); ++i) {
-      if (columns[i] instanceof WritableColumnVector) {
-        ((WritableColumnVector) columns[i]).reset();
-      }
-    }
-    this.numRows = 0;
-  }
-
-  /**
-   * Sets the number of rows that are valid.
+   * Sets the number of rows in this batch.
    */
   public void setNumRows(int numRows) {
     assert(numRows <= this.capacity);