Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/18659#discussion_r130209454
--- Diff:
sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ColumnarBatch.java
---
@@ -65,15 +65,44 @@
final Row row;
public static ColumnarBatch allocate(StructType schema, MemoryMode
memMode) {
- return new ColumnarBatch(schema, DEFAULT_BATCH_SIZE, memMode);
+ ColumnVector[] columns = allocateVectors(schema, DEFAULT_BATCH_SIZE,
memMode);
+ return create(schema, columns);
}
public static ColumnarBatch allocate(StructType type) {
- return new ColumnarBatch(type, DEFAULT_BATCH_SIZE,
DEFAULT_MEMORY_MODE);
+ ColumnVector[] columns = allocateVectors(type, DEFAULT_BATCH_SIZE,
DEFAULT_MEMORY_MODE);
+ return create(type, columns);
}
public static ColumnarBatch allocate(StructType schema, MemoryMode
memMode, int maxRows) {
- return new ColumnarBatch(schema, maxRows, memMode);
+ ColumnVector[] columns = allocateVectors(schema, maxRows, memMode);
+ return create(schema, columns);
+ }
+
+ private static ColumnVector[] allocateVectors(StructType schema, int
maxRows, MemoryMode memMode) {
+ ColumnVector[] columns = new ColumnVector[schema.size()];
+ for (int i = 0; i < schema.fields().length; ++i) {
+ StructField field = schema.fields()[i];
+ columns[i] = ColumnVector.allocate(maxRows, field.dataType(),
memMode);
+ }
+ return columns;
+ }
+
+ public static ColumnarBatch createReadOnly(
--- End diff --
@ueshin I made some changes here to allow for use with
`ArrowColumnVectors`. I was thinking of putting these in a separate JIRA
because it can be used regardless of what is done with vectorized UDFs. What
do you think?
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