flyrain edited a comment on issue #3141:
URL: https://github.com/apache/iceberg/issues/3141#issuecomment-932565038
By digging a bit deeper, extending class ColumnarBatch is a dead-end. The
way we thought may work is to override method `ColumnarBatch::rowIterator`, so
that we can filter out the deleted rows in a customized iterator. It works for
code path without Whole-Stage code gen. But codegen takes a different path,
which read rows directly from column vectors of a `ColumnarBatch` object by
invoking the public method `ColumnarBatch::column`, check the line 35 in the
code generated following.
We can try to make codegen to use `ColumnarBatch::rowIterator`, but it's not
a good practice since Spark will lose the ability to handle them as a batch. It
will fall back to row-by-row model.
Attached the code generated.
```
Found 1 WholeStageCodegen subtrees.
== Subtree 1 / 1 (maxMethodCodeSize:213; maxConstantPoolSize:129(0.20%
used); numInnerClasses:0) ==
*(1) Filter (data#1 = a)
+- *(1) ColumnarToRow
+- BatchScan[id#0, data#1] default_iceberg.default.test [filters=data =
‘a’]
Generated code:
/* 001 */ public Object generate(Object[] references) {
/* 002 */ return new GeneratedIteratorForCodegenStage1(references);
/* 003 */ }
/* 004 */
/* 005 */ // codegenStageId=1
/* 006 */ final class GeneratedIteratorForCodegenStage1 extends
org.apache.spark.sql.execution.BufferedRowIterator {
/* 007 */ private Object[] references;
/* 008 */ private scala.collection.Iterator[] inputs;
/* 009 */ private int columnartorow_batchIdx_0;
/* 010 */ private
org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[]
columnartorow_mutableStateArray_3 = new
org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[2];
/* 011 */ private org.apache.spark.sql.vectorized.ColumnarBatch[]
columnartorow_mutableStateArray_1 = new
org.apache.spark.sql.vectorized.ColumnarBatch[1];
/* 012 */ private scala.collection.Iterator[]
columnartorow_mutableStateArray_0 = new scala.collection.Iterator[1];
/* 013 */ private org.apache.spark.sql.vectorized.ColumnVector[]
columnartorow_mutableStateArray_2 = new
org.apache.spark.sql.vectorized.ColumnVector[2];
/* 014 */
/* 015 */ public GeneratedIteratorForCodegenStage1(Object[] references) {
/* 016 */ this.references = references;
/* 017 */ }
/* 018 */
/* 019 */ public void init(int index, scala.collection.Iterator[] inputs) {
/* 020 */ partitionIndex = index;
/* 021 */ this.inputs = inputs;
/* 022 */ columnartorow_mutableStateArray_0[0] = inputs[0];
/* 023 */
/* 024 */ columnartorow_mutableStateArray_3[0] = new
org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(2, 32);
/* 025 */ columnartorow_mutableStateArray_3[1] = new
org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(2, 32);
/* 026 */
/* 027 */ }
/* 028 */
/* 029 */ private void columnartorow_nextBatch_0() throws
java.io.IOException {
/* 030 */ if (columnartorow_mutableStateArray_0[0].hasNext()) {
/* 031 */ columnartorow_mutableStateArray_1[0] =
(org.apache.spark.sql.vectorized.ColumnarBatch)columnartorow_mutableStateArray_0[0].next();
/* 032 */ ((org.apache.spark.sql.execution.metric.SQLMetric)
references[1] /* numInputBatches */).add(1);
/* 033 */ ((org.apache.spark.sql.execution.metric.SQLMetric)
references[0] /* numOutputRows
*/).add(columnartorow_mutableStateArray_1[0].numRows());
/* 034 */ columnartorow_batchIdx_0 = 0;
/* 035 */ columnartorow_mutableStateArray_2[0] =
(org.apache.spark.sql.vectorized.ColumnVector)
columnartorow_mutableStateArray_1[0].column(0);
/* 036 */ columnartorow_mutableStateArray_2[1] =
(org.apache.spark.sql.vectorized.ColumnVector)
columnartorow_mutableStateArray_1[0].column(1);
/* 037 */
/* 038 */ }
/* 039 */ }
/* 040 */
/* 041 */ protected void processNext() throws java.io.IOException {
/* 042 */ if (columnartorow_mutableStateArray_1[0] == null) {
/* 043 */ columnartorow_nextBatch_0();
/* 044 */ }
/* 045 */ while ( columnartorow_mutableStateArray_1[0] != null) {
/* 046 */ int columnartorow_numRows_0 =
columnartorow_mutableStateArray_1[0].numRows();
/* 047 */ int columnartorow_localEnd_0 = columnartorow_numRows_0 -
columnartorow_batchIdx_0;
/* 048 */ for (int columnartorow_localIdx_0 = 0;
columnartorow_localIdx_0 < columnartorow_localEnd_0;
columnartorow_localIdx_0++) {
/* 049 */ int columnartorow_rowIdx_0 = columnartorow_batchIdx_0 +
columnartorow_localIdx_0;
/* 050 */ do {
/* 051 */ UTF8String columnartorow_value_1 =
columnartorow_mutableStateArray_2[1].getUTF8String(columnartorow_rowIdx_0);
/* 052 */
/* 053 */ boolean filter_value_0 = false;
/* 054 */ filter_value_0 =
columnartorow_value_1.equals(((UTF8String) references[3] /* literal */));
/* 055 */ if (!filter_value_0) continue;
/* 056 */
/* 057 */ ((org.apache.spark.sql.execution.metric.SQLMetric)
references[2] /* numOutputRows */).add(1);
/* 058 */
/* 059 */ int columnartorow_value_0 =
columnartorow_mutableStateArray_2[0].getInt(columnartorow_rowIdx_0);
/* 060 */ columnartorow_mutableStateArray_3[1].reset();
/* 061 */
/* 062 */ columnartorow_mutableStateArray_3[1].write(0,
columnartorow_value_0);
/* 063 */
/* 064 */ columnartorow_mutableStateArray_3[1].write(1,
columnartorow_value_1);
/* 065 */ append((columnartorow_mutableStateArray_3[1].getRow()));
/* 066 */
/* 067 */ } while(false);
/* 068 */ if (shouldStop()) { columnartorow_batchIdx_0 =
columnartorow_rowIdx_0 + 1; return; }
/* 069 */ }
/* 070 */ columnartorow_batchIdx_0 = columnartorow_numRows_0;
/* 071 */ columnartorow_mutableStateArray_1[0] = null;
/* 072 */ columnartorow_nextBatch_0();
/* 073 */ }
/* 074 */ }
/* 075 */
/* 076 */ }
```
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