Github user kiszk commented on the pull request:
https://github.com/apache/spark/pull/11636#issuecomment-195323234
@nongli , here is another example.
Spark code with two columns
````
sqlContext.conf.setConfString(SQLConf.PARQUET_VECTORIZED_READER_ENABLED.key,
"true")
sqlContext.conf.setConfString(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key,
"true")
val values = 10
withTempPath { dir =>
withTempTable("t1", "tempTable") {
sqlContext.range(values).registerTempTable("t1")
sqlContext.sql("select id % 2 as p, cast(id as INT) as id from t1")
.write.partitionBy("p").parquet(dir.getCanonicalPath)
sqlContext.read.parquet(dir.getCanonicalPath).registerTempTable("tempTable")
sqlContext.sql("select sum(p), sum(id) from tempTable").collect
}
}
````
Code snippet generated by this PR
````
...
/* 073 */ private void rdd_processBatches() throws
java.io.IOException {
/* 074 */ while (true) {
/* 075 */ int numRows = rdd_batch.numRows();
/* 076 */ if (rdd_batchIdx == 0) rdd_metricValue.add(numRows);
/* 077 */
/* 078 */ while (!shouldStop() && rdd_batchIdx < numRows) {
/* 079 */
org.apache.spark.sql.execution.vectorized.ColumnVector rdd_col0 =
rdd_batch.column(0);org.apache.spark.sql.execution.vectorized.ColumnVector r
dd_col1 = rdd_batch.column(1);
/* 080 */ /*** CONSUME: TungstenAggregate(key=[],
functions=[(sum(cast(p#4 as
bigint)),mode=Partial,isDistinct=false),(sum(cast(id#3 as bigint)),mode=Pa
rtial,isDistinct=false)], output=[sum#13L,sum#14L]) */
/* 081 */ /* input[0, int] */
/* 082 */ boolean rdd_isNull = rdd_col0.getIsNull(rdd_batchIdx);
/* 083 */ int rdd_value = rdd_isNull ? -1 :
(rdd_col0.getInt(rdd_batchIdx));
/* 084 */ /* input[1, int] */
/* 085 */ boolean rdd_isNull1 =
rdd_col1.getIsNull(rdd_batchIdx);
/* 086 */ int rdd_value1 = rdd_isNull1 ? -1 :
(rdd_col1.getInt(rdd_batchIdx));
/* 087 */
/* 088 */ // do aggregate
...
````
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