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Description
EXPLAIN CODEGEN and BenchmarkQueryTest don't show the corresponding code for subqueries.
The following example is about EXPLAIN CODEGEN.
spark.conf.set("spark.sql.adaptive.enabled", "false") val df = spark.range(1, 100) df.createTempView("df") spark.sql("SELECT (SELECT min(id) AS v FROM df)").explain("CODEGEN") scala> spark.sql("SELECT (SELECT min(id) AS v FROM df)").explain("CODEGEN") Found 1 WholeStageCodegen subtrees. == Subtree 1 / 1 (maxMethodCodeSize:55; maxConstantPoolSize:97(0.15% used); numInnerClasses:0) == *(1) Project [Subquery scalar-subquery#3, [id=#24] AS scalarsubquery()#5L] : +- Subquery scalar-subquery#3, [id=#24] : +- *(2) HashAggregate(keys=[], functions=[min(id#0L)], output=[v#2L]) : +- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#20] : +- *(1) HashAggregate(keys=[], functions=[partial_min(id#0L)], output=[min#8L]) : +- *(1) Range (1, 100, step=1, splits=12) +- *(1) Scan OneRowRelation[] 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 scala.collection.Iterator rdd_input_0; /* 010 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[] project_mutableStateArray_0 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[1]; /* 011 */ /* 012 */ public GeneratedIteratorForCodegenStage1(Object[] references) { /* 013 */ this.references = references; /* 014 */ } /* 015 */ /* 016 */ public void init(int index, scala.collection.Iterator[] inputs) { /* 017 */ partitionIndex = index; /* 018 */ this.inputs = inputs; /* 019 */ rdd_input_0 = inputs[0]; /* 020 */ project_mutableStateArray_0[0] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0); /* 021 */ /* 022 */ } /* 023 */ /* 024 */ private void project_doConsume_0() throws java.io.IOException { /* 025 */ // common sub-expressions /* 026 */ /* 027 */ project_mutableStateArray_0[0].reset(); /* 028 */ /* 029 */ if (false) { /* 030 */ project_mutableStateArray_0[0].setNullAt(0); /* 031 */ } else { /* 032 */ project_mutableStateArray_0[0].write(0, 1L); /* 033 */ } /* 034 */ append((project_mutableStateArray_0[0].getRow())); /* 035 */ /* 036 */ } /* 037 */ /* 038 */ protected void processNext() throws java.io.IOException { /* 039 */ while ( rdd_input_0.hasNext()) { /* 040 */ InternalRow rdd_row_0 = (InternalRow) rdd_input_0.next(); /* 041 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(1); /* 042 */ project_doConsume_0(); /* 043 */ if (shouldStop()) return; /* 044 */ } /* 045 */ } /* 046 */ /* 047 */ }