[SPARK-34796][SQL][3.1] Initialize counter variable for LIMIT code-gen in doProduce()

This PR is to fix the LIMIT code-gen bug in https://issues.apache.org/jira/browse/SPARK-34796, where the counter variable from `BaseLimitExec` is not initialized but used in code-gen. This is because the limit counter variable will be used in upstream operators (LIMIT's child plan, e.g. `ColumnarToRowExec` operator for early termination), but in the same stage, there can be some operators doing the shortcut and not calling `BaseLimitExec`'s `doConsume()`, e.g. [HashJoin.codegenInner](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala#L402). So if we have query that `LocalLimit - BroadcastHashJoin - FileScan` in the same stage, the whole stage code-gen compilation will be failed.

Here is an example:

```
  test("failed limit query") {
    withTable("left_table", "empty_right_table", "output_table") {
      spark.range(5).toDF("k").write.saveAsTable("left_table")
      spark.range(0).toDF("k").write.saveAsTable("empty_right_table")

      withSQLConf(SQLConf.ADAPTIVE_EXECUTION_ENABLED.key -> "false") {
        spark.sql("CREATE TABLE output_table (k INT) USING parquet")
        spark.sql(
          s"""
             |INSERT INTO TABLE output_table
             |SELECT t1.k FROM left_table t1
             |JOIN empty_right_table t2
             |ON t1.k = t2.k
             |LIMIT 3
             |""".stripMargin)
      }
    }
  }
```

Query plan:

```
Execute InsertIntoHadoopFsRelationCommand file:/Users/chengsu/spark/sql/core/spark-warehouse/org.apache.spark.sql.SQLQuerySuite/output_table, false, Parquet, Map(path -> file:/Users/chengsu/spark/sql/core/spark-warehouse/org.apache.spark.sql.SQLQuerySuite/output_table), Append, CatalogTable(
Database: default
Table: output_table
Created Time: Thu Mar 18 21:46:26 PDT 2021
Last Access: UNKNOWN
Created By: Spark 3.2.0-SNAPSHOT
Type: MANAGED
Provider: parquet
Location: file:/Users/chengsu/spark/sql/core/spark-warehouse/org.apache.spark.sql.SQLQuerySuite/output_table
Schema: root
 |-- k: integer (nullable = true)
), org.apache.spark.sql.execution.datasources.InMemoryFileIndexb25d08b, [k]
+- *(3) Project [ansi_cast(k#228L as int) AS k#231]
   +- *(3) GlobalLimit 3
      +- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=apache#179]
         +- *(2) LocalLimit 3
            +- *(2) Project [k#228L]
               +- *(2) BroadcastHashJoin [k#228L], [k#229L], Inner, BuildRight, false
                  :- *(2) Filter isnotnull(k#228L)
                  :  +- *(2) ColumnarToRow
                  :     +- FileScan parquet default.left_table[k#228L] Batched: true, DataFilters: [isnotnull(k#228L)], Format: Parquet, Location: InMemoryFileIndex(1 paths)[file:/Users/chengsu/spark/sql/core/spark-warehouse/org.apache.spark.sq..., PartitionFilters: [], PushedFilters: [IsNotNull(k)], ReadSchema: struct<k:bigint>
                  +- BroadcastExchange HashedRelationBroadcastMode(List(input[0, bigint, false]),false), [id=apache#173]
                     +- *(1) Filter isnotnull(k#229L)
                        +- *(1) ColumnarToRow
                           +- FileScan parquet default.empty_right_table[k#229L] Batched: true, DataFilters: [isnotnull(k#229L)], Format: Parquet, Location: InMemoryFileIndex(1 paths)[file:/Users/chengsu/spark/sql/core/spark-warehouse/org.apache.spark.sq..., PartitionFilters: [], PushedFilters: [IsNotNull(k)], ReadSchema: struct<k:bigint>
```

Codegen failure - https://gist.github.com/c21/ea760c75b546d903247582be656d9d66 .

The uninitialized variable `_limit_counter_1` from `LocalLimitExec` is referenced in `ColumnarToRowExec`, but `BroadcastHashJoinExec` does not call `LocalLimitExec.doConsume()` to initialize the counter variable.

The fix is to move the counter variable initialization to `doProduce()`, as in whole stage code-gen framework, `doProduce()` will definitely be called if upstream operators `doProduce()`/`doConsume()` is called.

Note: this only happens in AQE disabled case, because we have an AQE optimization rule [EliminateUnnecessaryJoin](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/EliminateUnnecessaryJoin.scala#L69) to change the whole query to an empty `LocalRelation` if inner join broadcast side is empty with AQE enabled.

Fix query failure.

No.

Added unit test in `SQLQuerySuite.scala`.

Closes apache#31911 from c21/limit-fix-3.1.

Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>

Author: c21

sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala

@@ -122,14 +122,18 @@ trait BaseLimitExec extends LimitExec with CodegenSupport {
   }
 
   protected override def doProduce(ctx: CodegenContext): String = {
-    child.asInstanceOf[CodegenSupport].produce(ctx, this)
-  }
-
-  override def doConsume(ctx: CodegenContext, input: Seq[ExprCode], row: ExprCode): String = {
     // The counter name is already obtained by the upstream operators via `limitNotReachedChecks`.
     // Here we have to inline it to not change its name. This is fine as we won't have many limit
     // operators in one query.
+    //
+    // Note: create counter variable here instead of `doConsume()` to avoid compilation error,
+    // because upstream operators might not call `doConsume()` here
+    // (e.g. `HashJoin.codegenInner()`).
     ctx.addMutableState(CodeGenerator.JAVA_INT, countTerm, forceInline = true, useFreshName = false)
+    child.asInstanceOf[CodegenSupport].produce(ctx, this)
+  }
+
+  override def doConsume(ctx: CodegenContext, input: Seq[ExprCode], row: ExprCode): String = {
     s"""
        | if ($countTerm < $limit) {
        |   $countTerm += 1;

sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala

@@ -3950,6 +3950,25 @@ class SQLQuerySuite extends QueryTest with SharedSparkSession with AdaptiveSpark
       }
     }
   }
+
+  test("SPARK-34796: Avoid code-gen compilation error for LIMIT query") {
+    withTable("left_table", "empty_right_table", "output_table") {
+      spark.range(5).toDF("k").write.saveAsTable("left_table")
+      spark.range(0).toDF("k").write.saveAsTable("empty_right_table")
+
+      withSQLConf(SQLConf.ADAPTIVE_EXECUTION_ENABLED.key -> "false") {
+        spark.sql("CREATE TABLE output_table (k INT) USING parquet")
+        spark.sql(
+          """
+            |INSERT INTO TABLE output_table
+            |SELECT t1.k FROM left_table t1
+            |JOIN empty_right_table t2
+            |ON t1.k = t2.k
+            |LIMIT 3
+          """.stripMargin)
+      }
+    }
+  }
 }
 
 case class Foo(bar: Option[String])