Fix ArrayIndexOutOfBoundsException on join counts with constant join keys

integration_tests/src/main/python/join_test.py

@@ -14,10 +14,10 @@
 
 import pytest
 from _pytest.mark.structures import ParameterSet
-from pyspark.sql.functions import array_contains, broadcast, col
+from pyspark.sql.functions import array_contains, broadcast, col, lit
 from pyspark.sql.types import *
-from asserts import assert_gpu_and_cpu_are_equal_collect, assert_gpu_fallback_collect, assert_cpu_and_gpu_are_equal_collect_with_capture
-from conftest import is_databricks_runtime, is_emr_runtime, is_not_utc
+from asserts import assert_gpu_and_cpu_are_equal_collect, assert_gpu_and_cpu_row_counts_equal, assert_gpu_fallback_collect, assert_cpu_and_gpu_are_equal_collect_with_capture
+from conftest import is_emr_runtime
 from data_gen import *
 from marks import ignore_order, allow_non_gpu, incompat, validate_execs_in_gpu_plan
 from spark_session import with_cpu_session, is_before_spark_330, is_databricks_runtime
@@ -164,6 +164,14 @@ def do_join(spark):
         return left.join(right.hint("broadcast"), left.a == right.r_a, join_type)
     assert_gpu_and_cpu_are_equal_collect(do_join, conf={'spark.sql.adaptive.enabled': 'true'})
 
+@pytest.mark.parametrize('join_type', ['Left', 'Inner', 'LeftSemi', 'LeftAnti'], ids=idfn)
+def test_broadcast_hash_join_constant_keys(join_type):
+    def do_join(spark):
+        left = spark.range(10).withColumn("s", lit(1))
+        right = spark.range(10000).withColumn("r_s", lit(1))
+        return left.join(right.hint("broadcast"), left.s == right.r_s, join_type)
+    assert_gpu_and_cpu_row_counts_equal(do_join, conf={'spark.sql.adaptive.enabled': 'true'})
+
 
 # local sort because of https://github.com/NVIDIA/spark-rapids/issues/84
 # After 3.1.0 is the min spark version we can drop this

sql-plugin/src/main/scala/org/apache/spark/sql/rapids/execution/GpuBroadcastNestedLoopJoinExecBase.scala

@@ -558,6 +558,16 @@ abstract class GpuBroadcastNestedLoopJoinExecBase(
     broadcastExchange.executeColumnarBroadcast[Any]()
   }
 
+  private def isUnconditionalJoin(condition: Option[GpuExpression]): Boolean = {
+    condition.forall {
+      case GpuLiteral(true, BooleanType) =>
+        // Spark can generate a degenerate conditional join when the join keys are constants
+        output.isEmpty
+      case GpuAlias(e: GpuExpression, _) => isUnconditionalJoin(Some(e))
+      case _ => false
+    }
+  }
+
   override def internalDoExecuteColumnar(): RDD[ColumnarBatch] = {
     // Determine which table will be first in the join and bind the references accordingly
     // so the AST column references match the appropriate table.
@@ -583,7 +593,9 @@ abstract class GpuBroadcastNestedLoopJoinExecBase(
       if (useTrueCondition) Some(GpuLiteral(true)) else None
     }
 
-    if (joinCondition.isEmpty) {
+    // Sometimes Spark specifies a true condition for a row-count-only join.
+    // This can happen when the join keys are detected to be constant.
+    if (isUnconditionalJoin(joinCondition)) {
       doUnconditionalJoin(broadcastRelation)
     } else {
       doConditionalJoin(broadcastRelation, joinCondition, numFirstTableColumns)