Enable approx percentile tests

integration_tests/src/main/python/hash_aggregate_test.py

@@ -1124,51 +1124,67 @@ def do_it(spark):
         return df.groupBy('a').agg(f.min(df.b[1]["a"]))
     assert_gpu_and_cpu_are_equal_collect(do_it)
 
-@pytest.mark.skip(reason="https://github.com/NVIDIA/spark-rapids/issues/3692")
-@ignore_order(local=True)
-def test_hash_groupby_approx_percentile_long_repeated_keys():
+@pytest.mark.parametrize('aqe_enabled', ['false', 'true'], ids=idfn)
+def test_hash_groupby_approx_percentile_byte(aqe_enabled):
+    conf = copy_and_update(_approx_percentile_conf, {'spark.sql.adaptive.enabled': aqe_enabled})
+    compare_percentile_approx(
+        lambda spark: gen_df(spark, [('k', StringGen(nullable=False)),
+                                     ('v', ByteGen())], length=100),
+        [0.05, 0.25, 0.5, 0.75, 0.95], conf)
+
+@pytest.mark.parametrize('aqe_enabled', ['false', 'true'], ids=idfn)
+def test_hash_groupby_approx_percentile_byte_scalar(aqe_enabled):
+    conf = copy_and_update(_approx_percentile_conf, {'spark.sql.adaptive.enabled': aqe_enabled})
+    compare_percentile_approx(
+        lambda spark: gen_df(spark, [('k', StringGen(nullable=False)),
+                                     ('v', ByteGen())], length=100),
+        0.5, conf)
+
+@pytest.mark.parametrize('aqe_enabled', ['false', 'true'], ids=idfn)
+def test_hash_groupby_approx_percentile_long_repeated_keys(aqe_enabled):
+    conf = copy_and_update(_approx_percentile_conf, {'spark.sql.adaptive.enabled': aqe_enabled})
     compare_percentile_approx(
         lambda spark: gen_df(spark, [('k', RepeatSeqGen(LongGen(), length=20)),
                                      ('v', LongRangeGen())], length=100),
-        [0.05, 0.25, 0.5, 0.75, 0.95])
+        [0.05, 0.25, 0.5, 0.75, 0.95], conf)
 
-@pytest.mark.skip(reason="https://github.com/NVIDIA/spark-rapids/issues/3692")
-@ignore_order(local=True)
-def test_hash_groupby_approx_percentile_long():
+@pytest.mark.parametrize('aqe_enabled', ['false', 'true'], ids=idfn)
+def test_hash_groupby_approx_percentile_long(aqe_enabled):
+    conf = copy_and_update(_approx_percentile_conf, {'spark.sql.adaptive.enabled': aqe_enabled})
     compare_percentile_approx(
         lambda spark: gen_df(spark, [('k', StringGen(nullable=False)),
                                      ('v', LongRangeGen())], length=100),
-        [0.05, 0.25, 0.5, 0.75, 0.95])
+        [0.05, 0.25, 0.5, 0.75, 0.95], conf)
 
-@pytest.mark.skip(reason="https://github.com/NVIDIA/spark-rapids/issues/3692")
-@ignore_order(local=True)
-def test_hash_groupby_approx_percentile_long_scalar():
+@pytest.mark.parametrize('aqe_enabled', ['false', 'true'], ids=idfn)
+def test_hash_groupby_approx_percentile_long_scalar(aqe_enabled):
+    conf = copy_and_update(_approx_percentile_conf, {'spark.sql.adaptive.enabled': aqe_enabled})
     compare_percentile_approx(
         lambda spark: gen_df(spark, [('k', StringGen(nullable=False)),
                                      ('v', LongRangeGen())], length=100),
-        0.5)
+        0.5, conf)
 
-@pytest.mark.skip(reason="https://github.com/NVIDIA/spark-rapids/issues/3692")
-@ignore_order(local=True)
-def test_hash_groupby_approx_percentile_double():
+@pytest.mark.parametrize('aqe_enabled', ['false', 'true'], ids=idfn)
+def test_hash_groupby_approx_percentile_double(aqe_enabled):
+    conf = copy_and_update(_approx_percentile_conf, {'spark.sql.adaptive.enabled': aqe_enabled})
     compare_percentile_approx(
         lambda spark: gen_df(spark, [('k', StringGen(nullable=False)),
                                      ('v', DoubleGen())], length=100),
-        [0.05, 0.25, 0.5, 0.75, 0.95])
+        [0.05, 0.25, 0.5, 0.75, 0.95], conf)
 
-@pytest.mark.skip(reason="https://github.com/NVIDIA/spark-rapids/issues/3692")
-@ignore_order(local=True)
-def test_hash_groupby_approx_percentile_double_scalar():
+@pytest.mark.parametrize('aqe_enabled', ['false', 'true'], ids=idfn)
+def test_hash_groupby_approx_percentile_double_scalar(aqe_enabled):
+    conf = copy_and_update(_approx_percentile_conf, {'spark.sql.adaptive.enabled': aqe_enabled})
     compare_percentile_approx(
         lambda spark: gen_df(spark, [('k', StringGen(nullable=False)),
                                      ('v', DoubleGen())], length=100),
-        0.05)
+        0.05, conf)
 
 # The percentile approx tests differ from other tests because we do not expect the CPU and GPU to produce the same
 # results due to the different algorithms being used. Instead we compute an exact percentile on the CPU and then
 # compute approximate percentiles on CPU and GPU and assert that the GPU numbers are accurate within some percentage
 # of the CPU numbers
-def compare_percentile_approx(df_fun, percentiles):
+def compare_percentile_approx(df_fun, percentiles, conf):
 
     # create SQL statements for exact and approx percentiles
     p_exact_sql = create_percentile_sql("percentile", percentiles)
@@ -1190,32 +1206,44 @@ def run_approx(spark):
     # run approx_percentile on CPU and GPU
     approx_cpu, approx_gpu = run_with_cpu_and_gpu(run_approx, 'COLLECT', _approx_percentile_conf)
 
-    for result in zip(exact, approx_cpu, approx_gpu):
+    assert len(exact) == len(approx_cpu)
+    assert len(exact) == len(approx_gpu)
+
+    for i in range(len(exact)):
+        cpu_exact_result = exact[i]
+        cpu_approx_result = approx_cpu[i]
+        gpu_approx_result = approx_gpu[i]
+
         # assert that keys match
-        assert result[0]['k'] == result[1]['k']
-        assert result[1]['k'] == result[2]['k']
-
-        exact = result[0]['the_percentile']
-        cpu = result[1]['the_percentile']
-        gpu = result[2]['the_percentile']
-
-        if exact is not None:
-            if isinstance(exact, list):
-                for x in zip(exact, cpu, gpu):
-                    exact = x[0]
-                    cpu = x[1]
-                    gpu = x[2]
-                    gpu_delta = abs(float(gpu) - float(exact))
-                    cpu_delta = abs(float(cpu) - float(exact))
+        assert cpu_exact_result['k'] == cpu_approx_result['k']
+        assert cpu_exact_result['k'] == gpu_approx_result['k']
+
+        # extract the percentile result column
+        exact_percentile = cpu_exact_result['the_percentile']
+        cpu_approx_percentile = cpu_approx_result['the_percentile']
+        gpu_approx_percentile = gpu_approx_result['the_percentile']
+
+        if exact_percentile is None:
+            assert cpu_approx_percentile is None
+            assert gpu_approx_percentile is None
+        else:
+            assert cpu_approx_percentile is not None
+            assert gpu_approx_percentile is not None
+            if isinstance(exact_percentile, list):
+                for j in range(len(exact_percentile)):
+                    assert cpu_approx_percentile[j] is not None
+                    assert gpu_approx_percentile[j] is not None
+                    gpu_delta = abs(float(gpu_approx_percentile[j]) - float(exact_percentile[j]))
+                    cpu_delta = abs(float(cpu_approx_percentile[j]) - float(exact_percentile[j]))
                     if gpu_delta > cpu_delta:
                         # GPU is less accurate so make sure we are within some tolerance
                         if gpu_delta == 0:
                             assert abs(gpu_delta / cpu_delta) - 1 < 0.001
                         else:
                             assert abs(cpu_delta / gpu_delta) - 1 < 0.001
             else:
-                gpu_delta = abs(float(gpu) - float(exact))
-                cpu_delta = abs(float(cpu) - float(exact))
+                gpu_delta = abs(float(gpu_approx_percentile) - float(exact_percentile))
+                cpu_delta = abs(float(cpu_approx_percentile) - float(exact_percentile))
                 if gpu_delta > cpu_delta:
                     # GPU is less accurate so make sure we are within some tolerance
                     if gpu_delta == 0:
@@ -1225,10 +1253,10 @@ def run_approx(spark):
 
 def create_percentile_sql(func_name, percentiles):
     if isinstance(percentiles, list):
-        return """select k, {}(v, array({})) as the_percentile from t group by k""".format(
+        return """select k, {}(v, array({})) as the_percentile from t group by k order by k""".format(
             func_name, ",".join(str(i) for i in percentiles))
     else:
-        return """select k, {}(v, {}) as the_percentile from t group by k""".format(
+        return """select k, {}(v, {}) as the_percentile from t group by k order by k""".format(
             func_name, percentiles)
 
 @ignore_order

tests/src/test/scala/com/nvidia/spark/rapids/ApproximatePercentileSuite.scala

@@ -28,46 +28,60 @@ class ApproximatePercentileSuite extends SparkQueryCompareTestSuite {
 
   val DEFAULT_PERCENTILES = Array(0.005, 0.05, 0.25, 0.45, 0.5, 0.55, 0.75, 0.95, 0.995)
 
+  test("null handling") {
+    val func = spark => salariesWithNull(spark)
+    doTest(func, delta = Some(100))
+  }
+
   test("1 row per group, delta 100, doubles") {
-    doTest(DataTypes.DoubleType, rowsPerGroup = 1, delta = Some(100))
+    val func = spark => salaries(spark, DataTypes.DoubleType, rowsPerDept = 1)
+    doTest(func, delta = Some(100))
   }
 
   test("5 rows per group, delta 100, doubles") {
-    doTest(DataTypes.DoubleType, rowsPerGroup = 5, delta = Some(100))
+    val func = spark => salaries(spark, DataTypes.DoubleType, rowsPerDept = 5)
+    doTest(func, delta = Some(100))
   }
 
   test("250 rows per group, delta 100, doubles") {
-    doTest(DataTypes.DoubleType, rowsPerGroup = 250, delta = Some(100))
+    val func = spark => salaries(spark, DataTypes.DoubleType, rowsPerDept = 250)
+    doTest(func, delta = Some(100))
   }
 
   test("2500 rows per group, delta 100, doubles") {
-    doTest(DataTypes.DoubleType, rowsPerGroup = 2500, delta = Some(100))
+    val func = spark => salaries(spark, DataTypes.DoubleType, rowsPerDept = 2500)
+    doTest(func, delta = Some(100))
   }
 
   test("250 rows per group, default delta, doubles") {
-    doTest(DataTypes.DoubleType, rowsPerGroup = 250, delta = None)
+    val func = spark => salaries(spark, DataTypes.DoubleType, rowsPerDept = 250)
+    doTest(func, delta = None)
   }
 
   test("25000 rows per group, default delta, doubles") {
-    doTest(DataTypes.DoubleType, rowsPerGroup = 25000, delta = None)
+    val func = spark => salaries(spark, DataTypes.DoubleType, rowsPerDept = 25000)
+    doTest(func, delta = None)
   }
 
   test("50000 rows per group, default delta, doubles") {
-    doTest(DataTypes.DoubleType, rowsPerGroup = 50000, delta = None)
+    val func = spark => salaries(spark, DataTypes.DoubleType, rowsPerDept = 50000)
+    doTest(func, delta = None)
   }
 
   // test with a threshold just below the default level of 10000
   test("50000 rows per group, delta 9999, doubles") {
-    doTest(DataTypes.DoubleType, rowsPerGroup = 50000, delta = Some(9999))
+    val func = spark => salaries(spark, DataTypes.DoubleType, rowsPerDept = 50000)
+    doTest(func, delta = Some(9999))
   }
 
   test("empty input set") {
-    doTest(DataTypes.DoubleType, rowsPerGroup = 0, delta = None)
+    val func = spark => salaries(spark, DataTypes.DoubleType, rowsPerDept = 1)
+    doTest(func, delta = None)
   }
 
   test("scalar percentile") {
-    doTest(DataTypes.DoubleType, rowsPerGroup = 250,
-      percentileArg = Left(0.5), delta = Some(100))
+    val func = spark => salaries(spark, DataTypes.DoubleType, rowsPerDept = 250)
+    doTest(func, percentileArg = Left(0.5), delta = Some(100))
   }
 
   test("empty percentile array fall back to CPU") {
@@ -105,25 +119,25 @@ class ApproximatePercentileSuite extends SparkQueryCompareTestSuite {
     }, conf)
   }
 
-  private def doTest(dataType: DataType,
-      rowsPerGroup: Int,
-      percentileArg: Either[Double, Array[Double]] = Right(DEFAULT_PERCENTILES),
-      delta: Option[Int]) {
+  private def doTest(
+    func: SparkSession => DataFrame,
+    percentileArg: Either[Double, Array[Double]] = Right(DEFAULT_PERCENTILES),
+    delta: Option[Int]) {
 
     val percentiles = withCpuSparkSession { spark =>
-      calcPercentiles(spark, dataType, rowsPerGroup, percentileArg, delta,
+      calcPercentiles(spark, func, percentileArg, delta,
         approx = false)
     }
 
     val approxPercentilesCpu = withCpuSparkSession { spark =>
-      calcPercentiles(spark, dataType, rowsPerGroup, percentileArg, delta, approx = true)
+      calcPercentiles(spark, func, percentileArg, delta, approx = true)
     }
 
     val conf = new SparkConf()
       .set("spark.rapids.sql.expression.ApproximatePercentile", "true")
 
     val approxPercentilesGpu = withGpuSparkSession(spark =>
-      calcPercentiles(spark, dataType, rowsPerGroup, percentileArg, delta, approx = true)
+      calcPercentiles(spark, func, percentileArg, delta, approx = true)
     , conf)
 
     val keys = percentiles.keySet ++ approxPercentilesCpu.keySet ++ approxPercentilesGpu.keySet
@@ -165,14 +179,13 @@ class ApproximatePercentileSuite extends SparkQueryCompareTestSuite {
 
   private def calcPercentiles(
       spark: SparkSession,
-      dataType: DataType,
-      rowsPerDept: Int,
+      dfFunc: SparkSession => DataFrame,
       percentilesArg: Either[Double, Array[Double]],
       delta: Option[Int],
       approx: Boolean
     ): Map[String, Array[Double]] = {
 
-    val df = salaries(spark, dataType, rowsPerDept)
+    val df = dfFunc(spark)
 
     val percentileArg = percentilesArg match {
       case Left(n) => s"$n"
@@ -210,6 +223,14 @@ class ApproximatePercentileSuite extends SparkQueryCompareTestSuite {
     }).toMap
   }
 
+  private def salariesWithNull(spark: SparkSession): DataFrame = {
+    import spark.implicits._
+    Seq(("a", null), ("b", null),  ("b", "123456.78")).toDF("dept", "x")
+      .withColumn("salary", expr("CAST(x AS double)"))
+      .drop("x")
+      .repartition(2)
+  }
+
   private def salaries(
       spark: SparkSession,
       salaryDataType: DataType, rowsPerDept: Int): DataFrame = {