Add in basic support for running tpcds like queries

integration_tests/conftest.py

@@ -20,6 +20,12 @@ def pytest_addoption(parser):
     parser.addoption(
         "--tpcxbb_path", action="store", default=None, help="path to TPCXbb data"
     )
+    parser.addoption(
+        "--tpcds_format", action="store", default="parquet", help="format of TPC-DS data"
+    )
+    parser.addoption(
+        "--tpcds_path", action="store", default=None, help="path to TPC-DS data"
+    )
     parser.addoption(
         "--tpch_format", action="store", default="parquet", help="format of TPCH data"
     )

integration_tests/src/main/python/asserts.py

@@ -14,6 +14,7 @@
 
 from conftest import is_incompat, should_sort_on_spark, should_sort_locally, get_float_check, get_limit, spark_jvm
 from datetime import date, datetime
+from decimal import Decimal
 import math
 from pyspark.sql import Row
 import pytest
@@ -73,6 +74,8 @@ def _assert_equal(cpu, gpu, float_check, path):
         assert cpu == gpu, "GPU and CPU date values are different at {}".format(path)
     elif isinstance(cpu, bool):
         assert cpu == gpu, "GPU and CPU boolean values are different at {}".format(path)
+    elif isinstance(cpu, Decimal):
+        assert cpu == gpu, "GPU and CPU decimal values are different at {}".format(path)
     elif (cpu == None):
         assert cpu == gpu, "GPU and CPU are not both null at {}".format(path)
     else:

integration_tests/src/main/python/conftest.py

@@ -207,6 +207,8 @@ def setup(self, spark):
       "parquet": jvm.com.nvidia.spark.rapids.tests.tpch.TpchLikeSpark.setupAllParquet,
       "orc": jvm.com.nvidia.spark.rapids.tests.tpch.TpchLikeSpark.setupAllOrc
     }
+    if not self.tpch_format in formats:
+        raise RuntimeError("{} is not a supported tpch input type".format(self.tpch_format))
     formats.get(self.tpch_format)(jvm_session, self.tpch_path)
 
   def do_test_query(self, query):
@@ -267,6 +269,8 @@ def setup(self, spark):
       "parquet": jvm.com.nvidia.spark.rapids.tests.tpcxbb.TpcxbbLikeSpark.setupAllParquet,
       "orc": jvm.com.nvidia.spark.rapids.tests.tpcxbb.TpcxbbLikeSpark.setupAllOrc
     }
+    if not self.tpcxbb_format in formats:
+        raise RuntimeError("{} is not a supported tpcxbb input type".format(self.tpcxbb_format))
     formats.get(self.tpcxbb_format)(jvm_session,self.tpcxbb_path)
 
   def do_test_query(self, query):
@@ -327,4 +331,37 @@ def mortgage(request):
     else:
         yield MortgageRunner(mortgage_format, mortgage_path + '/acq', mortgage_path + '/perf')
 
+class TpcdsRunner:
+  def __init__(self, tpcds_format, tpcds_path):
+    self.tpcds_format = tpcds_format
+    self.tpcds_path = tpcds_path
+    self.setup(get_spark_i_know_what_i_am_doing())
+
+  def setup(self, spark):
+    jvm_session = _get_jvm_session(spark)
+    jvm = _get_jvm(spark)
+    formats = {
+      "csv": jvm.com.nvidia.spark.rapids.tests.tpcds.TpcdsLikeSpark.setupAllCSV,
+      "parquet": jvm.com.nvidia.spark.rapids.tests.tpcds.TpcdsLikeSpark.setupAllParquet,
+      "orc": jvm.com.nvidia.spark.rapids.tests.tpcds.TpcdsLikeSpark.setupAllOrc
+    }
+    if not self.tpcds_format in formats:
+        raise RuntimeError("{} is not a supported tpcds input type".format(self.tpcds_format))
+    formats.get(self.tpcds_format)(jvm_session,self.tpcds_path)
+
+  def do_test_query(self, query):
+    spark = get_spark_i_know_what_i_am_doing()
+    jvm_session = _get_jvm_session(spark)
+    jvm = _get_jvm(spark)
+    df = jvm.com.nvidia.spark.rapids.tests.tpcds.TpcdsLikeSpark.run(jvm_session, query)
+    return DataFrame(df, spark.getActiveSession())
+
+@pytest.fixture(scope="session")
+def tpcds(request):
+  tpcds_format = request.config.getoption("tpcds_format")
+  tpcds_path = request.config.getoption("tpcds_path")
+  if tpcds_path is None:
+    pytest.skip("TPC-DS not configured to run")
+  else:
+    yield TpcdsRunner(tpcds_format, tpcds_path)
 

integration_tests/src/main/python/tpcds_test.py

@@ -0,0 +1,39 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pytest
+
+from asserts import assert_gpu_and_cpu_are_equal_collect
+from marks import incompat, ignore_order, allow_non_gpu, approximate_float
+
+queries = ['q1', 'q2', 'q3', 'q4', 'q5', 'q6', 'q7', 'q8', 'q9',
+        'q10', 'q11', 'q12', 'q13', 'q14a', 'q14b', 'q15', 'q16', 'q17', 'q18', 'q19',
+        'q20', 'q21', 'q22', 'q23a', 'q23b', 'q24a', 'q24b', 'q25', 'q26', 'q27', 'q28', 'q29',
+        'q30', 'q31', 'q32', 'q33', 'q34', 'q35', 'q36', 'q37', 'q38', 'q39a', 'q39b',
+        'q40', 'q41', 'q42', 'q43', 'q44', 'q45', 'q46', 'q47', 'q48', 'q49',
+        'q50', 'q51', 'q52', 'q53', 'q54', 'q55', 'q56', 'q57', 'q58', 'q59',
+        'q60', 'q61', 'q62', 'q63', 'q64', 'q65', 'q66', 'q67', 'q68', 'q69',
+        'q70', 'q71', 'q72', 'q73', 'q74', 'q75', 'q76', 'q77', 'q78', 'q79',
+        'q80', 'q81', 'q82', 'q83', 'q84', 'q85', 'q86', 'q87', 'q88', 'q89',
+        'q90', 'q91', 'q92', 'q93', 'q94', 'q95', 'q96', 'q97', 'q98', 'q99',
+        'ss_max', 'ss_maxb']
+
+@incompat
+@ignore_order
+@approximate_float
+@allow_non_gpu(any=True)
+@pytest.mark.parametrize('query', queries)
+def test_tpcds(tpcds, query):
+  assert_gpu_and_cpu_are_equal_collect(
+          lambda spark : tpcds.do_test_query(query))

integration_tests/src/main/scala/com/nvidia/spark/rapids/tests/tpcds/TpcdsLikeBench.scala

@@ -0,0 +1,82 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.spark.rapids.tests.tpcds
+
+import java.util.concurrent.TimeUnit.NANOSECONDS
+
+import scala.collection.mutable.ListBuffer
+
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.SparkSession
+
+object TpcdsLikeBench extends Logging {
+
+  /**
+   * This method can be called from Spark shell using the following syntax:
+   *
+   * TpcdsLikeBench.runBench(spark, "q5")
+   */
+  def runBench(
+      spark: SparkSession,
+      query: String,
+      numColdRuns: Int = 1,
+      numHotRuns: Int = 3): Unit = {
+
+    val coldRunElapsed = new ListBuffer[Long]()
+    for (i <- 0 until numColdRuns) {
+      println(s"*** Start cold run $i:")
+      val start = System.nanoTime()
+      TpcdsLikeSpark.run(spark, query).collect
+      val end = System.nanoTime()
+      val elapsed = NANOSECONDS.toMillis(end - start)
+      coldRunElapsed.append(elapsed)
+      println(s"*** Cold run $i took $elapsed msec.")
+    }
+
+    val hotRunElapsed = new ListBuffer[Long]()
+    for (i <- 0 until numHotRuns) {
+      println(s"*** Start hot run $i:")
+      val start = System.nanoTime()
+      TpcdsLikeSpark.run(spark, query).collect
+      val end = System.nanoTime()
+      val elapsed = NANOSECONDS.toMillis(end - start)
+      hotRunElapsed.append(elapsed)
+      println(s"*** Hot run $i took $elapsed msec.")
+    }
+
+    for (i <- 0 until numColdRuns) {
+      println(s"Cold run $i took ${coldRunElapsed(i)} msec.")
+    }
+    println(s"Average cold run took ${coldRunElapsed.sum.toDouble/numColdRuns} msec.")
+
+    for (i <- 0 until numHotRuns) {
+      println(s"Hot run $i took ${hotRunElapsed(i)} msec.")
+    }
+    println(s"Average hot run took ${hotRunElapsed.sum.toDouble/numHotRuns} msec.")
+  }
+
+  def main(args: Array[String]): Unit = {
+    val input = args(0)
+    val query = args(1)
+
+    val spark = SparkSession.builder.appName("TPC-DS Like Bench").getOrCreate()
+    TpcdsLikeSpark.setupAllParquet(spark, input)
+
+    println(s"*** RUNNING TPC-DS QUERY $query")
+    runBench(spark, query)
+  }
+}