From 4e197511569685916b5f55637878f4e60068bb58 Mon Sep 17 00:00:00 2001
From: Andy Grove <andygrove73@gmail.com>
Date: Fri, 16 Feb 2024 13:01:59 -0700
Subject: [PATCH] test: Add some fuzz testing for cast operations (#16)

---
 .../org/apache/comet/CometCastSuite.scala     | 113 ++++++++++++++++++
 1 file changed, 113 insertions(+)
 create mode 100644 spark/src/test/scala/org/apache/comet/CometCastSuite.scala

diff --git a/spark/src/test/scala/org/apache/comet/CometCastSuite.scala b/spark/src/test/scala/org/apache/comet/CometCastSuite.scala
new file mode 100644
index 000000000..565d2264b
--- /dev/null
+++ b/spark/src/test/scala/org/apache/comet/CometCastSuite.scala
@@ -0,0 +1,113 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you 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 org.apache.comet
+
+import java.io.File
+
+import scala.util.Random
+
+import org.apache.spark.sql.{CometTestBase, DataFrame, SaveMode}
+import org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanHelper
+import org.apache.spark.sql.functions.col
+import org.apache.spark.sql.types.{DataType, DataTypes}
+
+class CometCastSuite extends CometTestBase with AdaptiveSparkPlanHelper {
+  import testImplicits._
+
+  ignore("cast long to short") {
+    castTest(generateLongs, DataTypes.ShortType)
+  }
+
+  test("cast float to bool") {
+    castTest(generateFloats, DataTypes.BooleanType)
+  }
+
+  test("cast float to int") {
+    castTest(generateFloats, DataTypes.IntegerType)
+  }
+
+  ignore("cast float to string") {
+    castTest(generateFloats, DataTypes.StringType)
+  }
+
+  ignore("cast string to bool") {
+    castTest(
+      Seq("TRUE", "True", "true", "FALSE", "False", "false", "1", "0", "").toDF("a"),
+      DataTypes.BooleanType)
+    fuzzCastFromString("truefalseTRUEFALSEyesno10 \t\r\n", 8, DataTypes.BooleanType)
+  }
+
+  ignore("cast string to short") {
+    fuzzCastFromString("0123456789e+- \t\r\n", 8, DataTypes.ShortType)
+  }
+
+  ignore("cast string to float") {
+    fuzzCastFromString("0123456789e+- \t\r\n", 8, DataTypes.FloatType)
+  }
+
+  ignore("cast string to double") {
+    fuzzCastFromString("0123456789e+- \t\r\n", 8, DataTypes.DoubleType)
+  }
+
+  ignore("cast string to date") {
+    fuzzCastFromString("0123456789/ \t\r\n", 16, DataTypes.DateType)
+  }
+
+  ignore("cast string to timestamp") {
+    castTest(Seq("2020-01-01T12:34:56.123456", "T2").toDF("a"), DataTypes.TimestampType)
+    fuzzCastFromString("0123456789/:T \t\r\n", 32, DataTypes.TimestampType)
+  }
+
+  private def generateFloats = {
+    val r = new Random(0)
+    Range(0, 10000).map(_ => r.nextFloat()).toDF("a")
+  }
+
+  private def generateLongs = {
+    val r = new Random(0)
+    Range(0, 10000).map(_ => r.nextLong()).toDF("a")
+  }
+
+  private def genString(r: Random, chars: String, maxLen: Int): String = {
+    val len = r.nextInt(maxLen)
+    Range(0, len).map(_ => chars.charAt(r.nextInt(chars.length))).mkString
+  }
+
+  private def fuzzCastFromString(chars: String, maxLen: Int, toType: DataType) {
+    val r = new Random(0)
+    val inputs = Range(0, 10000).map(_ => genString(r, chars, maxLen))
+    castTest(inputs.toDF("a"), toType)
+  }
+
+  private def castTest(input: DataFrame, toType: DataType) {
+    withTempPath { dir =>
+      val df = roundtripParquet(input, dir)
+        .withColumn("converted", col("a").cast(toType))
+      checkSparkAnswer(df)
+    }
+  }
+
+  private def roundtripParquet(df: DataFrame, tempDir: File): DataFrame = {
+    val filename = new File(tempDir, s"castTest_${System.currentTimeMillis()}.parquet").toString
+    df.write.mode(SaveMode.Overwrite).parquet(filename)
+    spark.read.parquet(filename)
+  }
+
+}