test: Create test data in-memory

instead of reading from files

src/test/scala/com/crealytics/spark/excel/v2/DataFrameWriterApiComplianceSuite.scala

@@ -22,13 +22,12 @@ import org.scalatest.wordspec.AnyWordSpec
 
 class DataFrameWriterApiComplianceSuite extends AnyWordSpec with DataFrameSuiteBase with LocalFileTestingUtilities {
 
-  private def readSimpleCsv = {
-    spark.read
-      .format("csv")
-      .option("delimiter", ";")
-      .option("header", "true")
-      .option("path", "src/test/resources/v2readwritetest/simple_csv/simple.csv")
-      .load()
+  private def simpleDf = {
+    val data = Seq(
+      ("foo", "bar", "1"),
+      ("baz", "bang", "2")
+    )
+    spark.createDataFrame(data).toDF("col1", "col2", "col3")
   }
 
   /** Checks that the excel data files in given folder equal the provided dataframe */
@@ -56,9 +55,9 @@ class DataFrameWriterApiComplianceSuite extends AnyWordSpec with DataFrameSuiteB
     for (writeMode <- writeModes) {
       s"write a dataframe to xlsx with ${writeMode.toString}" in withExistingCleanTempDir("v2") { targetDir =>
         // create a df from csv then write as xlsx
-        val dfCsv = readSimpleCsv
+        val df = simpleDf
 
-        dfCsv.write
+        df.write
           .format("excel")
           .option("path", targetDir)
           .option("header", value = true)
@@ -69,16 +68,16 @@ class DataFrameWriterApiComplianceSuite extends AnyWordSpec with DataFrameSuiteB
         assert(listOfFiles.nonEmpty, s"expected at least one excel file")
 
         // is the result really the same?
-        assertWrittenExcelData(dfCsv, targetDir)
+        assertWrittenExcelData(df, targetDir)
 
       }
       s"write a dataframe to xlsx with ${writeMode.toString} (partitioned)" in withExistingCleanTempDir("v2") {
         targetDir =>
           assume(spark.sparkContext.version >= "3.0.1")
           // create a df from csv then write as xlsx
-          val dfCsv = readSimpleCsv
+          val df = simpleDf
 
-          dfCsv.write
+          df.write
             .partitionBy("col1")
             .format("excel")
             .option("path", targetDir)
@@ -96,7 +95,7 @@ class DataFrameWriterApiComplianceSuite extends AnyWordSpec with DataFrameSuiteB
           }
 
           // is the result really the same?
-          assertWrittenExcelData(dfCsv, targetDir)
+          assertWrittenExcelData(df, targetDir)
 
       }
     }
@@ -107,9 +106,9 @@ class DataFrameWriterApiComplianceSuite extends AnyWordSpec with DataFrameSuiteB
           assume(spark.sparkContext.version >= "3.0.1")
         }
 
-        val dfCsv = readSimpleCsv
+        val df = simpleDf
 
-        val dfWriter = if (isPartitioned) dfCsv.write else dfCsv.write.partitionBy("col1")
+        val dfWriter = if (isPartitioned) df.write else df.write.partitionBy("col1")
 
         dfWriter
           .format("excel")
@@ -124,9 +123,9 @@ class DataFrameWriterApiComplianceSuite extends AnyWordSpec with DataFrameSuiteB
           .mode(SaveMode.Append)
           .save()
 
-        val orderedSchemaColumns = dfCsv.schema.fields.map(f => f.name).sorted
+        val orderedSchemaColumns = df.schema.fields.map(f => f.name).sorted
         val expectedDf =
-          dfCsv.union(dfCsv).select(orderedSchemaColumns.head, orderedSchemaColumns.tail.toIndexedSeq: _*)
+          df.union(df).select(orderedSchemaColumns.head, orderedSchemaColumns.tail.toIndexedSeq: _*)
 
         assertWrittenExcelData(expectedDf, targetDir)
       }

src/test/scala/com/crealytics/spark/excel/v2/ManyPartitionReadSuite.scala

@@ -21,6 +21,7 @@ import org.apache.spark.sql._
 import org.apache.spark.sql.functions.col
 import org.apache.spark.sql.types.IntegerType
 import org.scalatest.wordspec.AnyWordSpec
+import org.apache.spark.sql.types.{StructType, StructField, StringType}
 
 class ManyPartitionReadSuite extends AnyWordSpec with DataFrameSuiteBase with LocalFileTestingUtilities {
 
@@ -45,14 +46,36 @@ class ManyPartitionReadSuite extends AnyWordSpec with DataFrameSuiteBase with Lo
 
   def createExpected(targetDir: String): DataFrame = {
 
-    val dfCsv = spark.read
-      .format("csv")
-      .option("delimiter", ",")
-      .option("header", "true")
-      .option("path", "src/test/resources/v2readwritetest/partition_csv/partition.csv")
-      .load()
+    // Generate data programmatically
+    val data = (1 to 19).flatMap { col1 =>
+      // Each col1 value has multiple rows (around 10-11 rows each)
+      val rowsPerPartition = if (col1 == 1) 8 else if (col1 == 2) 16 else 11
+      (0 until rowsPerPartition).map { i =>
+        val index = (col1 - 1) * 11 + i + 1234  // Starting from 1234 as in original data
+        Row(
+          Integer.valueOf(col1),  // Make it nullable Integer
+          s"fubar_$index",
+          s"bazbang_${index + 77000}",
+          s"barfang_${index + 237708}",
+          s"lorem_ipsum_$index"
+        )
+      }
+    }
+
+    // Define schema explicitly to match expected nullability
+    val schema = StructType(Array(
+      StructField("col1", IntegerType, nullable = true),
+      StructField("col2", StringType, nullable = true),
+      StructField("col3", StringType, nullable = true),
+      StructField("col4", StringType, nullable = true),
+      StructField("col5", StringType, nullable = true)
+    ))
+
+    val dfInput = spark.createDataFrame(spark.sparkContext.parallelize(data), schema)
+
+
 
-    val dfFinal = dfCsv.union(dfCsv)
+    val dfFinal = dfInput.union(dfInput)
 
     val dfWriter = dfFinal.write
       .partitionBy("col1")
@@ -64,7 +87,7 @@ class ManyPartitionReadSuite extends AnyWordSpec with DataFrameSuiteBase with Lo
     dfWriter.save()
     dfWriter.save()
 
-    val orderedSchemaColumns = dfCsv.schema.fields.map(f => f.name).sorted
+    val orderedSchemaColumns = dfInput.schema.fields.map(f => f.name).sorted
 
     dfFinal
       .union(dfFinal)