#672 Add tests for various scenarios.

README.md

@@ -32,7 +32,7 @@ Among the motivations for this project, it is possible to highlight:
 
 - Supports REDEFINES, OCCURS and DEPENDING ON fields (e.g. unchecked unions and variable-size arrays)
 
-- Supports nested structures and arrays (including "flattened" nested names)
+- Supports nested structures and arrays
 
 - Supports HDFS as well as local file systems
 
@@ -350,8 +350,18 @@ Currently, specifying multiple paths in `load()` is not supported. Use the follo
 ### Spark SQL schema extraction
 This library also provides convenient methods to extract Spark SQL schemas and Cobol layouts from copybooks.  
 
-If you want to extract a Spark SQL schema from a copybook: 
+If you want to extract a Spark SQL schema from a copybook by providing same options you provide to Spark: 
+```scala
+// Same options that you use for spark.read.format("cobol").option()
+val options = Map("schema_retention_policy" -> "keep_original")
+
+val cobolSchema = CobolSchema.fromSparkOptions(Seq(copybook), options)
+val sparkSchema = cobolSchema.getSparkSchema.toString()
+
+println(sparkSchema)
+```
 
+If you want to extract a Spark SQL schema from a copybook using the Cobol parser directly:
 ```scala
 import za.co.absa.cobrix.cobol.parser.CopybookParser
 import za.co.absa.cobrix.cobol.reader.policies.SchemaRetentionPolicy
@@ -1397,6 +1407,74 @@ When using `9` 8 refers to the number of digits the number has. Here, the size o
 ```
 You can have decimals when using COMP-3 as well.
 
+### Flattening schema with GROUPs and OCCURS
+Flattening could be helpful when migrating data from mainframe data with fields that have OCCURs (arrays) to a relational
+databases that do not support nested arrays.
+
+Cobrix has a method that can flatten the schema automatically given a DataFrame produced by `spark-cobol`.
+
+Spark Scala example:
+```scala
+val dfFlat = SparkUtils.flattenSchema(df, useShortFieldNames = false)
+```
+
+PySpark example
+```python
+from pyspark.sql import SparkSession, DataFrame, SQLContext
+from pyspark.sql.types import StructType, StructField, StringType, IntegerType, ArrayType
+from py4j.java_gateway import java_import
+
+schema = StructType([
+   StructField("id", IntegerType(), True),
+   StructField("name", StringType(), True),
+   StructField("subjects", ArrayType(StringType()), True)
+])
+
+# Sample data
+data = [
+   (1, "Alice", ["Math", "Science"]),
+   (2, "Bob", ["History", "Geography"]),
+   (3, "Charlie", ["English", "Math", "Physics"])
+]
+
+# Create a test DataFrame
+df = spark.createDataFrame(data, schema)
+
+# Show the Dataframe before flattening
+df.show()
+
+# Flatten the schema using Cobrix Scala 'SparkUtils.flattenSchema' method
+sc = spark.sparkContext
+java_import(sc._gateway.jvm, "za.co.absa.cobrix.spark.cobol.utils.SparkUtils")
+dfFlatJvm = spark._jvm.SparkUtils.flattenSchema(df._jdf, False)
+dfFlat = DataFrame(dfFlatJvm, SQLContext(sc))
+
+# Show the Dataframe after flattening
+dfFlat.show(truncate=False)
+dfFlat.printSchema()
+```
+
+The output looks like this:
+```
+# Before flattening
++---+-------+------------------------+
+|id |name   |subjects                |
++---+-------+------------------------+
+|1  |Alice  |[Math, Science]         |
+|2  |Bob    |[History, Geography]    |
+|3  |Charlie|[English, Math, Physics]|
++---+-------+------------------------+
+
+# After flattening
++---+-------+----------+----------+----------+
+|id |name   |subjects_0|subjects_1|subjects_2|
++---+-------+----------+----------+----------+
+|1  |Alice  |Math      |Science   |null      |
+|2  |Bob    |History   |Geography |null      |
+|3  |Charlie|English   |Math      |Physics   |
++---+-------+----------+----------+----------+
+```
+
 ## Summary of all available options
 
 ##### File reading options

cobol-parser/src/main/scala/za/co/absa/cobrix/cobol/reader/schema/CobolSchema.scala

@@ -137,4 +137,4 @@ object CobolSchema {
       case None => CodePage.getCodePageByName(codePageName)
     }
   }
-}
+}

cobol-parser/src/test/scala/za/co/absa/cobrix/cobol/parser/decoders/StringDecodersSpec.scala

@@ -123,8 +123,6 @@ class StringDecodersSpec extends AnyWordSpec {
 
         val actual = decodeEbcdicString(bytes, KeepAll, new CodePage500, improvedNullDetection = false)
 
-        println(actual)
-
         assert(actual == expected)
       }
 

spark-cobol/src/main/scala/za/co/absa/cobrix/spark/cobol/schema/CobolSchema.scala

@@ -341,4 +341,4 @@ object CobolSchema {
 
     CobolSchema.fromBaseReader(CobolReaderSchema.fromReaderParameters(copyBookContents, readerParameters))
   }
-}
+}

spark-cobol/src/main/scala/za/co/absa/cobrix/spark/cobol/source/DefaultSource.scala

@@ -112,4 +112,4 @@ class DefaultSource
       copybookContent, getReaderProperties(parameters, defaultHdfsBlockSize)
     )
   }
-}
+}

spark-cobol/src/test/scala/za/co/absa/cobrix/spark/cobol/CobolSchemaSpec.scala

@@ -16,7 +16,7 @@
 
 package za.co.absa.cobrix.spark.cobol
 
-import org.apache.spark.sql.types.{ArrayType, IntegerType, StringType, StructType}
+import org.apache.spark.sql.types.{ArrayType, IntegerType, LongType, StringType, StructType}
 import org.scalatest.wordspec.AnyWordSpec
 import org.slf4j.{Logger, LoggerFactory}
 import za.co.absa.cobrix.cobol.parser.CopybookParser
@@ -483,8 +483,6 @@ class CobolSchemaSpec extends AnyWordSpec with SimpleComparisonBase {
 
       val sparkSchema = cobolSchema.getSparkSchema
 
-      sparkSchema.printTreeString()
-
       assert(sparkSchema.fields.length == 3)
       assert(sparkSchema.fields.head.name == "HEADER")
       assert(sparkSchema.fields.head.dataType == StringType)
@@ -502,6 +500,54 @@ class CobolSchemaSpec extends AnyWordSpec with SimpleComparisonBase {
       assert(seg1.fields(2).name == "SEG3")
       assert(seg1.fields(2).dataType.isInstanceOf[ArrayType])
     }
+
+    "return a schema for a multi-segment copybook" in {
+      val copybook: String =
+        """       01  RECORD.
+          |         05  HEADER                PIC X(5).
+          |         05  SEGMENT-ID            PIC X(2).
+          |         05  SEG1.
+          |           10  FIELD1              PIC 9(7).
+          |         05  SEG2 REDEFINES SEG1.
+          |           10  FIELD3              PIC X(7).
+          |         05  SEG3 REDEFINES SEG1.
+          |           10  FIELD4              PIC S9(7).
+          |""".stripMargin
+
+      val cobolSchema = CobolSchema.fromSparkOptions(Seq(copybook),
+        Map(
+          "segment_field" -> "SEGMENT-ID",
+          "redefine-segment-id-map:0" -> "SEG1 => 01",
+          "redefine-segment-id-map:1" -> "SEG2 => 02",
+          "redefine-segment-id-map:2" -> "SEG3 => 03",
+          "segment_field" -> "SEGMENT-ID",
+          "segment_id_level0" -> "TEST",
+          "generate_record_id" -> "true"
+        )
+      )
+
+      val sparkSchema = cobolSchema.getSparkSchema
+
+      assert(sparkSchema.fields.length == 9)
+      assert(sparkSchema.fields.head.name == "File_Id")
+      assert(sparkSchema.fields.head.dataType == IntegerType)
+      assert(sparkSchema.fields(1).name == "Record_Id")
+      assert(sparkSchema.fields(1).dataType == LongType)
+      assert(sparkSchema.fields(2).name == "Record_Byte_Length")
+      assert(sparkSchema.fields(2).dataType == IntegerType)
+      assert(sparkSchema.fields(3).name == "Seg_Id0")
+      assert(sparkSchema.fields(3).dataType == StringType)
+      assert(sparkSchema.fields(4).name == "HEADER")
+      assert(sparkSchema.fields(4).dataType == StringType)
+      assert(sparkSchema.fields(5).name == "SEGMENT_ID")
+      assert(sparkSchema.fields(5).dataType == StringType)
+      assert(sparkSchema.fields(6).name == "SEG1")
+      assert(sparkSchema.fields(6).dataType.isInstanceOf[StructType])
+      assert(sparkSchema.fields(7).name == "SEG2")
+      assert(sparkSchema.fields(7).dataType.isInstanceOf[StructType])
+      assert(sparkSchema.fields(8).name == "SEG3")
+      assert(sparkSchema.fields(8).dataType.isInstanceOf[StructType])
+    }
   }
 
 }