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[SPARK-32025][SQL] Csv schema inference problems with different types in the same column #28896

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[SPARK-32025][SQL] Csv schema inference problems with different types in the same column #28896

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55c013e
Fix csv type inference
planga82 Jun 22, 2020
7fbd442
Discard infer schema changes
planga82 Jun 23, 2020
ae64bb9
Fix text
planga82 Jun 23, 2020
c6bbc27
Check compatibility types
planga82 Jun 23, 2020
a3a4bfd
Move compatible type inside inferSchema
planga82 Jun 24, 2020
d167493
Pull request comments
planga82 Jun 24, 2020
4629bb5
Delete not used variable
planga82 Jun 24, 2020
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3 changes: 2 additions & 1 deletion sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/csv/CSVInferSchema.scala
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Expand Up @@ -102,7 +102,7 @@ class CSVInferSchema(val options: CSVOptions) extends Serializable {
if (field == null || field.isEmpty || field == options.nullValue) {
typeSoFar
} else {
typeSoFar match {
val typeElemInfer = typeSoFar match {
case NullType => tryParseInteger(field)
case IntegerType => tryParseInteger(field)
case LongType => tryParseLong(field)
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Expand All @@ -116,6 +116,7 @@ class CSVInferSchema(val options: CSVOptions) extends Serializable {
case other: DataType =>
throw new UnsupportedOperationException(s"Unexpected data type $other")
}
compatibleType(typeSoFar, typeElemInfer).getOrElse(StringType)
}
}

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10 changes: 5 additions & 5 deletions sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/csv/CSVInferSchemaSuite.scala
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Expand Up @@ -56,11 +56,11 @@ class CSVInferSchemaSuite extends SparkFunSuite with SQLHelper {
assert(inferSchema.inferField(IntegerType, "1.0") == DoubleType)
assert(inferSchema.inferField(DoubleType, null) == DoubleType)
assert(inferSchema.inferField(DoubleType, "test") == StringType)
assert(inferSchema.inferField(LongType, "2015-08-20 14:57:00") == TimestampType)
assert(inferSchema.inferField(DoubleType, "2015-08-20 15:57:00") == TimestampType)
assert(inferSchema.inferField(LongType, "True") == BooleanType)
assert(inferSchema.inferField(IntegerType, "FALSE") == BooleanType)
assert(inferSchema.inferField(TimestampType, "FALSE") == BooleanType)
assert(inferSchema.inferField(LongType, "2015-08-20 14:57:00") == StringType)
assert(inferSchema.inferField(DoubleType, "2015-08-20 15:57:00") == StringType)
assert(inferSchema.inferField(LongType, "True") == StringType)
assert(inferSchema.inferField(IntegerType, "FALSE") == StringType)
assert(inferSchema.inferField(TimestampType, "FALSE") == StringType)

val textValueOne = Long.MaxValue.toString + "0"
val decimalValueOne = new java.math.BigDecimal(textValueOne)
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4 changes: 4 additions & 0 deletions sql/core/src/test/resources/test-data/mixed-types1.csv
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@@ -0,0 +1,4 @@
col_mixed_types
2012
1997
True
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12 changes: 12 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala
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Expand Up @@ -67,6 +67,7 @@ abstract class CSVSuite extends QueryTest with SharedSparkSession with TestCsvDa
private val valueMalformedFile = "test-data/value-malformed.csv"
private val badAfterGoodFile = "test-data/bad_after_good.csv"
private val malformedRowFile = "test-data/malformedRow.csv"
private val mixedTypes = "test-data/mixed-types1.csv"

/** Verifies data and schema. */
private def verifyCars(
Expand Down Expand Up @@ -2341,6 +2342,17 @@ abstract class CSVSuite extends QueryTest with SharedSparkSession with TestCsvDa
checkAnswer(csv, Row(null))
}
}

test("SPARK-32025: infer the schema from mixed-type values") {
val data = spark
.read
.format("csv")
.option("header", "true")
.option("inferSchema", "true")
.load(testFile(mixedTypes))

assert(data.schema.fields.last == StructField("col_mixed_types", StringType, true))
}
}

class CSVv1Suite extends CSVSuite {
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