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[SPARK-49082][SQL] Widening type promotions in AvroDeserializer #47582

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[SPARK-49082][SQL] Widening type promotions in AvroDeserializer #47582

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0e8042f
update
wayneguow Aug 2, 2024
418170e
update
wayneguow Aug 5, 2024
a7b7793
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wayneguow Aug 6, 2024
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9 changes: 9 additions & 0 deletions connector/avro/src/main/scala/org/apache/spark/sql/avro/AvroDeserializer.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -141,6 +141,12 @@ private[sql] class AvroDeserializer(
case (INT, IntegerType) => (updater, ordinal, value) =>
updater.setInt(ordinal, value.asInstanceOf[Int])

case (INT, LongType) => (updater, ordinal, value) =>
updater.setLong(ordinal, value.asInstanceOf[Int])

case (INT, DoubleType) => (updater, ordinal, value) =>
updater.setDouble(ordinal, value.asInstanceOf[Int])

case (INT, dt: DatetimeType)
if preventReadingIncorrectType && realDataType.isInstanceOf[YearMonthIntervalType] =>
throw QueryCompilationErrors.avroIncompatibleReadError(toFieldStr(avroPath),
Expand Down Expand Up @@ -194,6 +200,9 @@ private[sql] class AvroDeserializer(
case (FLOAT, FloatType) => (updater, ordinal, value) =>
updater.setFloat(ordinal, value.asInstanceOf[Float])

case (FLOAT, DoubleType) => (updater, ordinal, value) =>
updater.setDouble(ordinal, value.asInstanceOf[Float])

case (DOUBLE, DoubleType) => (updater, ordinal, value) =>
updater.setDouble(ordinal, value.asInstanceOf[Double])

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33 changes: 33 additions & 0 deletions connector/avro/src/test/scala/org/apache/spark/sql/avro/AvroSuite.scala
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Expand Up @@ -921,6 +921,39 @@ abstract class AvroSuite
}
}

test("SPARK-49082: Widening type promotions in AvroDeserializer") {
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@jackierwzhang jackierwzhang Aug 6, 2024

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Thanks for implementing this (I posted the original ticket :D)!

Is there a plan to convert Date -> TimestampNTZ (Parquet PR) and Int -> Decimal (Parquet PR)?

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@wayneguow wayneguow Aug 7, 2024

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Yes, I will seperate another two PR.

withTempPath { tempPath =>
// Int -> Long
val intPath = s"$tempPath/int_data"
val intDf = Seq(1, Int.MinValue, Int.MaxValue).toDF("col")
intDf.write.format("avro").save(intPath)
checkAnswer(
spark.read.schema("col Long").format("avro").load(intPath),
Seq(Row(1L), Row(-2147483648L), Row(2147483647L))
)

// Int -> Double
checkAnswer(
spark.read.schema("col Double").format("avro").load(intPath),
Seq(Row(1D), Row(-2147483648D), Row(2147483647D))
)

// Float -> Double
val floatPath = s"$tempPath/float_data"
val floatDf = Seq(1F,
Float.MinValue, Float.MinPositiveValue, Float.MaxValue,
Float.NaN, Float.NegativeInfinity, Float.PositiveInfinity
).toDF("col")
floatDf.write.format("avro").save(floatPath)
checkAnswer(
spark.read.schema("col Double").format("avro").load(floatPath),
Seq(Row(1D),
Row(-3.4028234663852886E38D), Row(1.401298464324817E-45D), Row(3.4028234663852886E38D),
Row(Double.NaN), Row(Double.NegativeInfinity), Row(Double.PositiveInfinity))
)
}
}

test("SPARK-43380: Fix Avro data type conversion" +
" of DayTimeIntervalType to avoid producing incorrect results") {
withTempPath { path =>
Expand Down