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Posted to reviews@spark.apache.org by "panbingkun (via GitHub)" <gi...@apache.org> on 2024/03/22 03:07:15 UTC
[PR] [WIP][SPARK-47497]SQL] Make `to_csv` support the output of `array/struct/map` as pretty strings [spark]
panbingkun opened a new pull request, #45657:
URL: https://github.com/apache/spark/pull/45657
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Re: [PR] [WIP][SPARK-47497]SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on PR #45657:
URL: https://github.com/apache/spark/pull/45657#issuecomment-2014452948
1.Why not use `ToPrettyString`?
Because in `ToPrettyString`, the data output logic for `DateType` is `different from` that of csv itself, eg:
- `ToPrettyString`
- `to_csv`
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Re: [PR] [WIP][SPARK-47497]SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #45657:
URL: https://github.com/apache/spark/pull/45657#discussion_r1535126352
##########
sql/core/src/test/scala/org/apache/spark/sql/CsvFunctionsSuite.scala:
##########
@@ -603,4 +605,118 @@ class CsvFunctionsSuite extends QueryTest with SharedSparkSession {
$"csv", schema_of_csv("1,2\n2"), Map.empty[String, String].asJava))
checkAnswer(actual, Row(Row(1, "2\n2")))
}
+
+ test("SPARK-47497: to_csv support the data of ArrayType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", Array(100L, 200L, null, 300L))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", ArrayType(LongType))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,\"[100, 200, null, 300]\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of MapType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice",
+ Map("math" -> 100L, "english" -> 200L, "science" -> null))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", MapType(StringType, LongType))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,\"{math -> 100, english -> 200, science -> null}\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of StructType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", Row(100L, 200L, null))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", StructType(Seq(
+ StructField("id1", LongType),
+ StructField("id2", LongType),
+ StructField("id3", LongType))))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,\"{100, 200, null}\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of BinaryType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", "a".getBytes(StandardCharsets.UTF_8))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("b", BinaryType)))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,[61]"))
+ }
+
+ test("SPARK-47497: to_csv can display VariantType data") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", new VariantVal(Array[Byte](1, 2, 3), Array[Byte](4, 5)))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("v", VariantType)))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,\"\""))
Review Comment:
Although this output seems a little weird, as `VariantVal` improves the method `toString` logic, it should be able to better display human-readable text.
https://github.com/apache/spark/blob/ca44489f458554714a620827b5fe2978a30db7f9/common/unsafe/src/main/java/org/apache/spark/unsafe/types/VariantVal.java#L101-L109
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Re: [PR] [SPARK-47497]SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #45657:
URL: https://github.com/apache/spark/pull/45657#discussion_r1535436451
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/csv/UnivocityGenerator.scala:
##########
@@ -64,33 +65,143 @@ class UnivocityGenerator(
private val nullAsQuotedEmptyString =
SQLConf.get.getConf(SQLConf.LEGACY_NULL_VALUE_WRITTEN_AS_QUOTED_EMPTY_STRING_CSV)
- @scala.annotation.tailrec
private def makeConverter(dataType: DataType): ValueConverter = dataType match {
+ case BinaryType =>
+ (getter, ordinal) => SparkStringUtils.getHexString(getter.getBinary(ordinal))
+
case DateType =>
- (row: InternalRow, ordinal: Int) => dateFormatter.format(row.getInt(ordinal))
+ (getter, ordinal) => dateFormatter.format(getter.getInt(ordinal))
case TimestampType =>
- (row: InternalRow, ordinal: Int) => timestampFormatter.format(row.getLong(ordinal))
+ (getter, ordinal) => timestampFormatter.format(getter.getLong(ordinal))
case TimestampNTZType =>
- (row: InternalRow, ordinal: Int) =>
- timestampNTZFormatter.format(DateTimeUtils.microsToLocalDateTime(row.getLong(ordinal)))
+ (getter, ordinal) =>
+ timestampNTZFormatter.format(DateTimeUtils.microsToLocalDateTime(getter.getLong(ordinal)))
case YearMonthIntervalType(start, end) =>
- (row: InternalRow, ordinal: Int) =>
+ (getter, ordinal) =>
IntervalUtils.toYearMonthIntervalString(
- row.getInt(ordinal), IntervalStringStyles.ANSI_STYLE, start, end)
+ getter.getInt(ordinal), IntervalStringStyles.ANSI_STYLE, start, end)
case DayTimeIntervalType(start, end) =>
- (row: InternalRow, ordinal: Int) =>
- IntervalUtils.toDayTimeIntervalString(
- row.getLong(ordinal), IntervalStringStyles.ANSI_STYLE, start, end)
+ (getter, ordinal) =>
+ IntervalUtils.toDayTimeIntervalString(
+ getter.getLong(ordinal), IntervalStringStyles.ANSI_STYLE, start, end)
case udt: UserDefinedType[_] => makeConverter(udt.sqlType)
+ case ArrayType(et, _) =>
+ val elementConverter = makeConverter(et)
+ (getter, ordinal) =>
+ val array = getter.getArray(ordinal)
+ val builder = new StringBuilder
+ builder.append("[")
+ if (array.numElements() > 0) {
+ if (array.isNullAt(0)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(elementConverter(array, 0))
+ }
+ var i = 1
+ while (i < array.numElements()) {
+ builder.append(", ")
+ if (array.isNullAt(i)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(elementConverter(array, i))
+ }
+ i += 1
+ }
+ }
+ builder.append("]")
+ builder.toString()
+
+ case MapType(kt, vt, _) =>
+ val keyConverter = makeConverter(kt)
+ val valueConverter = makeConverter(vt)
+ (getter, ordinal) =>
+ val map = getter.getMap(ordinal)
+ val builder = new StringBuilder
+ builder.append("{")
+ if (map.numElements() > 0) {
+ val keyArray = map.keyArray()
+ val valueArray = map.valueArray()
+ builder.append(keyConverter(keyArray, 0))
+ builder.append(" -> ")
+ if (valueArray.isNullAt(0)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(valueConverter(valueArray, 0))
+ }
+ var i = 1
+ while (i < map.numElements()) {
+ builder.append(", ")
+ builder.append(keyConverter(keyArray, i))
+ builder.append(" -> ")
+ if (valueArray.isNullAt(i)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(valueConverter(valueArray, i))
+ }
+ i += 1
+ }
+ }
+ builder.append("}")
+ builder.toString()
+
+ case StructType(fields) =>
+ val converters = fields.map(_.dataType).map(makeConverter)
+ (getter, ordinal) =>
+ val row = getter.getStruct(ordinal, fields.length)
+ val builder = new StringBuilder
+ builder.append("{")
+ if (row.numFields > 0) {
+ if (row.isNullAt(0)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(converters(0)(row, 0))
+ }
+ var i = 1
+ while (i < row.numFields) {
+ builder.append(", ")
+ if (row.isNullAt(i)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(converters(i)(row, i))
Review Comment:
Can we add a function for the common null handling code?
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Re: [PR] [SPARK-47497]SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #45657:
URL: https://github.com/apache/spark/pull/45657#discussion_r1535287917
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/csv/UnivocityGenerator.scala:
##########
@@ -36,9 +36,9 @@ class UnivocityGenerator(
writerSettings.setHeaders(schema.fieldNames: _*)
private val gen = new CsvWriter(writer, writerSettings)
- // A `ValueConverter` is responsible for converting a value of an `InternalRow` to `String`.
+ // A `ValueConverter` is responsible for converting a value of an `Any` to `String`.
// When the value is null, this converter should not be called.
- private type ValueConverter = (InternalRow, Int) => String
Review Comment:
Done.
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Re: [PR] [SPARK-47497]SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #45657:
URL: https://github.com/apache/spark/pull/45657#discussion_r1535478355
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/csv/UnivocityGenerator.scala:
##########
@@ -64,33 +65,143 @@ class UnivocityGenerator(
private val nullAsQuotedEmptyString =
SQLConf.get.getConf(SQLConf.LEGACY_NULL_VALUE_WRITTEN_AS_QUOTED_EMPTY_STRING_CSV)
- @scala.annotation.tailrec
private def makeConverter(dataType: DataType): ValueConverter = dataType match {
+ case BinaryType =>
+ (getter, ordinal) => SparkStringUtils.getHexString(getter.getBinary(ordinal))
+
case DateType =>
- (row: InternalRow, ordinal: Int) => dateFormatter.format(row.getInt(ordinal))
+ (getter, ordinal) => dateFormatter.format(getter.getInt(ordinal))
case TimestampType =>
- (row: InternalRow, ordinal: Int) => timestampFormatter.format(row.getLong(ordinal))
+ (getter, ordinal) => timestampFormatter.format(getter.getLong(ordinal))
case TimestampNTZType =>
- (row: InternalRow, ordinal: Int) =>
- timestampNTZFormatter.format(DateTimeUtils.microsToLocalDateTime(row.getLong(ordinal)))
+ (getter, ordinal) =>
+ timestampNTZFormatter.format(DateTimeUtils.microsToLocalDateTime(getter.getLong(ordinal)))
case YearMonthIntervalType(start, end) =>
- (row: InternalRow, ordinal: Int) =>
+ (getter, ordinal) =>
IntervalUtils.toYearMonthIntervalString(
- row.getInt(ordinal), IntervalStringStyles.ANSI_STYLE, start, end)
+ getter.getInt(ordinal), IntervalStringStyles.ANSI_STYLE, start, end)
case DayTimeIntervalType(start, end) =>
- (row: InternalRow, ordinal: Int) =>
- IntervalUtils.toDayTimeIntervalString(
- row.getLong(ordinal), IntervalStringStyles.ANSI_STYLE, start, end)
+ (getter, ordinal) =>
+ IntervalUtils.toDayTimeIntervalString(
+ getter.getLong(ordinal), IntervalStringStyles.ANSI_STYLE, start, end)
case udt: UserDefinedType[_] => makeConverter(udt.sqlType)
+ case ArrayType(et, _) =>
+ val elementConverter = makeConverter(et)
+ (getter, ordinal) =>
+ val array = getter.getArray(ordinal)
+ val builder = new StringBuilder
+ builder.append("[")
+ if (array.numElements() > 0) {
+ if (array.isNullAt(0)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(elementConverter(array, 0))
+ }
+ var i = 1
+ while (i < array.numElements()) {
+ builder.append(", ")
+ if (array.isNullAt(i)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(elementConverter(array, i))
+ }
+ i += 1
+ }
+ }
+ builder.append("]")
+ builder.toString()
+
+ case MapType(kt, vt, _) =>
+ val keyConverter = makeConverter(kt)
+ val valueConverter = makeConverter(vt)
+ (getter, ordinal) =>
+ val map = getter.getMap(ordinal)
+ val builder = new StringBuilder
+ builder.append("{")
+ if (map.numElements() > 0) {
+ val keyArray = map.keyArray()
+ val valueArray = map.valueArray()
+ builder.append(keyConverter(keyArray, 0))
+ builder.append(" -> ")
+ if (valueArray.isNullAt(0)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(valueConverter(valueArray, 0))
+ }
+ var i = 1
+ while (i < map.numElements()) {
+ builder.append(", ")
+ builder.append(keyConverter(keyArray, i))
+ builder.append(" -> ")
+ if (valueArray.isNullAt(i)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(valueConverter(valueArray, i))
+ }
+ i += 1
+ }
+ }
+ builder.append("}")
+ builder.toString()
+
+ case StructType(fields) =>
+ val converters = fields.map(_.dataType).map(makeConverter)
+ (getter, ordinal) =>
+ val row = getter.getStruct(ordinal, fields.length)
+ val builder = new StringBuilder
+ builder.append("{")
+ if (row.numFields > 0) {
+ if (row.isNullAt(0)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(converters(0)(row, 0))
+ }
+ var i = 1
+ while (i < row.numFields) {
+ builder.append(", ")
+ if (row.isNullAt(i)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(converters(i)(row, i))
Review Comment:
Okay
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Re: [PR] [SPARK-47497][SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #45657:
URL: https://github.com/apache/spark/pull/45657#discussion_r1537159668
##########
sql/core/src/test/scala/org/apache/spark/sql/CsvFunctionsSuite.scala:
##########
@@ -603,4 +605,167 @@ class CsvFunctionsSuite extends QueryTest with SharedSparkSession {
$"csv", schema_of_csv("1,2\n2"), Map.empty[String, String].asJava))
checkAnswer(actual, Row(Row(1, "2\n2")))
}
+
+ test("SPARK-47497: null value display when w or w/o options (nullValue)") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", null, "y")))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("x", StringType),
+ StructField("y", StringType)))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual1 = df.select(to_csv($"value"))
+ checkAnswer(actual1, Row("2,Alice,,y"))
+
+ val options = Map("nullValue" -> "-")
+ val actual2 = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual2, Row("2,Alice,-,y"))
+ }
+
+ test("SPARK-47497: to_csv support the data of ArrayType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", Array(100L, 200L, null, 300L))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", ArrayType(LongType))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual1 = df.select(to_csv($"value"))
+ checkAnswer(actual1, Row("2,Alice,\"[100, 200,, 300]\""))
+
+ val options = Map("nullValue" -> "-")
+ val actual2 = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual2, Row("2,Alice,\"[100, 200, -, 300]\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of MapType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice",
+ Map("math" -> 100L, "english" -> 200L, "science" -> null))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", MapType(StringType, LongType))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual1 = df.select(to_csv($"value"))
+ checkAnswer(actual1, Row("2,Alice,\"{math -> 100, english -> 200, science ->}\""))
+
+ val options = Map("nullValue" -> "-")
+ val actual2 = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual2, Row("2,Alice,\"{math -> 100, english -> 200, science -> -}\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of StructType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", Row(100L, 200L, null))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", StructType(Seq(
+ StructField("id1", LongType),
+ StructField("id2", LongType),
+ StructField("id3", LongType))))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual1 = df.select(to_csv($"value"))
+ checkAnswer(actual1, Row("2,Alice,\"{100, 200,}\""))
+
+ val options = Map("nullValue" -> "-")
+ val actual2 = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual2, Row("2,Alice,\"{100, 200, -}\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of BinaryType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", "a".getBytes(StandardCharsets.UTF_8))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("a", BinaryType)))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,[61]"))
+ }
+
+ test("SPARK-47497: to_csv can display NullType data") {
+ val df = Seq(Tuple1(Tuple1(null))).toDF("value")
+ val options = Map("nullValue" -> "-")
+ val actual = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual, Row("-"))
+ }
+
+ test("SPARK-47497: from_csv/to_csv does not support VariantType data") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", new VariantVal(Array[Byte](1, 2, 3), Array[Byte](4, 5)))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("v", VariantType)))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+
+ checkError(
+ exception = intercept[AnalysisException] {
+ df.select(to_csv($"value")).collect()
+ },
+ errorClass = "DATATYPE_MISMATCH.UNSUPPORTED_INPUT_TYPE",
+ parameters = Map(
+ "functionName" -> "`to_csv`",
+ "dataType" -> "\"STRUCT<age: BIGINT, name: STRING, v: VARIANT>\"",
+ "sqlExpr" -> "\"to_csv(value)\""),
+ context = ExpectedContext(fragment = "to_csv", getCurrentClassCallSitePattern)
+ )
+
+ checkError(
+ exception = intercept[SparkUnsupportedOperationException] {
+ df.select(from_csv(lit("data"), valueSchema, Map.empty[String, String])).collect()
+ },
+ errorClass = "UNSUPPORTED_DATATYPE",
+ parameters = Map("typeName" -> "\"VARIANT\"")
+ )
+ }
+
+ test("SPARK-47497: the input of to_csv must be StructType") {
Review Comment:
Let me add a UT for it.
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Re: [PR] [SPARK-47497]SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #45657:
URL: https://github.com/apache/spark/pull/45657#discussion_r1535135208
##########
sql/core/src/test/scala/org/apache/spark/sql/CsvFunctionsSuite.scala:
##########
@@ -603,4 +605,118 @@ class CsvFunctionsSuite extends QueryTest with SharedSparkSession {
$"csv", schema_of_csv("1,2\n2"), Map.empty[String, String].asJava))
checkAnswer(actual, Row(Row(1, "2\n2")))
}
+
+ test("SPARK-47497: to_csv support the data of ArrayType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", Array(100L, 200L, null, 300L))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", ArrayType(LongType))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,\"[100, 200, null, 300]\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of MapType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice",
+ Map("math" -> 100L, "english" -> 200L, "science" -> null))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", MapType(StringType, LongType))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,\"{math -> 100, english -> 200, science -> null}\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of StructType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", Row(100L, 200L, null))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", StructType(Seq(
+ StructField("id1", LongType),
+ StructField("id2", LongType),
+ StructField("id3", LongType))))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,\"{100, 200, null}\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of BinaryType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", "a".getBytes(StandardCharsets.UTF_8))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("b", BinaryType)))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,[61]"))
+ }
+
+ test("SPARK-47497: to_csv can display VariantType data") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", new VariantVal(Array[Byte](1, 2, 3), Array[Byte](4, 5)))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("v", VariantType)))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,\"\""))
Review Comment:
Okay
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Re: [PR] [SPARK-47497][SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "dongjoon-hyun (via GitHub)" <gi...@apache.org>.
dongjoon-hyun closed pull request #45657: [SPARK-47497][SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings
URL: https://github.com/apache/spark/pull/45657
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Re: [PR] [SPARK-47497]SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #45657:
URL: https://github.com/apache/spark/pull/45657#discussion_r1535132166
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/csv/UnivocityGenerator.scala:
##########
@@ -36,9 +36,9 @@ class UnivocityGenerator(
writerSettings.setHeaders(schema.fieldNames: _*)
private val gen = new CsvWriter(writer, writerSettings)
- // A `ValueConverter` is responsible for converting a value of an `InternalRow` to `String`.
+ // A `ValueConverter` is responsible for converting a value of an `Any` to `String`.
// When the value is null, this converter should not be called.
- private type ValueConverter = (InternalRow, Int) => String
Review Comment:
`(InternalRow, Int)` is more efficient than `Any`, as we can avoid boxing for primitive values.
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Re: [PR] [SPARK-47497]SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #45657:
URL: https://github.com/apache/spark/pull/45657#discussion_r1535384910
##########
python/pyspark/sql/functions/builtin.py:
##########
@@ -15591,12 +15591,12 @@ def to_csv(col: "ColumnOrName", options: Optional[Dict[str, str]] = None) -> Col
>>> from pyspark.sql import Row, functions as sf
>>> data = [(1, Row(age=2, name='Alice', scores=[100, 200, 300]))]
>>> df = spark.createDataFrame(data, ("key", "value"))
- >>> df.select(sf.to_csv(df.value)).show(truncate=False) # doctest: +SKIP
- +-----------------------+
- |to_csv(value) |
- +-----------------------+
- |2,Alice,"[100,200,300]"|
- +-----------------------+
+ >>> df.select(sf.to_csv(df.value)).show(truncate=False)
+ +-------------------------+
+ |to_csv(value) |
+ +-------------------------+
+ |2,Alice,"[100, 200, 300]"|
Review Comment:
Aligning with `ToPrettyString`, adding a `space` between array elements seems reasonable, eg:
<img width="687" alt="image" src="https://github.com/apache/spark/assets/15246973/0ab81ee7-7a34-426e-8cd7-ae8e995a70e0">
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Re: [PR] [SPARK-47497]SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #45657:
URL: https://github.com/apache/spark/pull/45657#discussion_r1535133053
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/csv/UnivocityGenerator.scala:
##########
@@ -36,9 +36,9 @@ class UnivocityGenerator(
writerSettings.setHeaders(schema.fieldNames: _*)
private val gen = new CsvWriter(writer, writerSettings)
- // A `ValueConverter` is responsible for converting a value of an `InternalRow` to `String`.
+ // A `ValueConverter` is responsible for converting a value of an `Any` to `String`.
// When the value is null, this converter should not be called.
- private type ValueConverter = (InternalRow, Int) => String
Review Comment:
I get that we want to recursively call the converter for array, then we can use `(SpecializedGetters, Int)`
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Re: [PR] [SPARK-47497]SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #45657:
URL: https://github.com/apache/spark/pull/45657#discussion_r1535547809
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/csv/UnivocityGenerator.scala:
##########
@@ -64,33 +65,143 @@ class UnivocityGenerator(
private val nullAsQuotedEmptyString =
SQLConf.get.getConf(SQLConf.LEGACY_NULL_VALUE_WRITTEN_AS_QUOTED_EMPTY_STRING_CSV)
- @scala.annotation.tailrec
private def makeConverter(dataType: DataType): ValueConverter = dataType match {
+ case BinaryType =>
+ (getter, ordinal) => SparkStringUtils.getHexString(getter.getBinary(ordinal))
+
case DateType =>
- (row: InternalRow, ordinal: Int) => dateFormatter.format(row.getInt(ordinal))
+ (getter, ordinal) => dateFormatter.format(getter.getInt(ordinal))
case TimestampType =>
- (row: InternalRow, ordinal: Int) => timestampFormatter.format(row.getLong(ordinal))
+ (getter, ordinal) => timestampFormatter.format(getter.getLong(ordinal))
case TimestampNTZType =>
- (row: InternalRow, ordinal: Int) =>
- timestampNTZFormatter.format(DateTimeUtils.microsToLocalDateTime(row.getLong(ordinal)))
+ (getter, ordinal) =>
+ timestampNTZFormatter.format(DateTimeUtils.microsToLocalDateTime(getter.getLong(ordinal)))
case YearMonthIntervalType(start, end) =>
- (row: InternalRow, ordinal: Int) =>
+ (getter, ordinal) =>
IntervalUtils.toYearMonthIntervalString(
- row.getInt(ordinal), IntervalStringStyles.ANSI_STYLE, start, end)
+ getter.getInt(ordinal), IntervalStringStyles.ANSI_STYLE, start, end)
case DayTimeIntervalType(start, end) =>
- (row: InternalRow, ordinal: Int) =>
- IntervalUtils.toDayTimeIntervalString(
- row.getLong(ordinal), IntervalStringStyles.ANSI_STYLE, start, end)
+ (getter, ordinal) =>
+ IntervalUtils.toDayTimeIntervalString(
+ getter.getLong(ordinal), IntervalStringStyles.ANSI_STYLE, start, end)
case udt: UserDefinedType[_] => makeConverter(udt.sqlType)
+ case ArrayType(et, _) =>
+ val elementConverter = makeConverter(et)
+ (getter, ordinal) =>
+ val array = getter.getArray(ordinal)
+ val builder = new StringBuilder
+ builder.append("[")
+ if (array.numElements() > 0) {
+ if (array.isNullAt(0)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(elementConverter(array, 0))
+ }
+ var i = 1
+ while (i < array.numElements()) {
+ builder.append(", ")
+ if (array.isNullAt(i)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(elementConverter(array, i))
+ }
+ i += 1
+ }
+ }
+ builder.append("]")
+ builder.toString()
+
+ case MapType(kt, vt, _) =>
+ val keyConverter = makeConverter(kt)
+ val valueConverter = makeConverter(vt)
+ (getter, ordinal) =>
+ val map = getter.getMap(ordinal)
+ val builder = new StringBuilder
+ builder.append("{")
+ if (map.numElements() > 0) {
+ val keyArray = map.keyArray()
+ val valueArray = map.valueArray()
+ builder.append(keyConverter(keyArray, 0))
+ builder.append(" -> ")
+ if (valueArray.isNullAt(0)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(valueConverter(valueArray, 0))
+ }
+ var i = 1
+ while (i < map.numElements()) {
+ builder.append(", ")
+ builder.append(keyConverter(keyArray, i))
+ builder.append(" -> ")
+ if (valueArray.isNullAt(i)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(valueConverter(valueArray, i))
+ }
+ i += 1
+ }
+ }
+ builder.append("}")
+ builder.toString()
+
+ case StructType(fields) =>
+ val converters = fields.map(_.dataType).map(makeConverter)
+ (getter, ordinal) =>
+ val row = getter.getStruct(ordinal, fields.length)
+ val builder = new StringBuilder
+ builder.append("{")
+ if (row.numFields > 0) {
+ if (row.isNullAt(0)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(converters(0)(row, 0))
+ }
+ var i = 1
+ while (i < row.numFields) {
+ builder.append(", ")
+ if (row.isNullAt(i)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(converters(i)(row, i))
Review Comment:
Please let me check a possible issue before merging.
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Re: [PR] [SPARK-47497]SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on PR #45657:
URL: https://github.com/apache/spark/pull/45657#issuecomment-2014467555
cc @cloud-fan
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Re: [PR] [SPARK-47497]SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #45657:
URL: https://github.com/apache/spark/pull/45657#discussion_r1535133900
##########
sql/core/src/test/scala/org/apache/spark/sql/CsvFunctionsSuite.scala:
##########
@@ -603,4 +605,118 @@ class CsvFunctionsSuite extends QueryTest with SharedSparkSession {
$"csv", schema_of_csv("1,2\n2"), Map.empty[String, String].asJava))
checkAnswer(actual, Row(Row(1, "2\n2")))
}
+
+ test("SPARK-47497: to_csv support the data of ArrayType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", Array(100L, 200L, null, 300L))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", ArrayType(LongType))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,\"[100, 200, null, 300]\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of MapType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice",
+ Map("math" -> 100L, "english" -> 200L, "science" -> null))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", MapType(StringType, LongType))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,\"{math -> 100, english -> 200, science -> null}\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of StructType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", Row(100L, 200L, null))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", StructType(Seq(
+ StructField("id1", LongType),
+ StructField("id2", LongType),
+ StructField("id3", LongType))))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,\"{100, 200, null}\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of BinaryType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", "a".getBytes(StandardCharsets.UTF_8))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("b", BinaryType)))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,[61]"))
+ }
+
+ test("SPARK-47497: to_csv can display VariantType data") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", new VariantVal(Array[Byte](1, 2, 3), Array[Byte](4, 5)))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("v", VariantType)))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,\"\""))
Review Comment:
I'm fine to ban variant type in `to_csv`, as it's new.
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Re: [PR] [SPARK-47497][SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #45657:
URL: https://github.com/apache/spark/pull/45657#discussion_r1537145637
##########
sql/core/src/test/scala/org/apache/spark/sql/CsvFunctionsSuite.scala:
##########
@@ -603,4 +605,167 @@ class CsvFunctionsSuite extends QueryTest with SharedSparkSession {
$"csv", schema_of_csv("1,2\n2"), Map.empty[String, String].asJava))
checkAnswer(actual, Row(Row(1, "2\n2")))
}
+
+ test("SPARK-47497: null value display when w or w/o options (nullValue)") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", null, "y")))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("x", StringType),
+ StructField("y", StringType)))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual1 = df.select(to_csv($"value"))
+ checkAnswer(actual1, Row("2,Alice,,y"))
+
+ val options = Map("nullValue" -> "-")
+ val actual2 = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual2, Row("2,Alice,-,y"))
+ }
+
+ test("SPARK-47497: to_csv support the data of ArrayType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", Array(100L, 200L, null, 300L))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", ArrayType(LongType))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual1 = df.select(to_csv($"value"))
+ checkAnswer(actual1, Row("2,Alice,\"[100, 200,, 300]\""))
+
+ val options = Map("nullValue" -> "-")
+ val actual2 = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual2, Row("2,Alice,\"[100, 200, -, 300]\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of MapType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice",
+ Map("math" -> 100L, "english" -> 200L, "science" -> null))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", MapType(StringType, LongType))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual1 = df.select(to_csv($"value"))
+ checkAnswer(actual1, Row("2,Alice,\"{math -> 100, english -> 200, science ->}\""))
+
+ val options = Map("nullValue" -> "-")
+ val actual2 = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual2, Row("2,Alice,\"{math -> 100, english -> 200, science -> -}\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of StructType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", Row(100L, 200L, null))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", StructType(Seq(
+ StructField("id1", LongType),
+ StructField("id2", LongType),
+ StructField("id3", LongType))))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual1 = df.select(to_csv($"value"))
+ checkAnswer(actual1, Row("2,Alice,\"{100, 200,}\""))
+
+ val options = Map("nullValue" -> "-")
+ val actual2 = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual2, Row("2,Alice,\"{100, 200, -}\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of BinaryType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", "a".getBytes(StandardCharsets.UTF_8))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("a", BinaryType)))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,[61]"))
+ }
+
+ test("SPARK-47497: to_csv can display NullType data") {
+ val df = Seq(Tuple1(Tuple1(null))).toDF("value")
+ val options = Map("nullValue" -> "-")
+ val actual = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual, Row("-"))
+ }
+
+ test("SPARK-47497: from_csv/to_csv does not support VariantType data") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", new VariantVal(Array[Byte](1, 2, 3), Array[Byte](4, 5)))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("v", VariantType)))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+
+ checkError(
+ exception = intercept[AnalysisException] {
+ df.select(to_csv($"value")).collect()
+ },
+ errorClass = "DATATYPE_MISMATCH.UNSUPPORTED_INPUT_TYPE",
+ parameters = Map(
+ "functionName" -> "`to_csv`",
+ "dataType" -> "\"STRUCT<age: BIGINT, name: STRING, v: VARIANT>\"",
+ "sqlExpr" -> "\"to_csv(value)\""),
+ context = ExpectedContext(fragment = "to_csv", getCurrentClassCallSitePattern)
+ )
+
+ checkError(
+ exception = intercept[SparkUnsupportedOperationException] {
+ df.select(from_csv(lit("data"), valueSchema, Map.empty[String, String])).collect()
+ },
+ errorClass = "UNSUPPORTED_DATATYPE",
+ parameters = Map("typeName" -> "\"VARIANT\"")
+ )
+ }
+
+ test("SPARK-47497: the input of to_csv must be StructType") {
Review Comment:
What if the input is a nested structure? For example, the item of the Array is a Map, does it remain consistent with what we've seen before?
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Re: [PR] [SPARK-47497][SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #45657:
URL: https://github.com/apache/spark/pull/45657#discussion_r1537151035
##########
sql/core/src/test/scala/org/apache/spark/sql/CsvFunctionsSuite.scala:
##########
@@ -603,4 +605,167 @@ class CsvFunctionsSuite extends QueryTest with SharedSparkSession {
$"csv", schema_of_csv("1,2\n2"), Map.empty[String, String].asJava))
checkAnswer(actual, Row(Row(1, "2\n2")))
}
+
+ test("SPARK-47497: null value display when w or w/o options (nullValue)") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", null, "y")))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("x", StringType),
+ StructField("y", StringType)))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual1 = df.select(to_csv($"value"))
+ checkAnswer(actual1, Row("2,Alice,,y"))
+
+ val options = Map("nullValue" -> "-")
+ val actual2 = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual2, Row("2,Alice,-,y"))
+ }
+
+ test("SPARK-47497: to_csv support the data of ArrayType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", Array(100L, 200L, null, 300L))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", ArrayType(LongType))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual1 = df.select(to_csv($"value"))
+ checkAnswer(actual1, Row("2,Alice,\"[100, 200,, 300]\""))
+
+ val options = Map("nullValue" -> "-")
+ val actual2 = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual2, Row("2,Alice,\"[100, 200, -, 300]\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of MapType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice",
+ Map("math" -> 100L, "english" -> 200L, "science" -> null))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", MapType(StringType, LongType))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual1 = df.select(to_csv($"value"))
+ checkAnswer(actual1, Row("2,Alice,\"{math -> 100, english -> 200, science ->}\""))
+
+ val options = Map("nullValue" -> "-")
+ val actual2 = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual2, Row("2,Alice,\"{math -> 100, english -> 200, science -> -}\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of StructType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", Row(100L, 200L, null))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", StructType(Seq(
+ StructField("id1", LongType),
+ StructField("id2", LongType),
+ StructField("id3", LongType))))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual1 = df.select(to_csv($"value"))
+ checkAnswer(actual1, Row("2,Alice,\"{100, 200,}\""))
+
+ val options = Map("nullValue" -> "-")
+ val actual2 = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual2, Row("2,Alice,\"{100, 200, -}\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of BinaryType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", "a".getBytes(StandardCharsets.UTF_8))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("a", BinaryType)))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,[61]"))
+ }
+
+ test("SPARK-47497: to_csv can display NullType data") {
+ val df = Seq(Tuple1(Tuple1(null))).toDF("value")
+ val options = Map("nullValue" -> "-")
+ val actual = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual, Row("-"))
+ }
+
+ test("SPARK-47497: from_csv/to_csv does not support VariantType data") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", new VariantVal(Array[Byte](1, 2, 3), Array[Byte](4, 5)))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("v", VariantType)))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+
+ checkError(
+ exception = intercept[AnalysisException] {
+ df.select(to_csv($"value")).collect()
+ },
+ errorClass = "DATATYPE_MISMATCH.UNSUPPORTED_INPUT_TYPE",
+ parameters = Map(
+ "functionName" -> "`to_csv`",
+ "dataType" -> "\"STRUCT<age: BIGINT, name: STRING, v: VARIANT>\"",
+ "sqlExpr" -> "\"to_csv(value)\""),
+ context = ExpectedContext(fragment = "to_csv", getCurrentClassCallSitePattern)
+ )
+
+ checkError(
+ exception = intercept[SparkUnsupportedOperationException] {
+ df.select(from_csv(lit("data"), valueSchema, Map.empty[String, String])).collect()
+ },
+ errorClass = "UNSUPPORTED_DATATYPE",
+ parameters = Map("typeName" -> "\"VARIANT\"")
+ )
+ }
+
+ test("SPARK-47497: the input of to_csv must be StructType") {
Review Comment:
If it's already supported, should we add some corresponding test cases?
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Re: [PR] [SPARK-47497][SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #45657:
URL: https://github.com/apache/spark/pull/45657#discussion_r1537172409
##########
sql/core/src/test/scala/org/apache/spark/sql/CsvFunctionsSuite.scala:
##########
@@ -603,4 +605,167 @@ class CsvFunctionsSuite extends QueryTest with SharedSparkSession {
$"csv", schema_of_csv("1,2\n2"), Map.empty[String, String].asJava))
checkAnswer(actual, Row(Row(1, "2\n2")))
}
+
+ test("SPARK-47497: null value display when w or w/o options (nullValue)") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", null, "y")))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("x", StringType),
+ StructField("y", StringType)))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual1 = df.select(to_csv($"value"))
+ checkAnswer(actual1, Row("2,Alice,,y"))
+
+ val options = Map("nullValue" -> "-")
+ val actual2 = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual2, Row("2,Alice,-,y"))
+ }
+
+ test("SPARK-47497: to_csv support the data of ArrayType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", Array(100L, 200L, null, 300L))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", ArrayType(LongType))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual1 = df.select(to_csv($"value"))
+ checkAnswer(actual1, Row("2,Alice,\"[100, 200,, 300]\""))
+
+ val options = Map("nullValue" -> "-")
+ val actual2 = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual2, Row("2,Alice,\"[100, 200, -, 300]\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of MapType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice",
+ Map("math" -> 100L, "english" -> 200L, "science" -> null))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", MapType(StringType, LongType))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual1 = df.select(to_csv($"value"))
+ checkAnswer(actual1, Row("2,Alice,\"{math -> 100, english -> 200, science ->}\""))
+
+ val options = Map("nullValue" -> "-")
+ val actual2 = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual2, Row("2,Alice,\"{math -> 100, english -> 200, science -> -}\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of StructType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", Row(100L, 200L, null))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("scores", StructType(Seq(
+ StructField("id1", LongType),
+ StructField("id2", LongType),
+ StructField("id3", LongType))))))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual1 = df.select(to_csv($"value"))
+ checkAnswer(actual1, Row("2,Alice,\"{100, 200,}\""))
+
+ val options = Map("nullValue" -> "-")
+ val actual2 = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual2, Row("2,Alice,\"{100, 200, -}\""))
+ }
+
+ test("SPARK-47497: to_csv support the data of BinaryType as pretty strings") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", "a".getBytes(StandardCharsets.UTF_8))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("a", BinaryType)))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+ val actual = df.select(to_csv($"value"))
+ checkAnswer(actual, Row("2,Alice,[61]"))
+ }
+
+ test("SPARK-47497: to_csv can display NullType data") {
+ val df = Seq(Tuple1(Tuple1(null))).toDF("value")
+ val options = Map("nullValue" -> "-")
+ val actual = df.select(to_csv($"value", options.asJava))
+ checkAnswer(actual, Row("-"))
+ }
+
+ test("SPARK-47497: from_csv/to_csv does not support VariantType data") {
+ val rows = new java.util.ArrayList[Row]()
+ rows.add(Row(1L, Row(2L, "Alice", new VariantVal(Array[Byte](1, 2, 3), Array[Byte](4, 5)))))
+
+ val valueSchema = StructType(Seq(
+ StructField("age", LongType),
+ StructField("name", StringType),
+ StructField("v", VariantType)))
+ val schema = StructType(Seq(
+ StructField("key", LongType),
+ StructField("value", valueSchema)))
+
+ val df = spark.createDataFrame(rows, schema)
+
+ checkError(
+ exception = intercept[AnalysisException] {
+ df.select(to_csv($"value")).collect()
+ },
+ errorClass = "DATATYPE_MISMATCH.UNSUPPORTED_INPUT_TYPE",
+ parameters = Map(
+ "functionName" -> "`to_csv`",
+ "dataType" -> "\"STRUCT<age: BIGINT, name: STRING, v: VARIANT>\"",
+ "sqlExpr" -> "\"to_csv(value)\""),
+ context = ExpectedContext(fragment = "to_csv", getCurrentClassCallSitePattern)
+ )
+
+ checkError(
+ exception = intercept[SparkUnsupportedOperationException] {
+ df.select(from_csv(lit("data"), valueSchema, Map.empty[String, String])).collect()
+ },
+ errorClass = "UNSUPPORTED_DATATYPE",
+ parameters = Map("typeName" -> "\"VARIANT\"")
+ )
+ }
+
+ test("SPARK-47497: the input of to_csv must be StructType") {
Review Comment:
@LuciferYang
The related UT here: https://github.com/apache/spark/pull/45692
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Re: [PR] [SPARK-47497]SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "panbingkun (via GitHub)" <gi...@apache.org>.
panbingkun commented on code in PR #45657:
URL: https://github.com/apache/spark/pull/45657#discussion_r1535478355
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/csv/UnivocityGenerator.scala:
##########
@@ -64,33 +65,143 @@ class UnivocityGenerator(
private val nullAsQuotedEmptyString =
SQLConf.get.getConf(SQLConf.LEGACY_NULL_VALUE_WRITTEN_AS_QUOTED_EMPTY_STRING_CSV)
- @scala.annotation.tailrec
private def makeConverter(dataType: DataType): ValueConverter = dataType match {
+ case BinaryType =>
+ (getter, ordinal) => SparkStringUtils.getHexString(getter.getBinary(ordinal))
+
case DateType =>
- (row: InternalRow, ordinal: Int) => dateFormatter.format(row.getInt(ordinal))
+ (getter, ordinal) => dateFormatter.format(getter.getInt(ordinal))
case TimestampType =>
- (row: InternalRow, ordinal: Int) => timestampFormatter.format(row.getLong(ordinal))
+ (getter, ordinal) => timestampFormatter.format(getter.getLong(ordinal))
case TimestampNTZType =>
- (row: InternalRow, ordinal: Int) =>
- timestampNTZFormatter.format(DateTimeUtils.microsToLocalDateTime(row.getLong(ordinal)))
+ (getter, ordinal) =>
+ timestampNTZFormatter.format(DateTimeUtils.microsToLocalDateTime(getter.getLong(ordinal)))
case YearMonthIntervalType(start, end) =>
- (row: InternalRow, ordinal: Int) =>
+ (getter, ordinal) =>
IntervalUtils.toYearMonthIntervalString(
- row.getInt(ordinal), IntervalStringStyles.ANSI_STYLE, start, end)
+ getter.getInt(ordinal), IntervalStringStyles.ANSI_STYLE, start, end)
case DayTimeIntervalType(start, end) =>
- (row: InternalRow, ordinal: Int) =>
- IntervalUtils.toDayTimeIntervalString(
- row.getLong(ordinal), IntervalStringStyles.ANSI_STYLE, start, end)
+ (getter, ordinal) =>
+ IntervalUtils.toDayTimeIntervalString(
+ getter.getLong(ordinal), IntervalStringStyles.ANSI_STYLE, start, end)
case udt: UserDefinedType[_] => makeConverter(udt.sqlType)
+ case ArrayType(et, _) =>
+ val elementConverter = makeConverter(et)
+ (getter, ordinal) =>
+ val array = getter.getArray(ordinal)
+ val builder = new StringBuilder
+ builder.append("[")
+ if (array.numElements() > 0) {
+ if (array.isNullAt(0)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(elementConverter(array, 0))
+ }
+ var i = 1
+ while (i < array.numElements()) {
+ builder.append(", ")
+ if (array.isNullAt(i)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(elementConverter(array, i))
+ }
+ i += 1
+ }
+ }
+ builder.append("]")
+ builder.toString()
+
+ case MapType(kt, vt, _) =>
+ val keyConverter = makeConverter(kt)
+ val valueConverter = makeConverter(vt)
+ (getter, ordinal) =>
+ val map = getter.getMap(ordinal)
+ val builder = new StringBuilder
+ builder.append("{")
+ if (map.numElements() > 0) {
+ val keyArray = map.keyArray()
+ val valueArray = map.valueArray()
+ builder.append(keyConverter(keyArray, 0))
+ builder.append(" -> ")
+ if (valueArray.isNullAt(0)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(valueConverter(valueArray, 0))
+ }
+ var i = 1
+ while (i < map.numElements()) {
+ builder.append(", ")
+ builder.append(keyConverter(keyArray, i))
+ builder.append(" -> ")
+ if (valueArray.isNullAt(i)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(valueConverter(valueArray, i))
+ }
+ i += 1
+ }
+ }
+ builder.append("}")
+ builder.toString()
+
+ case StructType(fields) =>
+ val converters = fields.map(_.dataType).map(makeConverter)
+ (getter, ordinal) =>
+ val row = getter.getStruct(ordinal, fields.length)
+ val builder = new StringBuilder
+ builder.append("{")
+ if (row.numFields > 0) {
+ if (row.isNullAt(0)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(converters(0)(row, 0))
+ }
+ var i = 1
+ while (i < row.numFields) {
+ builder.append(", ")
+ if (row.isNullAt(i)) {
+ if (nullAsQuotedEmptyString) {
+ builder.append(options.nullValue)
+ } else {
+ builder.append(null.asInstanceOf[String])
+ }
+ } else {
+ builder.append(converters(i)(row, i))
Review Comment:
Okay, I found a bug in the original (Before applying `this PR`) `to_csv`, as follows:
<img width="985" alt="image" src="https://github.com/apache/spark/assets/15246973/7491d868-3236-4518-8b27-7aed769d26e4">
https://github.com/apache/spark/blob/227a50a1766ac1476b0031e1c60d2604eccdb9a7/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/csv/UnivocityGenerator.scala#L100-L104
<img width="538" alt="image" src="https://github.com/apache/spark/assets/15246973/e576e7e0-a03b-497f-929a-cf8b1cdcfcc8">
when `row.isNullAt(i)` && `nullAsQuotedEmptyString == false`,
`values(i)` is not assigned, so it is the default value null, which seems incorrect?
<img width="859" alt="image" src="https://github.com/apache/spark/assets/15246973/68d263e9-934d-436f-b79d-1a04b6cd5b76">
According to this interpretation, we should assign value `""`.
@cloud-fan Should we fix it in this PR or open a separate PR to handle it?
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Re: [PR] [SPARK-47497][SQL] Make `to_csv` support the output of `array/struct/map/binary` as pretty strings [spark]
Posted by "dongjoon-hyun (via GitHub)" <gi...@apache.org>.
dongjoon-hyun commented on PR #45657:
URL: https://github.com/apache/spark/pull/45657#issuecomment-2016718242
Merged to master for Apache Spark 4.0.0.
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