Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/14102#discussion_r74193172
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonParser.scala
---
@@ -35,184 +34,337 @@ import org.apache.spark.util.Utils
private[json] class SparkSQLJsonProcessingException(msg: String) extends
RuntimeException(msg)
-object JacksonParser extends Logging {
+class JacksonParser(
+ schema: StructType,
+ columnNameOfCorruptRecord: String,
+ options: JSONOptions) extends Logging {
- def parse(
- input: RDD[String],
- schema: StructType,
- columnNameOfCorruptRecords: String,
- configOptions: JSONOptions): RDD[InternalRow] = {
+ import com.fasterxml.jackson.core.JsonToken._
- input.mapPartitions { iter =>
- parseJson(iter, schema, columnNameOfCorruptRecords, configOptions)
+ // A `ValueConverter` is responsible for converting a value from
`JsonParser`
+ // to a value in a field for `InternalRow`.
+ private type ValueConverter = (JsonParser) => Any
+
+ // `ValueConverter`s for the root schema for all fields in the schema
+ private val rootConverter: ValueConverter = makeRootConverter(schema)
+
+ private val factory = new JsonFactory()
+ options.setJacksonOptions(factory)
+
+ private def failedConversion(
+ parser: JsonParser,
+ dataType: DataType): Any = parser.getCurrentToken match {
+ case _ if parser.getTextLength < 1 =>
+ // If conversion is failed, this produces `null` rather than
+ // returning empty string. This will protect the mismatch of types.
+ null
+
+ case token =>
+ // We cannot parse this token based on the given data type. So, we
throw a
+ // SparkSQLJsonProcessingException and this exception will be caught
by
+ // parseJson method.
+ throw new SparkSQLJsonProcessingException(
+ s"Failed to parse a value for data type $dataType (current token:
$token).")
+ }
+
+ private def failedRecord(record: String): Seq[InternalRow] = {
+ // create a row even if no corrupt record column is present
+ if (options.failFast) {
+ throw new RuntimeException(s"Malformed line in FAILFAST mode:
$record")
+ }
+ if (options.dropMalformed) {
+ logWarning(s"Dropping malformed line: $record")
+ Nil
+ } else {
+ val row = new GenericMutableRow(schema.length)
+ for (corruptIndex <-
schema.getFieldIndex(columnNameOfCorruptRecord)) {
+ require(schema(corruptIndex).dataType == StringType)
+ row.update(corruptIndex, UTF8String.fromString(record))
+ }
+ Seq(row)
}
}
/**
- * Parse the current token (and related children) according to a desired
schema
- * This is a wrapper for the method `convertField()` to handle a row
wrapped
- * with an array.
+ * Create a converter which converts the JSON documents held by the
`JsonParser`
+ * to a value according to a desired schema. This is a wrapper for the
method
+ * `makeConverter()` to handle a row wrapped with an array.
*/
- def convertRootField(
- factory: JsonFactory,
- parser: JsonParser,
- schema: DataType): Any = {
- import com.fasterxml.jackson.core.JsonToken._
- (parser.getCurrentToken, schema) match {
- case (START_ARRAY, st: StructType) =>
- // SPARK-3308: support reading top level JSON arrays and take
every element
- // in such an array as a row
- convertArray(factory, parser, st)
-
- case (START_OBJECT, ArrayType(st, _)) =>
+ def makeRootConverter(dataType: DataType): ValueConverter = dataType
match {
+ case st: StructType =>
+ val elementConverter = makeConverter(st)
+ val fieldConverters = st.map(_.dataType).map(makeConverter)
+ (parser: JsonParser) => parser.getCurrentToken match {
+ case START_OBJECT => convertObject(parser, st, fieldConverters)
+ // SPARK-3308: support reading top level JSON arrays and take
every element
+ // in such an array as a row
+ //
+ // For example, we support, the JSON data as below:
+ //
+ // [{"a":"str_a_1"}]
+ // [{"a":"str_a_2"}, {"b":"str_b_3"}]
+ //
+ // resulting in:
+ //
+ // List([str_a_1,null])
+ // List([str_a_2,null], [null,str_b_3])
+ //
+ case START_ARRAY => convertArray(parser, elementConverter)
+ case _ => failedConversion(parser, st)
+ }
+
+ case ArrayType(st: StructType, _) =>
+ val elementConverter = makeConverter(st)
+ val fieldConverters = st.map(_.dataType).map(makeConverter)
+ (parser: JsonParser) => parser.getCurrentToken match {
// the business end of SPARK-3308:
- // when an object is found but an array is requested just wrap it
in a list
- convertField(factory, parser, st) :: Nil
+ // when an object is found but an array is requested just wrap it
in a list.
+ // This is being wrapped in `JacksonParser.parse`.
+ case START_OBJECT => convertObject(parser, st, fieldConverters)
+ case START_ARRAY => convertArray(parser, elementConverter)
+ case _ => failedConversion(parser, st)
+ }
- case _ =>
- convertField(factory, parser, schema)
- }
+ case _ => makeConverter(dataType)
}
- private def convertField(
- factory: JsonFactory,
- parser: JsonParser,
- schema: DataType): Any = {
- import com.fasterxml.jackson.core.JsonToken._
- (parser.getCurrentToken, schema) match {
- case (null | VALUE_NULL, _) =>
- null
-
- case (FIELD_NAME, _) =>
- parser.nextToken()
- convertField(factory, parser, schema)
-
- case (VALUE_STRING, StringType) =>
- UTF8String.fromString(parser.getText)
-
- case (VALUE_STRING, _) if parser.getTextLength < 1 =>
- // guard the non string type
- null
-
- case (VALUE_STRING, BinaryType) =>
- parser.getBinaryValue
-
- case (VALUE_STRING, DateType) =>
- val stringValue = parser.getText
- if (stringValue.contains("-")) {
- // The format of this string will probably be "yyyy-mm-dd".
-
DateTimeUtils.millisToDays(DateTimeUtils.stringToTime(parser.getText).getTime)
- } else {
- // In Spark 1.5.0, we store the data as number of days since
epoch in string.
- // So, we just convert it to Int.
- stringValue.toInt
+ /**
+ * Create a converter which converts the JSON documents held by the
`JsonParser`
+ * to a value according to a desired schema.
+ */
+ private def makeConverter(dataType: DataType): ValueConverter = dataType
match {
+ case BooleanType =>
+ (parser: JsonParser) => convertField(parser) {
+ parser.getCurrentToken match {
+ case VALUE_TRUE => true
+ case VALUE_FALSE => false
+ case _ => failedConversion(parser, dataType)
}
+ }
- case (VALUE_STRING, TimestampType) =>
- // This one will lose microseconds parts.
- // See https://issues.apache.org/jira/browse/SPARK-10681.
- DateTimeUtils.stringToTime(parser.getText).getTime * 1000L
+ case ByteType =>
+ (parser: JsonParser) => convertField(parser) {
+ parser.getCurrentToken match {
+ case VALUE_NUMBER_INT => parser.getByteValue
+ case _ => failedConversion(parser, dataType)
+ }
+ }
- case (VALUE_NUMBER_INT, TimestampType) =>
- parser.getLongValue * 1000000L
+ case ShortType =>
+ (parser: JsonParser) => convertField(parser) {
+ parser.getCurrentToken match {
+ case VALUE_NUMBER_INT => parser.getShortValue
+ case _ => failedConversion(parser, dataType)
+ }
+ }
- case (_, StringType) =>
- val writer = new ByteArrayOutputStream()
- Utils.tryWithResource(factory.createGenerator(writer,
JsonEncoding.UTF8)) {
- generator => generator.copyCurrentStructure(parser)
+ case IntegerType =>
+ (parser: JsonParser) => convertField(parser) {
+ parser.getCurrentToken match {
+ case VALUE_NUMBER_INT => parser.getIntValue
+ case _ => failedConversion(parser, dataType)
}
- UTF8String.fromBytes(writer.toByteArray)
-
- case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, FloatType) =>
- parser.getFloatValue
-
- case (VALUE_STRING, FloatType) =>
- // Special case handling for NaN and Infinity.
- val value = parser.getText
- val lowerCaseValue = value.toLowerCase()
- if (lowerCaseValue.equals("nan") ||
- lowerCaseValue.equals("infinity") ||
- lowerCaseValue.equals("-infinity") ||
- lowerCaseValue.equals("inf") ||
- lowerCaseValue.equals("-inf")) {
- value.toFloat
- } else {
- throw new SparkSQLJsonProcessingException(s"Cannot parse $value
as FloatType.")
+ }
+
+ case LongType =>
+ (parser: JsonParser) => convertField(parser) {
+ parser.getCurrentToken match {
+ case VALUE_NUMBER_INT => parser.getLongValue
+ case _ => failedConversion(parser, dataType)
}
+ }
- case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, DoubleType) =>
- parser.getDoubleValue
-
- case (VALUE_STRING, DoubleType) =>
- // Special case handling for NaN and Infinity.
- val value = parser.getText
- val lowerCaseValue = value.toLowerCase()
- if (lowerCaseValue.equals("nan") ||
- lowerCaseValue.equals("infinity") ||
- lowerCaseValue.equals("-infinity") ||
- lowerCaseValue.equals("inf") ||
- lowerCaseValue.equals("-inf")) {
- value.toDouble
- } else {
- throw new SparkSQLJsonProcessingException(s"Cannot parse $value
as DoubleType.")
+ case FloatType =>
+ (parser: JsonParser) => convertField(parser) {
+ parser.getCurrentToken match {
+ case VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT =>
+ parser.getFloatValue
+
+ case VALUE_STRING =>
+ // Special case handling for NaN and Infinity.
+ val value = parser.getText
+ val lowerCaseValue = value.toLowerCase
+ if (lowerCaseValue.equals("nan") ||
+ lowerCaseValue.equals("infinity") ||
+ lowerCaseValue.equals("-infinity") ||
+ lowerCaseValue.equals("inf") ||
+ lowerCaseValue.equals("-inf")) {
+ value.toFloat
+ } else {
+ throw new SparkSQLJsonProcessingException(s"Cannot parse
$value as FloatType.")
+ }
+
+ case _ => failedConversion(parser, dataType)
}
+ }
- case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, dt: DecimalType) =>
- Decimal(parser.getDecimalValue, dt.precision, dt.scale)
+ case DoubleType =>
+ (parser: JsonParser) => convertField(parser) {
+ parser.getCurrentToken match {
+ case VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT =>
+ parser.getDoubleValue
+
+ case VALUE_STRING =>
+ // Special case handling for NaN and Infinity.
+ val value = parser.getText
+ val lowerCaseValue = value.toLowerCase
+ if (lowerCaseValue.equals("nan") ||
+ lowerCaseValue.equals("infinity") ||
+ lowerCaseValue.equals("-infinity") ||
+ lowerCaseValue.equals("inf") ||
+ lowerCaseValue.equals("-inf")) {
+ value.toDouble
+ } else {
+ throw new SparkSQLJsonProcessingException(s"Cannot parse
$value as DoubleType.")
+ }
- case (VALUE_NUMBER_INT, ByteType) =>
- parser.getByteValue
+ case _ => failedConversion(parser, dataType)
+ }
+ }
- case (VALUE_NUMBER_INT, ShortType) =>
- parser.getShortValue
+ case StringType =>
+ (parser: JsonParser) => convertField(parser) {
+ parser.getCurrentToken match {
+ case VALUE_STRING =>
+ UTF8String.fromString(parser.getText)
- case (VALUE_NUMBER_INT, IntegerType) =>
- parser.getIntValue
+ case _ =>
+ val writer = new ByteArrayOutputStream()
+ Utils.tryWithResource(factory.createGenerator(writer,
JsonEncoding.UTF8)) {
+ generator => generator.copyCurrentStructure(parser)
+ }
+ UTF8String.fromBytes(writer.toByteArray)
+ }
+ }
- case (VALUE_NUMBER_INT, LongType) =>
- parser.getLongValue
+ case TimestampType =>
+ (parser: JsonParser) => convertField(parser) {
+ parser.getCurrentToken match {
+ case VALUE_STRING =>
+ // This one will lose microseconds parts.
+ // See https://issues.apache.org/jira/browse/SPARK-10681.
+ DateTimeUtils.stringToTime(parser.getText).getTime * 1000L
- case (VALUE_TRUE, BooleanType) =>
- true
+ case VALUE_NUMBER_INT =>
+ parser.getLongValue * 1000000L
- case (VALUE_FALSE, BooleanType) =>
- false
+ case _ => failedConversion(parser, dataType)
+ }
+ }
- case (START_OBJECT, st: StructType) =>
- convertObject(factory, parser, st)
+ case DateType =>
+ (parser: JsonParser) => convertField(parser) {
+ parser.getCurrentToken match {
+ case VALUE_STRING =>
+ val stringValue = parser.getText
+ if (stringValue.contains("-")) {
+ // The format of this string will probably be "yyyy-mm-dd".
+
DateTimeUtils.millisToDays(DateTimeUtils.stringToTime(parser.getText).getTime)
+ } else {
+ // In Spark 1.5.0, we store the data as number of days since
epoch in string.
+ // So, we just convert it to Int.
+ stringValue.toInt
+ }
- case (START_ARRAY, ArrayType(st, _)) =>
- convertArray(factory, parser, st)
+ case _ => failedConversion(parser, dataType)
+ }
+ }
- case (START_OBJECT, MapType(StringType, kt, _)) =>
- convertMap(factory, parser, kt)
+ case BinaryType =>
+ (parser: JsonParser) => convertField(parser) {
+ parser.getCurrentToken match {
+ case VALUE_STRING => parser.getBinaryValue
+ case _ => failedConversion(parser, dataType)
+ }
+ }
+
+ case dt: DecimalType =>
+ (parser: JsonParser) => convertField(parser) {
+ parser.getCurrentToken match {
+ case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT) =>
+ Decimal(parser.getDecimalValue, dt.precision, dt.scale)
+
+ case _ => failedConversion(parser, dt)
+ }
+ }
+
+ case st: StructType =>
+ val fieldConverters = st.map(_.dataType).map(makeConverter)
+ (parser: JsonParser) => convertField(parser) {
+ parser.getCurrentToken match {
+ case START_OBJECT => convertObject(parser, st, fieldConverters)
+ case _ => failedConversion(parser, st)
+ }
+ }
+
+ case at: ArrayType =>
+ val elementConverter = makeConverter(at.elementType)
+ (parser: JsonParser) => convertField(parser) {
+ parser.getCurrentToken match {
+ case START_ARRAY => convertArray(parser, elementConverter)
+ case _ => failedConversion(parser, at)
+ }
+ }
+
+ case mt: MapType =>
+ val valueConverter = makeConverter(mt.valueType)
+ (parser: JsonParser) => convertField(parser) {
+ parser.getCurrentToken match {
+ case START_OBJECT => convertMap(parser, valueConverter)
+ case _ => failedConversion(parser, mt)
+ }
+ }
+
+ case udt: UserDefinedType[_] =>
+ makeConverter(udt.sqlType)
+
+ case _ =>
+ (parser: JsonParser) =>
+ failedConversion(parser, dataType)
+ }
+
+ /**
+ * This converts a field. If this is called after `START_OBJECT`, then,
the next token can be
+ * `FIELD_NAME`. Since the names are kept in
`JacksonParser.convertObject`, this `FIELD_NAME`
+ * token can be skipped as below. When this is called after
`START_ARRAY`, the tokens become
+ * ones about values until `END_ARRAY`. In this case, we don't have to
skip.
+ */
+ private def convertField(parser: JsonParser)(f: => Any): Any = {
--- End diff --
these 2 and https://github.com/apache/spark/pull/14102/files#r74034675 are
just some common logics for all data types, can we put them together?
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