[SPARK-26163][SQL] Parsing decimals from JSON using locale

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExprUtils.scala

@@ -17,6 +17,9 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import java.text.{DecimalFormat, DecimalFormatSymbols, ParsePosition}
+import java.util.Locale
+
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.util.ArrayBasedMapData
 import org.apache.spark.sql.types.{DataType, MapType, StringType, StructType}
@@ -83,4 +86,22 @@ object ExprUtils {
       }
     }
   }
+
+  def getDecimalParser(locale: Locale): String => java.math.BigDecimal = {
+    if (locale == Locale.US) { // Special handling the default locale for backward compatibility
+      (s: String) => new java.math.BigDecimal(s.replaceAll(",", ""))
+    } else {
+      val decimalFormat = new DecimalFormat("", new DecimalFormatSymbols(locale))
+      decimalFormat.setParseBigDecimal(true)
+      (s: String) => {
+        val pos = new ParsePosition(0)
+        val result = decimalFormat.parse(s, pos).asInstanceOf[java.math.BigDecimal]
+        if (pos.getIndex() != s.length() || pos.getErrorIndex() != -1) {
+          throw new IllegalArgumentException("Cannot parse any decimal");
+        } else {
+          result
+        }
+      }
+    }
+  }
 }

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonExpressions.scala

@@ -23,12 +23,10 @@ import scala.util.parsing.combinator.RegexParsers
 
 import com.fasterxml.jackson.core._
 
-import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.json._
-import org.apache.spark.sql.catalyst.json.JsonInferSchema.inferField
 import org.apache.spark.sql.catalyst.util._
 import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.types._
@@ -775,6 +773,9 @@ case class SchemaOfJson(
     factory
   }
 
+  @transient
+  private lazy val jsonInferSchema = new JsonInferSchema(jsonOptions)
+
   @transient
   private lazy val json = child.eval().asInstanceOf[UTF8String]
 
@@ -787,7 +788,7 @@ case class SchemaOfJson(
   override def eval(v: InternalRow): Any = {
     val dt = Utils.tryWithResource(CreateJacksonParser.utf8String(jsonFactory, json)) { parser =>
       parser.nextToken()
-      inferField(parser, jsonOptions)
+      jsonInferSchema.inferField(parser)
     }
 
     UTF8String.fromString(dt.catalogString)

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JacksonParser.scala

@@ -29,6 +29,7 @@ import org.apache.spark.internal.Logging
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.util._
+import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.util.Utils
@@ -135,6 +136,8 @@ class JacksonParser(
     }
   }
 
+  private val decimalParser = ExprUtils.getDecimalParser(options.locale)
+
   /**
    * Create a converter which converts the JSON documents held by the `JsonParser`
    * to a value according to a desired schema.
@@ -261,6 +264,9 @@ class JacksonParser(
       (parser: JsonParser) => parseJsonToken[Decimal](parser, dataType) {
         case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT) =>
           Decimal(parser.getDecimalValue, dt.precision, dt.scale)
+        case VALUE_STRING if parser.getTextLength >= 1 =>
+          val bigDecimal = decimalParser(parser.getText)
+          Decimal(bigDecimal, dt.precision, dt.scale)
       }
 
     case st: StructType =>

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JsonInferSchema.scala

@@ -19,18 +19,23 @@ package org.apache.spark.sql.catalyst.json
 
 import java.util.Comparator
 
+import scala.util.control.Exception.allCatch
+
 import com.fasterxml.jackson.core._
 
 import org.apache.spark.SparkException
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.analysis.TypeCoercion
+import org.apache.spark.sql.catalyst.expressions.ExprUtils
 import org.apache.spark.sql.catalyst.json.JacksonUtils.nextUntil
 import org.apache.spark.sql.catalyst.util.{DropMalformedMode, FailFastMode, ParseMode, PermissiveMode}
 import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
-private[sql] object JsonInferSchema {
+private[sql] class JsonInferSchema(options: JSONOptions) extends Serializable {
+
+  private val decimalParser = ExprUtils.getDecimalParser(options.locale)
 
   /**
    * Infer the type of a collection of json records in three stages:
@@ -40,21 +45,20 @@ private[sql] object JsonInferSchema {
    */
   def infer[T](
       json: RDD[T],
-      configOptions: JSONOptions,
       createParser: (JsonFactory, T) => JsonParser): StructType = {
-    val parseMode = configOptions.parseMode
-    val columnNameOfCorruptRecord = configOptions.columnNameOfCorruptRecord
+    val parseMode = options.parseMode
+    val columnNameOfCorruptRecord = options.columnNameOfCorruptRecord
 
     // In each RDD partition, perform schema inference on each row and merge afterwards.
-    val typeMerger = compatibleRootType(columnNameOfCorruptRecord, parseMode)
+    val typeMerger = JsonInferSchema.compatibleRootType(columnNameOfCorruptRecord, parseMode)
     val mergedTypesFromPartitions = json.mapPartitions { iter =>
       val factory = new JsonFactory()
-      configOptions.setJacksonOptions(factory)
+      options.setJacksonOptions(factory)
       iter.flatMap { row =>
         try {
           Utils.tryWithResource(createParser(factory, row)) { parser =>
             parser.nextToken()
-            Some(inferField(parser, configOptions))
+            Some(inferField(parser))
           }
         } catch {
           case  e @ (_: RuntimeException | _: JsonProcessingException) => parseMode match {
@@ -82,42 +86,25 @@ private[sql] object JsonInferSchema {
     }
     json.sparkContext.runJob(mergedTypesFromPartitions, foldPartition, mergeResult)
 
-    canonicalizeType(rootType, configOptions) match {
+    canonicalizeType(rootType, options) match {
       case Some(st: StructType) => st
       case _ =>
         // canonicalizeType erases all empty structs, including the only one we want to keep
         StructType(Nil)
     }
   }
 
-  private[this] val structFieldComparator = new Comparator[StructField] {
-    override def compare(o1: StructField, o2: StructField): Int = {
-      o1.name.compareTo(o2.name)
-    }
-  }
-
-  private def isSorted(arr: Array[StructField]): Boolean = {
-    var i: Int = 0
-    while (i < arr.length - 1) {
-      if (structFieldComparator.compare(arr(i), arr(i + 1)) > 0) {
-        return false
-      }
-      i += 1
-    }
-    true
-  }
-
   /**
    * Infer the type of a json document from the parser's token stream
    */
-  def inferField(parser: JsonParser, configOptions: JSONOptions): DataType = {
+  def inferField(parser: JsonParser): DataType = {
     import com.fasterxml.jackson.core.JsonToken._
     parser.getCurrentToken match {
       case null | VALUE_NULL => NullType
 
       case FIELD_NAME =>
         parser.nextToken()
-        inferField(parser, configOptions)
+        inferField(parser)
 
       case VALUE_STRING if parser.getTextLength < 1 =>
         // Zero length strings and nulls have special handling to deal
@@ -128,18 +115,25 @@ private[sql] object JsonInferSchema {
         // record fields' types have been combined.
         NullType
 
+      case VALUE_STRING if options.prefersDecimal =>
+        val decimalTry = allCatch opt {
+          val bigDecimal = decimalParser(parser.getText)
+            DecimalType(bigDecimal.precision, bigDecimal.scale)
+        }
+        decimalTry.getOrElse(StringType)
       case VALUE_STRING => StringType
+
       case START_OBJECT =>
         val builder = Array.newBuilder[StructField]
         while (nextUntil(parser, END_OBJECT)) {
           builder += StructField(
             parser.getCurrentName,
-            inferField(parser, configOptions),
+            inferField(parser),
             nullable = true)
         }
         val fields: Array[StructField] = builder.result()
         // Note: other code relies on this sorting for correctness, so don't remove it!
-        java.util.Arrays.sort(fields, structFieldComparator)
+        java.util.Arrays.sort(fields, JsonInferSchema.structFieldComparator)
         StructType(fields)
 
       case START_ARRAY =>
@@ -148,15 +142,15 @@ private[sql] object JsonInferSchema {
         // the type as we pass through all JSON objects.
         var elementType: DataType = NullType
         while (nextUntil(parser, END_ARRAY)) {
-          elementType = compatibleType(
-            elementType, inferField(parser, configOptions))
+          elementType = JsonInferSchema.compatibleType(
+            elementType, inferField(parser))
         }
 
         ArrayType(elementType)
 
-      case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT) if configOptions.primitivesAsString => StringType
+      case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT) if options.primitivesAsString => StringType
 
-      case (VALUE_TRUE | VALUE_FALSE) if configOptions.primitivesAsString => StringType
+      case (VALUE_TRUE | VALUE_FALSE) if options.primitivesAsString => StringType
 
       case VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT =>
         import JsonParser.NumberType._
@@ -172,7 +166,7 @@ private[sql] object JsonInferSchema {
             } else {
               DoubleType
             }
-          case FLOAT | DOUBLE if configOptions.prefersDecimal =>
+          case FLOAT | DOUBLE if options.prefersDecimal =>
             val v = parser.getDecimalValue
             if (Math.max(v.precision(), v.scale()) <= DecimalType.MAX_PRECISION) {
               DecimalType(Math.max(v.precision(), v.scale()), v.scale())
@@ -217,20 +211,39 @@ private[sql] object JsonInferSchema {
 
     case other => Some(other)
   }
+}
+
+object JsonInferSchema {
+  val structFieldComparator = new Comparator[StructField] {
+    override def compare(o1: StructField, o2: StructField): Int = {
+      o1.name.compareTo(o2.name)
+    }
+  }
+
+  def isSorted(arr: Array[StructField]): Boolean = {
+    var i: Int = 0
+    while (i < arr.length - 1) {
+      if (structFieldComparator.compare(arr(i), arr(i + 1)) > 0) {
+        return false
+      }
+      i += 1
+    }
+    true
+  }
 
-  private def withCorruptField(
+  def withCorruptField(
       struct: StructType,
       other: DataType,
       columnNameOfCorruptRecords: String,
-      parseMode: ParseMode) = parseMode match {
+      parseMode: ParseMode): StructType = parseMode match {
     case PermissiveMode =>
       // If we see any other data type at the root level, we get records that cannot be
       // parsed. So, we use the struct as the data type and add the corrupt field to the schema.
       if (!struct.fieldNames.contains(columnNameOfCorruptRecords)) {
         // If this given struct does not have a column used for corrupt records,
         // add this field.
         val newFields: Array[StructField] =
-          StructField(columnNameOfCorruptRecords, StringType, nullable = true) +: struct.fields
+        StructField(columnNameOfCorruptRecords, StringType, nullable = true) +: struct.fields
         // Note: other code relies on this sorting for correctness, so don't remove it!
         java.util.Arrays.sort(newFields, structFieldComparator)
         StructType(newFields)
@@ -253,7 +266,7 @@ private[sql] object JsonInferSchema {
   /**
    * Remove top-level ArrayType wrappers and merge the remaining schemas
    */
-  private def compatibleRootType(
+  def compatibleRootType(
       columnNameOfCorruptRecords: String,
       parseMode: ParseMode): (DataType, DataType) => DataType = {
     // Since we support array of json objects at the top level,

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala

@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import java.text.SimpleDateFormat
+import java.text.{DecimalFormat, DecimalFormatSymbols, SimpleDateFormat}
 import java.util.{Calendar, Locale}
 
 import org.scalatest.exceptions.TestFailedException
@@ -765,4 +765,44 @@ class JsonExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper with
         timeZoneId = gmtId),
       expectedErrMsg = "The field for corrupt records must be string type and nullable")
   }
+
+  def decimalInput(langTag: String): (Decimal, String) = {
+    val decimalVal = new java.math.BigDecimal("1000.001")
+    val decimalType = new DecimalType(10, 5)
+    val expected = Decimal(decimalVal, decimalType.precision, decimalType.scale)
+    val decimalFormat = new DecimalFormat("",
+      new DecimalFormatSymbols(Locale.forLanguageTag(langTag)))
+    val input = s"""{"d": "${decimalFormat.format(expected.toBigDecimal)}"}"""
+
+    (expected, input)
+  }
+
+  test("parse decimals using locale") {
+    def checkDecimalParsing(langTag: String): Unit = {
+      val schema = new StructType().add("d", DecimalType(10, 5))
+      val options = Map("locale" -> langTag)
+      val (expected, input) = decimalInput(langTag)
+
+      checkEvaluation(
+        JsonToStructs(schema, options, Literal.create(input), gmtId),
+        InternalRow(expected))
+    }
+
+    Seq("en-US", "ko-KR", "ru-RU", "de-DE").foreach(checkDecimalParsing)
+  }
+
+  test("inferring the decimal type using locale") {
+    def checkDecimalInfer(langTag: String, expectedType: String): Unit = {
+      val options = Map("locale" -> langTag, "prefersDecimal" -> "true")
+      val (_, input) = decimalInput(langTag)
+
+      checkEvaluation(
+        SchemaOfJson(Literal.create(input), options),
+        expectedType)
+    }
+
+    Seq("en-US", "ko-KR", "ru-RU", "de-DE").foreach {
+        checkDecimalInfer(_, """struct<d:decimal(7,3)>""")
+    }
+  }
 }

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonDataSource.scala

@@ -107,7 +107,7 @@ object TextInputJsonDataSource extends JsonDataSource {
     }.getOrElse(CreateJacksonParser.internalRow(_: JsonFactory, _: InternalRow))
 
     SQLExecution.withSQLConfPropagated(json.sparkSession) {
-      JsonInferSchema.infer(rdd, parsedOptions, rowParser)
+      new JsonInferSchema(parsedOptions).infer(rdd, rowParser)
     }
   }
 
@@ -166,7 +166,7 @@ object MultiLineJsonDataSource extends JsonDataSource {
       .getOrElse(createParser(_: JsonFactory, _: PortableDataStream))
 
     SQLExecution.withSQLConfPropagated(sparkSession) {
-      JsonInferSchema.infer[PortableDataStream](sampled, parsedOptions, parser)
+      new JsonInferSchema(parsedOptions).infer[PortableDataStream](sampled, parser)
     }
   }