Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/5801#discussion_r29565973
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD2.scala
---
@@ -0,0 +1,409 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.json
+
+import java.io.ByteArrayOutputStream
+import java.sql.Timestamp
+
+import scala.collection.Map
+
+import com.fasterxml.jackson.core._
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types._
+import org.apache.spark.Logging
+
+private[sql] object JsonRDD2 extends Logging {
+ def jsonStringToRow(
+ json: RDD[String],
+ schema: StructType,
+ columnNameOfCorruptRecords: String): RDD[Row] = {
+ parseJson(json, schema, columnNameOfCorruptRecords)
+ }
+
+ def inferSchema(
+ json: RDD[String],
+ samplingRatio: Double = 1.0,
+ columnNameOfCorruptRecords: String): StructType = {
+ require(samplingRatio > 0, s"samplingRatio ($samplingRatio) should be
greater than 0")
+ val schemaData = if (samplingRatio > 0.99) {
+ json
+ } else {
+ json.sample(withReplacement = false, samplingRatio, 1)
+ }
+
+ // perform schema inference on each row and merge afterwards
+ schemaData.mapPartitions { iter =>
+ val factory = new JsonFactory()
+ iter.map { row =>
+ try {
+ val parser = factory.createParser(row)
+ parser.nextToken()
+ inferField(parser)
+ } catch {
+ case _: JsonParseException =>
+ StructType(Seq(StructField(columnNameOfCorruptRecords,
StringType)))
+ }
+ }
+ }.treeAggregate[DataType](StructType(Seq()))(compatibleRootType,
compatibleRootType) match {
+ case st: StructType => nullTypeToStringType(st)
+ }
+ }
+
+ /**
+ * Infer the type of a json document from the parser's token stream
+ */
+ private 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)
+
+ case VALUE_STRING if parser.getTextLength < 1 => NullType
+ case VALUE_STRING => StringType
+ case START_OBJECT =>
+ val builder = Seq.newBuilder[StructField]
+ while (nextUntil(parser, END_OBJECT)) {
+ builder += StructField(parser.getCurrentName,
inferField(parser), nullable = true)
+ }
+
+ StructType(builder.result().sortBy(_.name))
+
+ case START_ARRAY =>
+ // If this JSON array is empty, we use NullType as a placeholder.
+ // If this array is not empty in other JSON objects, we can resolve
+ // the type as we pass through all JSON objects.
+ var elementType: DataType = NullType
+ while (nextUntil(parser, END_ARRAY)) {
+ elementType = compatibleType(elementType, inferField(parser))
+ }
+
+ ArrayType(elementType)
+
+ case VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT =>
+ import JsonParser.NumberType._
+ parser.getNumberType match {
+ // For Integer values, use LongType by default.
+ case INT | LONG => LongType
+ // Since we do not have a data type backed by BigInteger,
+ // when we see a Java BigInteger, we use DecimalType.
+ case BIG_INTEGER | BIG_DECIMAL => DecimalType.Unlimited
+ case FLOAT | DOUBLE => DoubleType
+ }
+
+ case VALUE_TRUE | VALUE_FALSE => BooleanType
+ }
+ }
+
+ def nullTypeToStringType(struct: StructType): StructType = {
+ val fields = struct.fields.map {
+ case StructField(fieldName, dataType, nullable, _) =>
+ val newType = dataType match {
+ case NullType => StringType
+ case ArrayType(NullType, containsNull) => ArrayType(StringType,
containsNull)
+ case ArrayType(struct: StructType, containsNull) =>
+ ArrayType(nullTypeToStringType(struct), containsNull)
+ case struct: StructType =>nullTypeToStringType(struct)
+ case other: DataType => other
+ }
+
+ StructField(fieldName, newType, nullable)
+ }
+
+ StructType(fields)
+ }
+
+ /**
+ * Advance the parser until a null or a specific token is found
+ */
+ private def nextUntil(parser: JsonParser, stopOn: JsonToken): Boolean = {
+ parser.nextToken() match {
+ case null => false
+ case x => x != stopOn
+ }
+ }
+
+ /**
+ * Remove top-level ArrayType wrappers and merge the remaining schemas
+ */
+ private def compatibleRootType: (DataType, DataType) => DataType = {
+ case (ArrayType(ty1, _), ty2) => compatibleRootType(ty1, ty2)
+ case (ty1, ArrayType(ty2, _)) => compatibleRootType(ty1, ty2)
+ case (ty1, ty2) => compatibleType(ty1, ty2)
+ }
+
+ /**
+ * Returns the most general data type for two given data types.
+ */
+ private[json] def compatibleType(t1: DataType, t2: DataType): DataType =
{
+ HiveTypeCoercion.findTightestCommonType(t1, t2).getOrElse {
+ // t1 or t2 is a StructType, ArrayType, or an unexpected type.
+ (t1, t2) match {
+ case (other: DataType, NullType) => other
+ case (NullType, other: DataType) => other
+ case (StructType(fields1), StructType(fields2)) =>
+ val newFields = (fields1 ++ fields2).groupBy(field =>
field.name).map {
+ case (name, fieldTypes) =>
+ val dataType =
fieldTypes.view.map(_.dataType).reduce(compatibleType)
+ StructField(name, dataType, nullable = true)
+ }
+ StructType(newFields.toSeq.sortBy(_.name))
+
+ case (ArrayType(elementType1, containsNull1),
ArrayType(elementType2, containsNull2)) =>
+ ArrayType(compatibleType(elementType1, elementType2),
containsNull1 || containsNull2)
+
+ // strings and every string is a Json object.
+ case (_, _) => StringType
+ }
+ }
+ }
+
+ /**
+ * Parse the current token (and related children) according to a desired
schema
+ */
+ private[sql] 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(parser.getText)
+
+ case (VALUE_STRING, _) if parser.getTextLength < 1 =>
+ // guard the non string type
+ null
+
+ case (VALUE_STRING, DateType) =>
+
DateUtils.millisToDays(DateUtils.stringToTime(parser.getText).getTime)
+
+ case (VALUE_STRING, TimestampType) =>
+ new Timestamp(DateUtils.stringToTime(parser.getText).getTime)
+
+ case (VALUE_NUMBER_INT, TimestampType) =>
+ new Timestamp(parser.getLongValue)
+
+ case (_, StringType) =>
+ val writer = new ByteArrayOutputStream()
+ val generator = factory.createGenerator(writer, JsonEncoding.UTF8)
+ generator.copyCurrentStructure(parser)
+ generator.close()
+ UTF8String(writer.toByteArray)
+
+ case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, FloatType) =>
+ parser.getFloatValue
+
+ case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, DoubleType) =>
+ parser.getDoubleValue
+
+ case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, DecimalType()) =>
+ Decimal(parser.getDecimalValue)
+
+ case (VALUE_NUMBER_INT, ByteType) =>
+ parser.getByteValue
+
+ case (VALUE_NUMBER_INT, ShortType) =>
+ parser.getShortValue
+
+ case (VALUE_NUMBER_INT, IntegerType) =>
+ parser.getIntValue
+
+ case (VALUE_NUMBER_INT, LongType) =>
+ parser.getLongValue
+
+ case (VALUE_TRUE, BooleanType) =>
+ true
+
+ case (VALUE_FALSE, BooleanType) =>
+ false
+
+ case (START_OBJECT, st: StructType) =>
+ convertObject(factory, parser, st)
+
+ case (START_ARRAY, ArrayType(st, _)) =>
+ convertList(factory, parser, st)
+
+ case (START_OBJECT, ArrayType(st, _)) =>
+ // 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
+
+ case (START_OBJECT, MapType(StringType, kt, _)) =>
+ convertMap(factory, parser, kt)
+
+ case (_, udt: UserDefinedType[_]) =>
+ udt.deserialize(convertField(factory, parser, udt.sqlType))
+
+ case _ =>
+ parser.skipChildren()
+ null
--- End diff --
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