Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/999#discussion_r13828804
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
---
@@ -0,0 +1,402 @@
+/*
+ * 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 scala.collection.JavaConversions._
+import scala.math.BigDecimal
+
+import com.fasterxml.jackson.databind.ObjectMapper
+
+import org.apache.spark.annotation.{DeveloperApi, Experimental}
+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.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.types._
+import org.apache.spark.sql.execution.{ExistingRdd, SparkLogicalPlan}
+import org.apache.spark.sql.Logging
+
+@Experimental
+private[sql] object JsonRDD extends Logging {
+
+ @DeveloperApi
+ private[sql] def inferSchema(
+ json: RDD[String],
+ samplingRatio: Double = 1.0): LogicalPlan = {
+ require(samplingRatio > 0)
+ val schemaData = if (samplingRatio > 0.99) json else
json.sample(false, samplingRatio, 1)
+
+ val allKeys =
parseJson(schemaData).map(getAllKeysWithValueTypes).reduce(_ ++ _)
+
+ val baseSchema = createSchema(allKeys)
+
+ createLogicalPlan(json, baseSchema)
+ }
+
+ private def createLogicalPlan(
+ json: RDD[String],
+ baseSchema: StructType): LogicalPlan = {
+ val schema = nullTypeToStringType(baseSchema)
+
+ SparkLogicalPlan(ExistingRdd(asAttributes(schema),
parseJson(json).map(asRow(_, schema))))
+ }
+
+ private def createSchema(allKeys: Set[(String, DataType)]): StructType =
{
+ // Resolve type conflicts
+ val resolved = allKeys.groupBy {
+ case (key, dataType) => key
+ }.map {
+ // Now, keys and types are organized in the format of
+ // key -> Set(type1, type2, ...).
+ case (key, typeSet) => {
+ val fieldName = key.substring(1, key.length - 1).split("`.`").toSeq
+ val dataType = typeSet.map {
+ case (_, dataType) => dataType
+ }.reduce((type1: DataType, type2: DataType) =>
getCompatibleType(type1, type2))
+
+ (fieldName, dataType)
+ }
+ }
+
+ def makeStruct(values: Seq[Seq[String]], prefix: Seq[String]):
StructType = {
+ val (topLevel, structLike) = values.partition(_.size == 1)
+ val topLevelFields = topLevel.filter {
+ name => resolved.get(prefix ++ name).get match {
+ case ArrayType(StructType(Nil)) => false
+ case ArrayType(_) => true
+ case struct: StructType => false
+ case _ => true
+ }
+ }.map {
+ a => StructField(a.head, resolved.get(prefix ++ a).get, nullable =
true)
+ }
+
+ val structFields: Seq[StructField] = structLike.groupBy(_(0)).map {
+ case (name, fields) => {
+ val nestedFields = fields.map(_.tail)
+ val structType = makeStruct(nestedFields, prefix :+ name)
+ val dataType = resolved.get(prefix :+ name).get
+ dataType match {
+ case array: ArrayType => Some(StructField(name,
ArrayType(structType), nullable = true))
+ case struct: StructType => Some(StructField(name, structType,
nullable = true))
+ // dataType is StringType means that we have resolved type
conflicts involving
+ // primitive types and complex types. So, the type of name has
been relaxed to
+ // StringType. Also, this field should have already been put
in topLevelFields.
+ case StringType => None
+ }
+ }
+ }.flatMap(field => field).toSeq
+
+ StructType(
+ (topLevelFields ++ structFields).sortBy {
+ case StructField(name, _, _) => name
+ })
+ }
+
+ makeStruct(resolved.keySet.toSeq, Nil)
+ }
+
+ /**
+ * Returns the most general data type for two given data types.
+ */
+ private[json] def getCompatibleType(t1: DataType, t2: DataType):
DataType = {
+ // Try and find a promotion rule that contains both types in question.
+ val applicableConversion = HiveTypeCoercion.allPromotions.find(p =>
p.contains(t1) && p
+ .contains(t2))
+
+ // If found return the widest common type, otherwise None
+ val returnType = applicableConversion.map(_.filter(t => t == t1 || t
== t2).last)
+
+ if (returnType.isDefined) {
+ returnType.get
+ } else {
+ // 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.map(field =>
field.dataType).reduce(
+ (type1: DataType, type2: DataType) =>
getCompatibleType(type1, type2))
+ StructField(name, dataType, true)
+ }
+ }
+ StructType(newFields.toSeq.sortBy {
+ case StructField(name, _, _) => name
+ })
+ }
+ case (ArrayType(elementType1), ArrayType(elementType2)) =>
+ ArrayType(getCompatibleType(elementType1, elementType2))
+ // TODO: We should use JsonObjectStringType to mark that values of
field will be
+ // strings and every string is a Json object.
+ case (_, _) => StringType
+ }
+ }
+ }
+
+ private def getPrimitiveType(value: Any): DataType = {
+ value match {
+ case value: java.lang.String => StringType
+ case value: java.lang.Integer => IntegerType
+ case value: java.lang.Long => LongType
+ // Since we do not have a data type backed by BigInteger,
+ // when we see a Java BigInteger, we use DecimalType.
+ case value: java.math.BigInteger => DecimalType
+ case value: java.lang.Double => DoubleType
+ case value: java.math.BigDecimal => DecimalType
+ case value: java.lang.Boolean => BooleanType
+ case null => NullType
+ // Unexpected data type.
+ case _ => StringType
+ }
+ }
+
+ /**
+ * Returns the element type of an JSON array. We go through all elements
of this array
+ * to detect any possible type conflict. We use [[getCompatibleType]] to
resolve
+ * type conflicts. Right now, when the element of an array is another
array, we
+ * treat the element as String.
+ */
+ private def getTypeOfArray(l: Seq[Any]): ArrayType = {
+ val elements = l.flatMap(v => Option(v))
+ if (elements.isEmpty) {
+ // 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 after we have passed through all JSON objects.
+ ArrayType(NullType)
+ } else {
+ val elementType = elements.map {
+ e => e match {
+ case map: Map[_, _] => StructType(Nil)
+ // We have an array of arrays. If those element arrays do not
have the same
+ // element types, we will return ArrayType[StringType].
+ case seq: Seq[_] => getTypeOfArray(seq)
+ case value => getPrimitiveType(value)
+ }
+ }.reduce((type1: DataType, type2: DataType) =>
getCompatibleType(type1, type2))
+
+ ArrayType(elementType)
+ }
+ }
+
+ /**
+ * Figures out all key names and data types of values from a parsed JSON
object
+ * (in the format of Map[Stirng, Any]). When the value of a key is an
JSON object, we
+ * only use a placeholder (StructType(Nil)) to mark that it should be a
struct
+ * instead of getting all fields of this struct because a field does not
appear
+ * in this JSON object can appear in other JSON objects.
+ */
+ private def getAllKeysWithValueTypes(m: Map[String, Any]): Set[(String,
DataType)] = {
+ m.map{
+ // Quote the key with backticks to handle cases which have dots
+ // in the field name.
+ case (key, dataType) => (s"`$key`", dataType)
+ }.flatMap {
+ case (key: String, struct: Map[String, Any]) => {
+ // The value associted with the key is an JSON object.
+ getAllKeysWithValueTypes(struct).map {
+ case (k, dataType) => (s"$key.$k", dataType)
+ } ++ Set((key, StructType(Nil)))
+ }
+ case (key: String, array: List[Any]) => {
+ // The value associted with the key is an array.
+ getTypeOfArray(array) match {
+ case ArrayType(StructType(Nil)) => {
+ // The elements of this arrays are structs.
+ array.asInstanceOf[List[Map[String, Any]]].flatMap {
+ element => getAllKeysWithValueTypes(element)
+ }.map {
+ case (k, dataType) => (s"$key.$k", dataType)
+ } :+ (key, ArrayType(StructType(Nil)))
+ }
+ case ArrayType(elementType) => (key, ArrayType(elementType)) ::
Nil
+ }
+ }
+ case (key: String, value) => (key, getPrimitiveType(value)) :: Nil
+ }.toSet
+ }
+
+ /**
+ * Converts a Java Map/List to a Scala Map/List.
--- End diff --
Is there a reason we can't just `import scala.collection.JavaConversions._`?
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