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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