Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/999#discussion_r13601226
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/json/JsonTable.scala
---
@@ -0,0 +1,364 @@
+/*
+ * 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 org.apache.spark.annotation.Experimental
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.execution.{ExistingRdd, SparkLogicalPlan}
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.types._
+import org.apache.spark.sql.SchemaRDD
+import org.apache.spark.sql.Logging
+import org.apache.spark.sql.catalyst.expressions.{Alias,
AttributeReference, GetField}
+
+import com.fasterxml.jackson.databind.ObjectMapper
+
+import scala.collection.JavaConversions._
+import scala.math.BigDecimal
+import org.apache.spark.sql.catalyst.expressions.GetField
+import org.apache.spark.sql.catalyst.expressions.AttributeReference
+import org.apache.spark.sql.execution.SparkLogicalPlan
+import org.apache.spark.sql.catalyst.expressions.Alias
+import org.apache.spark.sql.catalyst.expressions.GetField
+import org.apache.spark.sql.catalyst.expressions.AttributeReference
+import org.apache.spark.sql.execution.SparkLogicalPlan
+import org.apache.spark.sql.catalyst.expressions.Alias
+import org.apache.spark.sql.catalyst.types.StructField
+import org.apache.spark.sql.catalyst.types.StructType
+import org.apache.spark.sql.catalyst.types.ArrayType
+import org.apache.spark.sql.catalyst.expressions.GetField
+import org.apache.spark.sql.catalyst.expressions.AttributeReference
+import org.apache.spark.sql.execution.SparkLogicalPlan
+import org.apache.spark.sql.catalyst.expressions.Alias
+
+sealed trait SchemaResolutionMode
+
+case object EAGER_SCHEMA_RESOLUTION extends SchemaResolutionMode
+case class EAGER_SCHEMA_RESOLUTION_WITH_SAMPLING(val fraction: Double)
extends SchemaResolutionMode
+case object LAZY_SCHEMA_RESOLUTION extends SchemaResolutionMode
+
+/**
+ * :: Experimental ::
+ * Converts a JSON file to a SparkSQL logical query plan. This
implementation is only designed to
+ * work on JSON files that have mostly uniform schema. The conversion
suffers from the following
+ * limitation:
+ * - The data is optionally sampled to determine all of the possible
fields. Any fields that do
+ * not appear in this sample will not be included in the final output.
+ */
+@Experimental
+object JsonTable extends Serializable with Logging {
+ def inferSchema(
+ json: RDD[String], sampleSchema: Option[Double] = None): LogicalPlan
= {
+ val schemaData = sampleSchema.map(json.sample(false, _,
1)).getOrElse(json)
+ val allKeys =
parseJson(schemaData).map(getAllKeysWithValueTypes).reduce(_ ++ _)
+
+ // 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))
+
+ // Finally, we replace all NullType to StringType. We do not need
to take care
+ // StructType because all fields with a StructType are represented
by a placeholder
+ // StructType(Nil).
+ dataType match {
+ case NullType => (fieldName, StringType)
+ case ArrayType(NullType) => (fieldName, ArrayType(StringType))
+ case other => (fieldName, other)
+ }
+ }
+ }
+
+ 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)
+ }.sortBy {
+ case StructField(name, _, _) => name
+ }
+
+ 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.sortBy {
+ case StructField(name, _, _) => name
+ }
+
+ StructType(topLevelFields ++ structFields)
+ }
+
+ val schema = makeStruct(resolved.keySet.toSeq, Nil)
+
+ SparkLogicalPlan(
+ ExistingRdd(
+ asAttributes(schema),
+ parseJson(json).map(asRow(_, schema))))
+ }
+
+ // numericPrecedence and booleanPrecedence are from WidenTypes.
+ // A widening conversion of a value with IntegerType and LongType to
FloatType,
+ // or of a value with LongType to DoubleType, may result in loss of
precision
+ // (some of the least significant bits of the value).
+ val numericPrecedence =
+ Seq(NullType, ByteType, ShortType, IntegerType, LongType, FloatType,
DoubleType, DecimalType)
+ // Boolean is only wider than Void
+ val booleanPrecedence = Seq(NullType, BooleanType)
+ val allPromotions: Seq[Seq[DataType]] = numericPrecedence ::
booleanPrecedence :: Nil
+
+ /**
+ * Returns the most general data type for two given data types.
+ */
+ protected def getCompatibleType(t1: DataType, t2: DataType): DataType = {
+ // Try and find a promotion rule that contains both types in question.
+ val applicableConversion = 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
+ // TODO: Returns the union of fields1 and fields2?
+ case (StructType(fields1), StructType(fields2))
+ if (fields1 == fields2) => StructType(fields1)
+ case (ArrayType(elementType1), ArrayType(elementType2)) =>
+ ArrayType(getCompatibleType(elementType1, elementType2))
+ case (_, _) => StringType
+ }
+ }
+ }
+
+ protected 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
+ // We comment out the following line in the development to catch
bugs.
+ // We need to enable this line in future to handle
+ // 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.
+ */
+ protected 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 a value of a key is an
object, we
+ * only use a placeholder for a struct type (StructType(Nil)) instead of
getting
+ * all fields of this struct because a field does not appear in this
JSON object
+ * can appear in other JSON objects.
+ */
+ protected 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.
+ * We do not use Jackson's scala module at here because
+ * DefaultScalaModule in jackson-module-scala will make
+ * the parsing very slow.
+ */
+ protected def scalafy(obj: Any): Any = obj match {
--- End diff --
We will use functions provided by Scala collections (e.g. map and flatMap).
So, we explicitly convert the type from Java Map/Lists to Scala Map/List.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
