spark SchemaConverters 源码
spark SchemaConverters 代码
文件路径:/connector/protobuf/src/main/scala/org/apache/spark/sql/protobuf/utils/SchemaConverters.scala
/*
* 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.protobuf.utils
import scala.collection.JavaConverters._
import com.google.protobuf.Descriptors.{Descriptor, FieldDescriptor}
import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.sql.protobuf.ScalaReflectionLock
import org.apache.spark.sql.types._
@DeveloperApi
object SchemaConverters {
/**
* Internal wrapper for SQL data type and nullability.
*
* @since 3.4.0
*/
case class SchemaType(dataType: DataType, nullable: Boolean)
/**
* Converts an Protobuf schema to a corresponding Spark SQL schema.
*
* @since 3.4.0
*/
def toSqlType(descriptor: Descriptor): SchemaType = {
toSqlTypeHelper(descriptor)
}
def toSqlTypeHelper(descriptor: Descriptor): SchemaType = ScalaReflectionLock.synchronized {
SchemaType(
StructType(descriptor.getFields.asScala.flatMap(structFieldFor(_, Set.empty)).toSeq),
nullable = true)
}
def structFieldFor(
fd: FieldDescriptor,
existingRecordNames: Set[String]): Option[StructField] = {
import com.google.protobuf.Descriptors.FieldDescriptor.JavaType._
val dataType = fd.getJavaType match {
case INT => Some(IntegerType)
case LONG => Some(LongType)
case FLOAT => Some(FloatType)
case DOUBLE => Some(DoubleType)
case BOOLEAN => Some(BooleanType)
case STRING => Some(StringType)
case BYTE_STRING => Some(BinaryType)
case ENUM => Some(StringType)
case MESSAGE if fd.getMessageType.getName == "Duration" =>
Some(DayTimeIntervalType.defaultConcreteType)
case MESSAGE if fd.getMessageType.getName == "Timestamp" =>
Some(TimestampType)
case MESSAGE if fd.isRepeated && fd.getMessageType.getOptions.hasMapEntry =>
var keyType: DataType = NullType
var valueType: DataType = NullType
fd.getMessageType.getFields.forEach { field =>
field.getName match {
case "key" =>
keyType = structFieldFor(field, existingRecordNames).get.dataType
case "value" =>
valueType = structFieldFor(field, existingRecordNames).get.dataType
}
}
return Option(
StructField(
fd.getName,
MapType(keyType, valueType, valueContainsNull = false).defaultConcreteType,
nullable = false))
case MESSAGE =>
if (existingRecordNames.contains(fd.getFullName)) {
throw new IncompatibleSchemaException(s"""
|Found recursive reference in Protobuf schema, which can not be processed by Spark:
|${fd.toString()}""".stripMargin)
}
val newRecordNames = existingRecordNames + fd.getFullName
Option(
fd.getMessageType.getFields.asScala
.flatMap(structFieldFor(_, newRecordNames.toSet))
.toSeq)
.filter(_.nonEmpty)
.map(StructType.apply)
case _ =>
throw new IncompatibleSchemaException(
s"Cannot convert Protobuf type" +
s" ${fd.getJavaType}")
}
dataType.map(dt =>
StructField(
fd.getName,
if (fd.isRepeated) ArrayType(dt, containsNull = false) else dt,
nullable = !fd.isRequired && !fd.isRepeated))
}
private[protobuf] class IncompatibleSchemaException(msg: String, ex: Throwable = null)
extends Exception(msg, ex)
}
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