Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*
* 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.execution.datasources
import java.lang.{Double => JDouble, Long => JLong}
import java.math.{BigDecimal => JBigDecimal}
import java.time.ZoneId
import java.util.Locale
import scala.collection.mutable
import scala.collection.mutable.ArrayBuffer
import scala.util.Try
import scala.util.control.NonFatal
import org.apache.hadoop.fs.Path
import org.apache.spark.sql.catalyst.{InternalRow, SQLConfHelper}
import org.apache.spark.sql.catalyst.analysis.TypeCoercion
import org.apache.spark.sql.catalyst.catalog.CatalogTypes.TablePartitionSpec
import org.apache.spark.sql.catalyst.catalog.ExternalCatalogUtils.getPartitionValueString
import org.apache.spark.sql.catalyst.expressions.{Attribute, Cast, Literal}
import org.apache.spark.sql.catalyst.types.DataTypeUtils
import org.apache.spark.sql.catalyst.util.{CaseInsensitiveMap, DateFormatter, DateTimeUtils, TimestampFormatter}
import org.apache.spark.sql.errors.{QueryCompilationErrors, QueryExecutionErrors}
import org.apache.spark.sql.types._
import org.apache.spark.sql.util.SchemaUtils
import org.apache.spark.unsafe.types.UTF8String
// TODO: We should tighten up visibility of the classes here once we clean up Hive coupling.
object PartitionPath {
def apply(values: InternalRow, path: String): PartitionPath =
apply(values, new Path(path))
}
/**
* Holds a directory in a partitioned collection of files as well as the partition values
* in the form of a Row. Before scanning, the files at `path` need to be enumerated.
*/
case class PartitionPath(values: InternalRow, path: Path)
case class PartitionSpec(
partitionColumns: StructType,
partitions: Seq[PartitionPath])
object PartitionSpec {
val emptySpec = PartitionSpec(StructType(Seq.empty[StructField]), Seq.empty[PartitionPath])
}
object PartitioningUtils extends SQLConfHelper {
val timestampPartitionPattern = "yyyy-MM-dd HH:mm:ss[.S]"
case class TypedPartValue(value: String, dataType: DataType)
case class PartitionValues(columnNames: Seq[String], typedValues: Seq[TypedPartValue])
{
require(columnNames.size == typedValues.size)
}
import org.apache.spark.sql.catalyst.catalog.ExternalCatalogUtils.{escapePathName, unescapePathName, DEFAULT_PARTITION_NAME}
/**
* Given a group of qualified paths, tries to parse them and returns a partition specification.
* For example, given:
* {{{
* hdfs://:/path/to/partition/a=1/b=hello/c=3.14
* hdfs://:/path/to/partition/a=2/b=world/c=6.28
* }}}
* it returns:
* {{{
* PartitionSpec(
* partitionColumns = StructType(
* StructField(name = "a", dataType = IntegerType, nullable = true),
* StructField(name = "b", dataType = StringType, nullable = true),
* StructField(name = "c", dataType = DoubleType, nullable = true)),
* partitions = Seq(
* Partition(
* values = Row(1, "hello", 3.14),
* path = "hdfs://:/path/to/partition/a=1/b=hello/c=3.14"),
* Partition(
* values = Row(2, "world", 6.28),
* path = "hdfs://:/path/to/partition/a=2/b=world/c=6.28")))
* }}}
*/
private[datasources] def parsePartitions(
paths: Seq[Path],
typeInference: Boolean,
basePaths: Set[Path],
userSpecifiedSchema: Option[StructType],
caseSensitive: Boolean,
validatePartitionColumns: Boolean,
timeZoneId: String): PartitionSpec = {
parsePartitions(paths, typeInference, basePaths, userSpecifiedSchema, caseSensitive,
validatePartitionColumns, DateTimeUtils.getZoneId(timeZoneId))
}
private[datasources] def parsePartitions(
paths: Seq[Path],
typeInference: Boolean,
basePaths: Set[Path],
userSpecifiedSchema: Option[StructType],
caseSensitive: Boolean,
validatePartitionColumns: Boolean,
zoneId: ZoneId): PartitionSpec = {
val userSpecifiedDataTypes = if (userSpecifiedSchema.isDefined) {
val nameToDataType = userSpecifiedSchema.get.fields.map(f => f.name -> f.dataType).toMap
if (!caseSensitive) {
CaseInsensitiveMap(nameToDataType)
} else {
nameToDataType
}
} else {
Map.empty[String, DataType]
}
// SPARK-26990: use user specified field names if case insensitive.
val userSpecifiedNames = if (userSpecifiedSchema.isDefined && !caseSensitive) {
CaseInsensitiveMap(userSpecifiedSchema.get.fields.map(f => f.name -> f.name).toMap)
} else {
Map.empty[String, String]
}
val dateFormatter = DateFormatter(DateFormatter.defaultPattern)
val timestampFormatter = TimestampFormatter(
timestampPartitionPattern,
zoneId,
isParsing = true)
// First, we need to parse every partition's path and see if we can find partition values.
val (partitionValues, optDiscoveredBasePaths) = paths.map { path =>
parsePartition(path, typeInference, basePaths, userSpecifiedDataTypes,
validatePartitionColumns, zoneId, dateFormatter, timestampFormatter)
}.unzip
// We create pairs of (path -> path's partition value) here
// If the corresponding partition value is None, the pair will be skipped
val pathsWithPartitionValues = paths.zip(partitionValues).flatMap(x => x._2.map(x._1 -> _))
if (pathsWithPartitionValues.isEmpty) {
// This dataset is not partitioned.
PartitionSpec.emptySpec
} else {
// This dataset is partitioned. We need to check whether all partitions have the same
// partition columns and resolve potential type conflicts.
// Check if there is conflicting directory structure.
// For the paths such as:
// var paths = Seq(
// "hdfs://host:9000/invalidPath",
// "hdfs://host:9000/path/a=10/b=20",
// "hdfs://host:9000/path/a=10.5/b=hello")
// It will be recognised as conflicting directory structure:
// "hdfs://host:9000/invalidPath"
// "hdfs://host:9000/path"
// TODO: Selective case sensitivity.
val discoveredBasePaths = optDiscoveredBasePaths.flatten.map(_.toString.toLowerCase())
assert(
discoveredBasePaths.distinct.size == 1,
"Conflicting directory structures detected. Suspicious paths:\b" +
discoveredBasePaths.distinct.mkString("\n\t", "\n\t", "\n\n") +
"If provided paths are partition directories, please set " +
"\"basePath\" in the options of the data source to specify the " +
"root directory of the table. If there are multiple root directories, " +
"please load them separately and then union them.")
val resolvedPartitionValues = resolvePartitions(pathsWithPartitionValues, caseSensitive)
// Creates the StructType which represents the partition columns.
val fields = {
val PartitionValues(columnNames, typedValues) = resolvedPartitionValues.head
columnNames.zip(typedValues).map { case (name, TypedPartValue(_, dataType)) =>
// We always assume partition columns are nullable since we've no idea whether null values
// will be appended in the future.
val resultName = userSpecifiedNames.getOrElse(name, name)
val resultDataType = userSpecifiedDataTypes.getOrElse(name, dataType)
StructField(resultName, resultDataType, nullable = true)
}
}
// Finally, we create `Partition`s based on paths and resolved partition values.
val partitions = resolvedPartitionValues.zip(pathsWithPartitionValues).map {
case (PartitionValues(columnNames, typedValues), (path, _)) =>
val rowValues = columnNames.zip(typedValues).map { case (columnName, typedValue) =>
try {
castPartValueToDesiredType(typedValue.dataType, typedValue.value, zoneId)
} catch {
case NonFatal(_) =>
if (validatePartitionColumns) {
throw QueryExecutionErrors.failedToCastValueToDataTypeForPartitionColumnError(
typedValue.value, typedValue.dataType, columnName)
} else null
}
}
PartitionPath(InternalRow.fromSeq(rowValues), path)
}
PartitionSpec(StructType(fields), partitions)
}
}
/**
* Parses a single partition, returns column names and values of each partition column, also
* the path when we stop partition discovery. For example, given:
* {{{
* path = hdfs://:/path/to/partition/a=42/b=hello/c=3.14
* }}}
* it returns the partition:
* {{{
* PartitionValues(
* Seq("a", "b", "c"),
* Seq(
* Literal.create(42, IntegerType),
* Literal.create("hello", StringType),
* Literal.create(3.14, DoubleType)))
* }}}
* and the path when we stop the discovery is:
* {{{
* hdfs://:/path/to/partition
* }}}
*/
private[datasources] def parsePartition(
path: Path,
typeInference: Boolean,
basePaths: Set[Path],
userSpecifiedDataTypes: Map[String, DataType],
validatePartitionColumns: Boolean,
zoneId: ZoneId,
dateFormatter: DateFormatter,
timestampFormatter: TimestampFormatter): (Option[PartitionValues], Option[Path]) = {
val columns = ArrayBuffer.empty[(String, TypedPartValue)]
// Old Hadoop versions don't have `Path.isRoot`
var finished = path.getParent == null
// currentPath is the current path that we will use to parse partition column value.
var currentPath: Path = path
while (!finished) {
// Sometimes (e.g., when speculative task is enabled), temporary directories may be left
// uncleaned. Here we simply ignore them.
if (currentPath.getName.toLowerCase(Locale.ROOT) == "_temporary") {
return (None, None)
}
if (basePaths.contains(currentPath)) {
// If the currentPath is one of base paths. We should stop.
finished = true
} else {
// Let's say currentPath is a path of "/table/a=1/", currentPath.getName will give us a=1.
// Once we get the string, we try to parse it and find the partition column and value.
val maybeColumn =
parsePartitionColumn(currentPath.getName, typeInference, userSpecifiedDataTypes,
zoneId, dateFormatter, timestampFormatter)
maybeColumn.foreach(columns += _)
// Now, we determine if we should stop.
// When we hit any of the following cases, we will stop:
// - In this iteration, we could not parse the value of partition column and value,
// i.e. maybeColumn is None, and columns is not empty. At here we check if columns is
// empty to handle cases like /table/a=1/_temporary/something (we need to find a=1 in
// this case).
// - After we get the new currentPath, this new currentPath represent the top level dir
// i.e. currentPath.getParent == null. For the example of "/table/a=1/",
// the top level dir is "/table".
finished =
(maybeColumn.isEmpty && !columns.isEmpty) || currentPath.getParent == null
if (!finished) {
// For the above example, currentPath will be "/table/".
currentPath = currentPath.getParent
}
}
}
if (columns.isEmpty) {
(None, Some(path))
} else {
val (columnNames, values) = columns.reverse.unzip
(Some(PartitionValues(columnNames.toSeq, values.toSeq)), Some(currentPath))
}
}
private def parsePartitionColumn(
columnSpec: String,
typeInference: Boolean,
userSpecifiedDataTypes: Map[String, DataType],
zoneId: ZoneId,
dateFormatter: DateFormatter,
timestampFormatter: TimestampFormatter): Option[(String, TypedPartValue)] = {
val equalSignIndex = columnSpec.indexOf('=')
if (equalSignIndex == -1) {
None
} else {
val columnName = unescapePathName(columnSpec.take(equalSignIndex))
assert(columnName.nonEmpty, s"Empty partition column name in '$columnSpec'")
val rawColumnValue = columnSpec.drop(equalSignIndex + 1)
assert(rawColumnValue.nonEmpty, s"Empty partition column value in '$columnSpec'")
val dataType = if (userSpecifiedDataTypes.contains(columnName)) {
// SPARK-26188: if user provides corresponding column schema, get the column value without
// inference, and then cast it as user specified data type.
userSpecifiedDataTypes(columnName)
} else {
inferPartitionColumnValue(
rawColumnValue,
typeInference,
zoneId,
dateFormatter,
timestampFormatter)
}
Some(columnName -> TypedPartValue(rawColumnValue, dataType))
}
}
/**
* Given a partition path fragment, e.g. `fieldOne=1/fieldTwo=2`, returns a parsed spec
* for that fragment as a `TablePartitionSpec`, e.g. `Map(("fieldOne", "1"), ("fieldTwo", "2"))`.
*/
def parsePathFragment(pathFragment: String): TablePartitionSpec = {
parsePathFragmentAsSeq(pathFragment).toMap
}
/**
* Given a partition path fragment, e.g. `fieldOne=1/fieldTwo=2`, returns a parsed spec
* for that fragment as a `Seq[(String, String)]`, e.g.
* `Seq(("fieldOne", "1"), ("fieldTwo", "2"))`.
*/
def parsePathFragmentAsSeq(pathFragment: String): Seq[(String, String)] = {
pathFragment.split("/").map { kv =>
val pair = kv.split("=", 2)
(unescapePathName(pair(0)), unescapePathName(pair(1)))
}
}
/**
* This is the inverse of parsePathFragment().
*/
def getPathFragment(spec: TablePartitionSpec, partitionSchema: StructType): String = {
partitionSchema.map { field =>
escapePathName(field.name) + "=" +
getPartitionValueString(
removeLeadingZerosFromNumberTypePartition(spec(field.name), field.dataType))
}.mkString("/")
}
def removeLeadingZerosFromNumberTypePartition(value: String, dataType: DataType): String =
dataType match {
case ByteType | ShortType | IntegerType | LongType | FloatType | DoubleType =>
Option(castPartValueToDesiredType(dataType, value, null)).map(_.toString).orNull
case _ => value
}
def getPathFragment(spec: TablePartitionSpec, partitionColumns: Seq[Attribute]): String = {
getPathFragment(spec, DataTypeUtils.fromAttributes(partitionColumns))
}
/**
* Resolves possible type conflicts between partitions by up-casting "lower" types using
* [[findWiderTypeForPartitionColumn]].
*/
def resolvePartitions(
pathsWithPartitionValues: Seq[(Path, PartitionValues)],
caseSensitive: Boolean): Seq[PartitionValues] = {
if (pathsWithPartitionValues.isEmpty) {
Seq.empty
} else {
val partColNames = if (caseSensitive) {
pathsWithPartitionValues.map(_._2.columnNames)
} else {
pathsWithPartitionValues.map(_._2.columnNames.map(_.toLowerCase()))
}
assert(
partColNames.distinct.size == 1,
listConflictingPartitionColumns(pathsWithPartitionValues))
// Resolves possible type conflicts for each column
val values = pathsWithPartitionValues.map(_._2)
val columnCount = values.head.columnNames.size
val resolvedValues = (0 until columnCount).map { i =>
resolveTypeConflicts(values.map(_.typedValues(i)))
}
// Fills resolved literals back to each partition
values.zipWithIndex.map { case (d, index) =>
d.copy(typedValues = resolvedValues.map(_(index)))
}
}
}
private[datasources] def listConflictingPartitionColumns(
pathWithPartitionValues: Seq[(Path, PartitionValues)]): String = {
val distinctPartColNames = pathWithPartitionValues.map(_._2.columnNames).distinct
def groupByKey[K, V](seq: Seq[(K, V)]): Map[K, Iterable[V]] =
seq.groupBy { case (key, _) => key }.mapValues(_.map { case (_, value) => value }).toMap
val partColNamesToPaths = groupByKey(pathWithPartitionValues.map {
case (path, partValues) => partValues.columnNames -> path
})
val distinctPartColLists = distinctPartColNames.map(_.mkString(", ")).zipWithIndex.map {
case (names, index) =>
s"Partition column name list #$index: $names"
}
// Lists out those non-leaf partition directories that also contain files
val suspiciousPaths = distinctPartColNames.sortBy(_.length).flatMap(partColNamesToPaths)
s"Conflicting partition column names detected:\n" +
distinctPartColLists.mkString("\n\t", "\n\t", "\n\n") +
"For partitioned table directories, data files should only live in leaf directories.\n" +
"And directories at the same level should have the same partition column name.\n" +
"Please check the following directories for unexpected files or " +
"inconsistent partition column names:\n" +
suspiciousPaths.map("\t" + _).mkString("\n", "\n", "")
}
// scalastyle:off line.size.limit
/**
* Converts a string to a [[Literal]] with automatic type inference. Currently only supports
* [[NullType]], [[IntegerType]], [[LongType]], [[DoubleType]], [[DecimalType]], [[DateType]]
* [[TimestampType]], and [[StringType]].
*
* When resolving conflicts, it follows the table below:
*
* +--------------------+-------------------+-------------------+-------------------+--------------------+------------+---------------+---------------+------------+
* | InputA \ InputB | NullType | IntegerType | LongType | DecimalType(38,0)* | DoubleType | DateType | TimestampType | StringType |
* +--------------------+-------------------+-------------------+-------------------+--------------------+------------+---------------+---------------+------------+
* | NullType | NullType | IntegerType | LongType | DecimalType(38,0) | DoubleType | DateType | TimestampType | StringType |
* | IntegerType | IntegerType | IntegerType | LongType | DecimalType(38,0) | DoubleType | StringType | StringType | StringType |
* | LongType | LongType | LongType | LongType | DecimalType(38,0) | StringType | StringType | StringType | StringType |
* | DecimalType(38,0)* | DecimalType(38,0) | DecimalType(38,0) | DecimalType(38,0) | DecimalType(38,0) | StringType | StringType | StringType | StringType |
* | DoubleType | DoubleType | DoubleType | StringType | StringType | DoubleType | StringType | StringType | StringType |
* | DateType | DateType | StringType | StringType | StringType | StringType | DateType | TimestampType | StringType |
* | TimestampType | TimestampType | StringType | StringType | StringType | StringType | TimestampType | TimestampType | StringType |
* | StringType | StringType | StringType | StringType | StringType | StringType | StringType | StringType | StringType |
* +--------------------+-------------------+-------------------+-------------------+--------------------+------------+---------------+---------------+------------+
* Note that, for DecimalType(38,0)*, the table above intentionally does not cover all other
* combinations of scales and precisions because currently we only infer decimal type like
* `BigInteger`/`BigInt`. For example, 1.1 is inferred as double type.
*/
// scalastyle:on line.size.limit
private[datasources] def inferPartitionColumnValue(
raw: String,
typeInference: Boolean,
zoneId: ZoneId,
dateFormatter: DateFormatter,
timestampFormatter: TimestampFormatter): DataType = {
val decimalTry = Try {
// `BigDecimal` conversion can fail when the `field` is not a form of number.
val bigDecimal = new JBigDecimal(raw)
// It reduces the cases for decimals by disallowing values having scale (e.g. `1.1`).
require(bigDecimal.scale <= 0)
// `DecimalType` conversion can fail when
// 1. The precision is bigger than 38.
// 2. scale is bigger than precision.
DecimalType.fromDecimal(Decimal(bigDecimal))
}
val dateTry = Try {
// try and parse the date, if no exception occurs this is a candidate to be resolved as
// DateType
dateFormatter.parse(raw)
// SPARK-23436: Casting the string to date may still return null if a bad Date is provided.
// This can happen since DateFormat.parse may not use the entire text of the given string:
// so if there are extra-characters after the date, it returns correctly.
// We need to check that we can cast the raw string since we later can use Cast to get
// the partition values with the right DataType (see
// org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex.inferPartitioning)
val dateValue = Cast(Literal(raw), DateType, Some(zoneId.getId)).eval()
// Disallow DateType if the cast returned null
require(dateValue != null)
DateType
}
val timestampTry = Try {
val unescapedRaw = unescapePathName(raw)
// try and parse the date, if no exception occurs this is a candidate to be resolved as
// TimestampType or TimestampNTZType. The inference timestamp typ is controlled by the conf
// "spark.sql.timestampType".
val timestampType = conf.timestampType
timestampType match {
case TimestampType => timestampFormatter.parse(unescapedRaw)
case TimestampNTZType => timestampFormatter.parseWithoutTimeZone(unescapedRaw)
}
// SPARK-23436: see comment for date
val timestampValue = Cast(Literal(unescapedRaw), timestampType, Some(zoneId.getId)).eval()
// Disallow TimestampType if the cast returned null
require(timestampValue != null)
timestampType
}
if (typeInference) {
// First tries integral types
Try({ Integer.parseInt(raw); IntegerType })
.orElse(Try { JLong.parseLong(raw); LongType })
.orElse(decimalTry)
// Then falls back to fractional types
.orElse(Try { JDouble.parseDouble(raw); DoubleType })
// Then falls back to date/timestamp types
.orElse(timestampTry)
.orElse(dateTry)
// Then falls back to string
.getOrElse {
if (raw == DEFAULT_PARTITION_NAME) NullType else StringType
}
} else {
if (raw == DEFAULT_PARTITION_NAME) NullType else StringType
}
}
def castPartValueToDesiredType(
desiredType: DataType,
value: String,
zoneId: ZoneId): Any = desiredType match {
case _ if value == DEFAULT_PARTITION_NAME => null
case NullType => null
case StringType => UTF8String.fromString(unescapePathName(value))
case ByteType => Integer.parseInt(value).toByte
case ShortType => Integer.parseInt(value).toShort
case IntegerType => Integer.parseInt(value)
case LongType => JLong.parseLong(value)
case FloatType => JDouble.parseDouble(value).toFloat
case DoubleType => JDouble.parseDouble(value)
case _: DecimalType => Literal(new JBigDecimal(value)).value
case DateType =>
Cast(Literal(value), DateType, Some(zoneId.getId)).eval()
// Timestamp types
case dt if AnyTimestampType.acceptsType(dt) =>
Try {
Cast(Literal(unescapePathName(value)), dt, Some(zoneId.getId)).eval()
}.getOrElse {
Cast(Cast(Literal(value), DateType, Some(zoneId.getId)), dt).eval()
}
case it: AnsiIntervalType =>
Cast(Literal(unescapePathName(value)), it).eval()
case BinaryType => value.getBytes()
case BooleanType => value.toBoolean
case dt => throw QueryExecutionErrors.typeUnsupportedError(dt)
}
def validatePartitionColumn(
schema: StructType,
partitionColumns: Seq[String],
caseSensitive: Boolean): Unit = {
SchemaUtils.checkColumnNameDuplication(partitionColumns, caseSensitive)
partitionColumnsSchema(schema, partitionColumns).foreach {
field => field.dataType match {
case _: AtomicType => // OK
case _ => throw QueryCompilationErrors.cannotUseDataTypeForPartitionColumnError(field)
}
}
if (partitionColumns.nonEmpty && partitionColumns.size == schema.fields.length) {
throw QueryCompilationErrors.cannotUseAllColumnsForPartitionColumnsError()
}
}
def partitionColumnsSchema(
schema: StructType,
partitionColumns: Seq[String]): StructType = {
StructType(partitionColumns.map { col =>
schema.find(f => conf.resolver(f.name, col)).getOrElse {
val schemaCatalog = schema.catalogString
throw QueryCompilationErrors.partitionColumnNotFoundInSchemaError(col, schemaCatalog)
}
}).asNullable
}
def mergeDataAndPartitionSchema(
dataSchema: StructType,
partitionSchema: StructType,
caseSensitive: Boolean): (StructType, Map[String, StructField]) = {
val overlappedPartCols = mutable.Map.empty[String, StructField]
partitionSchema.foreach { partitionField =>
val partitionFieldName = getColName(partitionField, caseSensitive)
if (dataSchema.exists(getColName(_, caseSensitive) == partitionFieldName)) {
overlappedPartCols += partitionFieldName -> partitionField
}
}
// When data and partition schemas have overlapping columns, the output
// schema respects the order of the data schema for the overlapping columns, and it
// respects the data types of the partition schema.
val fullSchema =
StructType(dataSchema.map(f => overlappedPartCols.getOrElse(getColName(f, caseSensitive), f)) ++
partitionSchema.filterNot(f => overlappedPartCols.contains(getColName(f, caseSensitive))))
(fullSchema, overlappedPartCols.toMap)
}
def getColName(f: StructField, caseSensitive: Boolean): String = {
if (caseSensitive) {
f.name
} else {
f.name.toLowerCase(Locale.ROOT)
}
}
/**
* Given a collection of [[Literal]]s, resolves possible type conflicts by
* [[findWiderTypeForPartitionColumn]].
*/
private def resolveTypeConflicts(typedValues: Seq[TypedPartValue]): Seq[TypedPartValue] = {
val dataTypes = typedValues.map(_.dataType)
val desiredType = dataTypes.reduce(findWiderTypeForPartitionColumn)
typedValues.map(tv => tv.copy(dataType = desiredType))
}
/**
* Type widening rule for partition column types. It is similar to
* [[TypeCoercion.findWiderTypeForTwo]] but the main difference is that here we disallow
* precision loss when widening double/long and decimal, and fall back to string.
*/
private val findWiderTypeForPartitionColumn: (DataType, DataType) => DataType = {
case (DoubleType, _: DecimalType) | (_: DecimalType, DoubleType) => StringType
case (DoubleType, LongType) | (LongType, DoubleType) => StringType
case (t1, t2) => TypeCoercion.findWiderTypeForTwo(t1, t2).getOrElse(StringType)
}
}
