Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/10615#discussion_r49377643
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala
---
@@ -0,0 +1,228 @@
+/*
+ * 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.csv
+
+import java.math.BigDecimal
+import java.sql.{Date, Timestamp}
+import java.text.NumberFormat
+import java.util.Locale
+
+
+import scala.util.Try
+import scala.util.control.Exception._
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion
+
+private[sql] object CSVInferSchema {
+
+ /**
+ * Similar to the JSON schema inference
+ * 1. Infer type of each row
+ * 2. Merge row types to find common type
+ * 3. Replace any null types with string type
+ * TODO(hossein): Can we reuse JSON schema inference? [SPARK-12670]
+ */
+ def apply(
+ tokenRdd: RDD[Array[String]],
+ header: Array[String],
+ nullValue: String = ""): StructType = {
+
+ val startType: Array[DataType] =
Array.fill[DataType](header.length)(NullType)
+ val rootTypes: Array[DataType] =
+ tokenRdd.aggregate(startType)(inferRowType(nullValue), mergeRowTypes)
+
+ val structFields = header.zip(rootTypes).map { case (thisHeader,
rootType) =>
+ StructField(thisHeader, rootType, nullable = true)
+ }
+
+ StructType(structFields)
+ }
+
+ private def inferRowType(nullValue: String)
+ (rowSoFar: Array[DataType], next: Array[String]): Array[DataType] = {
+ var i = 0
+ while (i < math.min(rowSoFar.length, next.length)) { // May have
columns on right missing.
+ rowSoFar(i) = inferField(rowSoFar(i), next(i), nullValue)
+ i+=1
+ }
+ rowSoFar
+ }
+
+ private[csv] def mergeRowTypes(
+ first: Array[DataType],
+ second: Array[DataType]): Array[DataType] = {
+
+ first.zipAll(second, NullType, NullType).map { case ((a, b)) =>
+ val tpe = findTightestCommonType(a, b).getOrElse(StringType)
+ tpe match {
+ case _: NullType => StringType
+ case other => other
+ }
+ }
+ }
+
+ /**
+ * Infer type of string field. Given known type Double, and a string
"1", there is no
+ * point checking if it is an Int, as the final type must be Double or
higher.
+ */
+ private[csv] def inferField(
+ typeSoFar: DataType, field: String, nullValue: String = ""):
DataType = {
+ if (field == null || field.isEmpty || field == nullValue) {
+ typeSoFar
+ } else {
+ typeSoFar match {
+ case NullType => tryParseInteger(field)
+ case IntegerType => tryParseInteger(field)
+ case LongType => tryParseLong(field)
+ case DoubleType => tryParseDouble(field)
+ case TimestampType => tryParseTimestamp(field)
+ case StringType => StringType
+ case other: DataType =>
+ throw new UnsupportedOperationException(s"Unexpected data type
$other")
+ }
+ }
+ }
+
+
+ private def tryParseInteger(field: String): DataType = if ((allCatch opt
field.toInt).isDefined) {
+ IntegerType
+ } else {
+ tryParseLong(field)
+ }
+
+ private def tryParseLong(field: String): DataType = if ((allCatch opt
field.toLong).isDefined) {
+ LongType
+ } else {
+ tryParseDouble(field)
+ }
+
+ private def tryParseDouble(field: String): DataType = {
+ if ((allCatch opt field.toDouble).isDefined) {
+ DoubleType
+ } else {
+ tryParseTimestamp(field)
+ }
+ }
+
+ def tryParseTimestamp(field: String): DataType = {
+ if ((allCatch opt Timestamp.valueOf(field)).isDefined) {
+ TimestampType
+ } else {
+ stringType()
+ }
+ }
+
+ // Defining a function to return the StringType constant is necessary in
order to work around
+ // a Scala compiler issue which leads to runtime incompatibilities with
certain Spark versions;
+ // see issue #128 for more details.
+ private def stringType(): DataType = {
+ StringType
+ }
+
+ private val numericPrecedence: IndexedSeq[DataType] =
HiveTypeCoercion.numericPrecedence
+
+ /**
+ * Copied from internal Spark api
+ * [[org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion]]
+ */
+ val findTightestCommonType: (DataType, DataType) => Option[DataType] = {
+ case (t1, t2) if t1 == t2 => Some(t1)
+ case (NullType, t1) => Some(t1)
+ case (t1, NullType) => Some(t1)
+
+ // Promote numeric types to the highest of the two and all numeric
types to unlimited decimal
+ case (t1, t2) if Seq(t1, t2).forall(numericPrecedence.contains) =>
+ val index = numericPrecedence.lastIndexWhere(t => t == t1 || t == t2)
+ Some(numericPrecedence(index))
+
+ case _ => None
+ }
+}
+
+object CSVTypeCast {
+
+ /**
+ * Casts given string datum to specified type.
+ * Currently we do not support complex types (ArrayType, MapType,
StructType).
+ *
+ * For string types, this is simply the datum. For other types.
+ * For other nullable types, this is null if the string datum is empty.
+ *
+ * @param datum string value
+ * @param castType SparkSQL type
+ */
+ private[csv] def castTo(
--- End diff --
We should probably do this with expressions now that we are in Spark.
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