Github user HyukjinKwon commented on a diff in the pull request:

    https://github.com/apache/spark/pull/22379#discussion_r225186492
  
    --- Diff: 
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/csvExpressions.scala
 ---
    @@ -0,0 +1,117 @@
    +/*
    + * 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.catalyst.expressions
    +
    +import org.apache.spark.sql.AnalysisException
    +import org.apache.spark.sql.catalyst.InternalRow
    +import org.apache.spark.sql.catalyst.csv._
    +import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
    +import org.apache.spark.sql.catalyst.util._
    +import org.apache.spark.sql.types._
    +import org.apache.spark.unsafe.types.UTF8String
    +
    +/**
    + * Converts a CSV input string to a [[StructType]] with the specified 
schema.
    + */
    +// scalastyle:off line.size.limit
    +@ExpressionDescription(
    +  usage = "_FUNC_(csvStr, schema[, options]) - Returns a struct value with 
the given `csvStr` and `schema`.",
    +  examples = """
    +    Examples:
    +      > SELECT _FUNC_('1, 0.8', 'a INT, b DOUBLE');
    +       {"a":1, "b":0.8}
    +      > SELECT _FUNC_('26/08/2015', 'time Timestamp', 
map('timestampFormat', 'dd/MM/yyyy'))
    +       {"time":2015-08-26 00:00:00.0}
    +  """,
    +  since = "3.0.0")
    +// scalastyle:on line.size.limit
    +case class CsvToStructs(
    +    schema: StructType,
    +    options: Map[String, String],
    +    child: Expression,
    +    timeZoneId: Option[String] = None)
    +  extends UnaryExpression
    +    with TimeZoneAwareExpression
    +    with CodegenFallback
    +    with ExpectsInputTypes
    +    with NullIntolerant {
    +
    +  override def nullable: Boolean = child.nullable
    +
    +  // The CSV input data might be missing certain fields. We force the 
nullability
    +  // of the user-provided schema to avoid data corruptions.
    +  val nullableSchema: StructType = schema.asNullable
    +
    +  // Used in `FunctionRegistry`
    +  def this(child: Expression, schema: Expression, options: Map[String, 
String]) =
    +    this(
    +      schema = ExprUtils.evalSchemaExpr(schema),
    +      options = options,
    +      child = child,
    +      timeZoneId = None)
    +
    +  def this(child: Expression, schema: Expression) = this(child, schema, 
Map.empty[String, String])
    +
    +  def this(child: Expression, schema: Expression, options: Expression) =
    +    this(
    +      schema = ExprUtils.evalSchemaExpr(schema),
    +      options = ExprUtils.convertToMapData(options),
    +      child = child,
    +      timeZoneId = None)
    +
    +  // This converts parsed rows to the desired output by the given schema.
    +  @transient
    +  lazy val converter = (rows: Iterator[InternalRow]) => {
    +    if (rows.hasNext) {
    +      rows.next()
    --- End diff --
    
    Oops, I missed. Let me check


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