Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/22379#discussion_r225190224
--- 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 --
Looks rows can't be more then one in this CSV's code path specifically.
See below:
```scala
val rawParser = new UnivocityParser(actualSchema, actualSchema,
parsedOptions)
new FailureSafeParser[String](
input => Seq(rawParser.parse(input)),
mode,
nullableSchema,
parsedOptions.columnNameOfCorruptRecord,
parsedOptions.multiLine)
```
Univocity parser:
```scala
def parse(input: String): InternalRow =
convert(tokenizer.parseLine(input))
```
and in the `FailureSafeParser`
```scala
class FailureSafeParser[IN](
rawParser: IN => Seq[InternalRow],
mode: ParseMode,
schema: StructType,
columnNameOfCorruptRecord: String,
isMultiLine: Boolean) {
```
```scala
def parse(input: IN): Iterator[InternalRow] = {
try {
if (skipParsing) {
Iterator.single(InternalRow.empty)
} else {
rawParser.apply(input).toIterator.map(row => toResultRow(Some(row),
() => null))
}
} catch {
case e: BadRecordException => mode match {
case PermissiveMode =>
Iterator(toResultRow(e.partialResult(), e.record))
case DropMalformedMode =>
Iterator.empty
case FailFastMode =>
throw new SparkException("Malformed records are detected in
record parsing. " +
s"Parse Mode: ${FailFastMode.name}.", e.cause)
}
}
}
```
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