Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/13988#discussion_r94756023
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/UnivocityParser.scala
---
@@ -0,0 +1,272 @@
+/*
+ * 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.text.NumberFormat
+import java.util.Locale
+
+import scala.util.Try
+import scala.util.control.NonFatal
+
+import com.univocity.parsers.csv.CsvParser
+
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.GenericInternalRow
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
+
+private[csv] class UnivocityParser(
+ schema: StructType,
+ requiredSchema: StructType,
+ options: CSVOptions) extends Logging {
+ def this(schema: StructType, options: CSVOptions) = this(schema, schema,
options)
+
+ val valueConverters = makeConverters(schema, options)
+ val parser = new CsvParser(options.asParserSettings)
+
+ // A `ValueConverter` is responsible for converting the given value to a
desired type.
+ private type ValueConverter = String => Any
+
+ var numMalformedRecords = 0
+ val row = new GenericInternalRow(requiredSchema.length)
+ val indexArr: Array[Int] = {
+ val fields = if (options.dropMalformed) {
+ // If `dropMalformed` is enabled, then it needs to parse all the
values
+ // so that we can decide which row is malformed.
+ requiredSchema ++ schema.filterNot(requiredSchema.contains(_))
+ } else {
+ requiredSchema
+ }
+ fields.filter(schema.contains).map(schema.indexOf).toArray
+ }
+
+ /**
+ * Create converters which cast each given string datum to each
specified type in given schema.
+ * Currently, we do not support complex types (`ArrayType`, `MapType`,
`StructType`).
+ *
+ * For string types, this is simply the datum.
+ * For other types, this is converted into the value according to the
type.
+ * For other nullable types, returns null if it is null or equals to the
value specified
+ * in `nullValue` option.
+ *
+ * @param schema schema that contains data types to cast the given value
into.
+ * @param options CSV options.
+ */
+ private def makeConverters(
+ schema: StructType,
+ options: CSVOptions = CSVOptions()): Array[ValueConverter] = {
+ schema.map(f => makeConverter(f.name, f.dataType, f.nullable,
options)).toArray
+ }
+
+ /**
+ * Create a converter which converts the string value to a value
according to a desired type.
+ */
+ def makeConverter(
+ name: String,
+ dataType: DataType,
+ nullable: Boolean = true,
+ options: CSVOptions = CSVOptions()): ValueConverter = dataType match
{
+ case _: ByteType => (d: String) =>
+ nullSafeDatum(d, name, nullable, options)(_.toByte)
+
+ case _: ShortType => (d: String) =>
+ nullSafeDatum(d, name, nullable, options)(_.toShort)
+
+ case _: IntegerType => (d: String) =>
+ nullSafeDatum(d, name, nullable, options)(_.toInt)
+
+ case _: LongType => (d: String) =>
+ nullSafeDatum(d, name, nullable, options)(_.toLong)
+
+ case _: FloatType => (d: String) =>
+ nullSafeDatum(d, name, nullable, options) {
+ case options.nanValue => Float.NaN
+ case options.negativeInf => Float.NegativeInfinity
+ case options.positiveInf => Float.PositiveInfinity
+ case datum =>
+ Try(datum.toFloat)
+
.getOrElse(NumberFormat.getInstance(Locale.US).parse(datum).floatValue())
+ }
+
+ case _: DoubleType => (d: String) =>
+ nullSafeDatum(d, name, nullable, options) {
+ case options.nanValue => Double.NaN
+ case options.negativeInf => Double.NegativeInfinity
+ case options.positiveInf => Double.PositiveInfinity
+ case datum =>
+ Try(datum.toDouble)
+
.getOrElse(NumberFormat.getInstance(Locale.US).parse(datum).doubleValue())
+ }
+
+ case _: BooleanType => (d: String) =>
+ nullSafeDatum(d, name, nullable, options)(_.toBoolean)
+
+ case dt: DecimalType => (d: String) =>
+ nullSafeDatum(d, name, nullable, options) { datum =>
+ val value = new BigDecimal(datum.replaceAll(",", ""))
+ Decimal(value, dt.precision, dt.scale)
+ }
+
+ case _: TimestampType => (d: String) =>
+ nullSafeDatum(d, name, nullable, options) { datum =>
+ // This one will lose microseconds parts.
+ // See https://issues.apache.org/jira/browse/SPARK-10681.
+ Try(options.timestampFormat.parse(datum).getTime * 1000L)
+ .getOrElse {
+ // If it fails to parse, then tries the way used in 2.0 and 1.x
for backwards
+ // compatibility.
+ DateTimeUtils.stringToTime(datum).getTime * 1000L
+ }
+ }
+
+ case _: DateType => (d: String) =>
+ nullSafeDatum(d, name, nullable, options) { datum =>
+ // This one will lose microseconds parts.
+ // See https://issues.apache.org/jira/browse/SPARK-10681.x
+
Try(DateTimeUtils.millisToDays(options.dateFormat.parse(datum).getTime))
+ .getOrElse {
+ // If it fails to parse, then tries the way used in 2.0 and 1.x
for backwards
+ // compatibility.
+
DateTimeUtils.millisToDays(DateTimeUtils.stringToTime(datum).getTime)
+ }
+ }
+
+ case _: StringType => (d: String) =>
+ nullSafeDatum(d, name, nullable, options)(UTF8String.fromString(_))
+
+ case udt: UserDefinedType[_] => (datum: String) =>
+ makeConverter(name, udt.sqlType, nullable, options)
+
+ case _ => throw new RuntimeException(s"Unsupported type:
${dataType.typeName}")
+ }
+
+ private def nullSafeDatum(
+ datum: String,
+ name: String,
+ nullable: Boolean,
+ options: CSVOptions)(converter: ValueConverter): Any = {
+ if (datum == options.nullValue || datum == null) {
+ if (!nullable) {
+ throw new RuntimeException(s"null value found but field $name is
not nullable.")
+ }
+ null
+ } else {
+ converter.apply(datum)
+ }
+ }
+
+ /**
+ * Parses a single CSV record (in the form of an array of strings in
which
+ * each element represents a column) and turns it into either one
resulting row or no row (if the
+ * the record is malformed).
+ */
+ def parse(input: String): Option[InternalRow] = {
--- End diff --
Actually, the argument change (matching it up to `JacksonParser`) is also
important. We could avoid additional refactoring when introducing the same
funtionalities with `JacksonParser`, (e.g., `from_json` and `to_json`
functions).
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