Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/1351#discussion_r14804578
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/csv/CsvRDD.scala ---
@@ -0,0 +1,91 @@
+/*
+ * 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.csv
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.types._
+import org.apache.spark.sql.catalyst.expressions.{GenericMutableRow,
AttributeReference, Row}
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.execution.{ExistingRdd, SparkLogicalPlan}
+import org.apache.spark.sql.Logging
+
+private[sql] object CsvRDD extends Logging {
+
+ private[sql] def inferSchema(
+ csv: RDD[String],
+ delimiter: String,
+ quote: String,
+ useHeader: Boolean): LogicalPlan = {
+
+ // Constructing schema
+ // TODO: Infer types based on a sample and/or let user specify
types/schema
+ val firstLine = csv.first()
+ // Assuming first row is representative and using it to determine
number of fields
+ val firstRow = new CsvTokenizer(Seq(firstLine).iterator, delimiter,
quote).next()
+ val header = if (useHeader) {
+ firstRow
+ } else {
+ firstRow.zipWithIndex.map { case (value, index) => s"V$index" }
+ }
+
+ val schemaFields = header.map { fieldName =>
+ StructField(fieldName.asInstanceOf[String], StringType, nullable =
true)
+ }
+ val schema = StructType(schemaFields)
+
+ val parsedCSV = csv.mapPartitions { iter =>
+ // When using header, any input line that equals firstLine is
assumed to be header
+ val csvIter = if (useHeader) {
+ iter.filter(_ != firstLine)
+ } else {
+ iter
+ }
+ val tokenIter = new CsvTokenizer(csvIter, delimiter, quote)
+ parseCSV(tokenIter, schema)
+ }
+
+ SparkLogicalPlan(ExistingRdd(asAttributes(schema), parsedCSV))
+ }
+
+ private def castToType(value: Any, dataType: DataType): Any = dataType
match {
--- End diff --
Also we need to add tests for casting. Right now I'm pretty sure this just
throws `ClassCastException`s for non-strings.
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