[SPARK-16496][SQL] Add wholetext as option for reading text in SQL.

python/pyspark/sql/readwriter.py

@@ -304,7 +304,7 @@ def parquet(self, *paths):
 
     @ignore_unicode_prefix
     @since(1.6)
-    def text(self, paths):
+    def text(self, paths, wholetext=False):
         """
         Loads text files and returns a :class:`DataFrame` whose schema starts with a
         string column named "value", and followed by partitioned columns if there
@@ -313,11 +313,16 @@ def text(self, paths):
         Each line in the text file is a new row in the resulting DataFrame.
 
         :param paths: string, or list of strings, for input path(s).
+        :param wholetext: if true, read each file from input path(s) as a single row.
 
         >>> df = spark.read.text('python/test_support/sql/text-test.txt')
         >>> df.collect()
         [Row(value=u'hello'), Row(value=u'this')]
+        >>> df = spark.read.text('python/test_support/sql/text-test.txt', wholetext=True)
+        >>> df.collect()
+        [Row(value=u'hello\nthis')]
         """
+        self._set_opts(wholetext=wholetext)
         if isinstance(paths, basestring):
             paths = [paths]
         return self._df(self._jreader.text(self._spark._sc._jvm.PythonUtils.toSeq(paths)))

sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala

@@ -646,7 +646,14 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
    * Loads text files and returns a `DataFrame` whose schema starts with a string column named
    * "value", and followed by partitioned columns if there are any.
    *
-   * Each line in the text files is a new row in the resulting DataFrame. For example:
+   * You can set the following text-specific option(s) for reading text files:
+   * <ul>
+   * <li>`wholetext` ( default `false`): If true, read a file as a single row and not split by "\n".
+   * </li>
+   * </ul>
+   * By default, each line in the text files is a new row in the resulting DataFrame.
+   *
+   * Usage example:
    * {{{
    *   // Scala:
    *   spark.read.text("/path/to/spark/README.md")
@@ -678,7 +685,12 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
    * If the directory structure of the text files contains partitioning information, those are
    * ignored in the resulting Dataset. To include partitioning information as columns, use `text`.
    *
-   * Each line in the text files is a new element in the resulting Dataset. For example:
+   * You can set the following textFile-specific option(s) for reading text files:
+   * <ul>
+   * <li>`wholetext` ( default `false`): If true, read a file as a single row and not split by "\n".
+   * </li>
+   * </ul>
+   * By default, each line in the text files is a new row in the resulting DataFrame. For example:
    * {{{
    *   // Scala:
    *   spark.read.textFile("/path/to/spark/README.md")

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/HadoopFileWholeTextReader.scala

@@ -0,0 +1,57 @@
+/*
+ * 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
+
+import java.io.Closeable
+import java.net.URI
+
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.Path
+import org.apache.hadoop.io.Text
+import org.apache.hadoop.mapreduce._
+import org.apache.hadoop.mapreduce.lib.input.CombineFileSplit
+import org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
+
+import org.apache.spark.input.WholeTextFileRecordReader
+
+/**
+ * An adaptor from a [[PartitionedFile]] to an [[Iterator]] of [[Text]], which is all of the lines
+ * in that file.
+ */
+class HadoopFileWholeTextReader(file: PartitionedFile, conf: Configuration)
+  extends Iterator[Text] with Closeable {
+  private val iterator = {
+    val fileSplit = new CombineFileSplit(
+      Array(new Path(new URI(file.filePath))),
+      Array(file.start),
+      Array(file.length),
+      // TODO: Implement Locality
+      Array.empty[String])
+    val attemptId = new TaskAttemptID(new TaskID(new JobID(), TaskType.MAP, 0), 0)
+    val hadoopAttemptContext = new TaskAttemptContextImpl(conf, attemptId)
+    val reader = new WholeTextFileRecordReader(fileSplit, hadoopAttemptContext, 0)
+    reader.initialize(fileSplit, hadoopAttemptContext)
+    new RecordReaderIterator(reader)
+  }
+
+  override def hasNext: Boolean = iterator.hasNext
+
+  override def next(): Text = iterator.next()
+
+  override def close(): Unit = iterator.close()
+}

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/text/TextFileFormat.scala

@@ -17,12 +17,16 @@
 
 package org.apache.spark.sql.execution.datasources.text
 
+import java.io.Closeable
+
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hadoop.io.Text
 import org.apache.hadoop.mapreduce.{Job, TaskAttemptContext}
 
 import org.apache.spark.TaskContext
-import org.apache.spark.sql.{AnalysisException, Row, SparkSession}
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.sql.{AnalysisException, SparkSession}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.UnsafeRow
 import org.apache.spark.sql.catalyst.expressions.codegen.{BufferHolder, UnsafeRowWriter}
@@ -53,6 +57,14 @@ class TextFileFormat extends TextBasedFileFormat with DataSourceRegister {
     }
   }
 
+  override def isSplitable(
+      sparkSession: SparkSession,
+      options: Map[String, String],
+      path: Path): Boolean = {
+    val textOptions = new TextOptions(options)
+    super.isSplitable(sparkSession, options, path) && !textOptions.wholeText
+  }
+
   override def inferSchema(
       sparkSession: SparkSession,
       options: Map[String, String],
@@ -97,14 +109,25 @@ class TextFileFormat extends TextBasedFileFormat with DataSourceRegister {
     assert(
       requiredSchema.length <= 1,
       "Text data source only produces a single data column named \"value\".")
-
+    val textOptions = new TextOptions(options)
     val broadcastedHadoopConf =
       sparkSession.sparkContext.broadcast(new SerializableConfiguration(hadoopConf))
 
+    readToUnsafeMem(broadcastedHadoopConf, requiredSchema, textOptions.wholeText)
+  }
+
+  private def readToUnsafeMem(conf: Broadcast[SerializableConfiguration],
+      requiredSchema: StructType, wholeTextMode: Boolean):
+  (PartitionedFile) => Iterator[UnsafeRow] = {
+
     (file: PartitionedFile) => {
-      val reader = new HadoopFileLinesReader(file, broadcastedHadoopConf.value.value)
+      val confValue = conf.value.value
+      val reader = if (!wholeTextMode) {
+        new HadoopFileLinesReader(file, confValue)
+      } else {
+        new HadoopFileWholeTextReader(file, confValue)
+      }
       Option(TaskContext.get()).foreach(_.addTaskCompletionListener(_ => reader.close()))
-
       if (requiredSchema.isEmpty) {
         val emptyUnsafeRow = new UnsafeRow(0)
         reader.map(_ => emptyUnsafeRow)

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/text/TextOptions.scala

@@ -33,8 +33,15 @@ private[text] class TextOptions(@transient private val parameters: CaseInsensiti
    * Compression codec to use.
    */
   val compressionCodec = parameters.get(COMPRESSION).map(CompressionCodecs.getCodecClassName)
+
+  /**
+   * wholetext - If true, read a file as a single row and not split by "\n".
+   */
+  val wholeText = parameters.getOrElse(WHOLETEXT, "false").toBoolean
+
 }
 
 private[text] object TextOptions {
   val COMPRESSION = "compression"
+  val WHOLETEXT = "wholetext"
 }

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/text/TextSuite.scala

@@ -39,6 +39,54 @@ class TextSuite extends QueryTest with SharedSQLContext {
     verifyFrame(spark.read.text(testFile))
   }
 
+  test("reading text file with option wholetext=true") {
+    val df = spark.read.option("wholetext", "true")
+      .format("text").load(testFile)
+    // schema
+    assert(df.schema == new StructType().add("value", StringType))
+
+    // verify content
+    val data = df.collect()
+    assert(data(0) ==
+      Row(
+        // scalastyle:off nonascii
+        """This is a test file for the text data source
+          |1+1
+          |数据砖头
+          |"doh"
+          |""".stripMargin))
+    // scalastyle:on nonascii
+    assert(data.length == 1)
+  }
+
+  test("reading multiple text files with option wholetext=true") {
+    import org.apache.spark.sql.catalyst.util._
+    withTempDir { dir =>
+      val file1 = new File(dir, "text1.txt")
+      stringToFile(file1,
+        """text file 1 contents.
+          |From: None to: ??
+        """.stripMargin)
+      val file2 = new File(dir, "text2.txt")
+      stringToFile(file2, "text file 2 contents.")
+      val file3 = new File(dir, "text3.txt")
+      stringToFile(file3, "text file 3 contents.")
+      val df = spark.read.option("wholetext", "true").text(dir.getAbsolutePath)
+      // Since wholetext option reads each file into a single row, df.length should be no. of files.
+      val data = df.sort("value").collect()
+      assert(data.length == 3)
+      // Each files should represent a single Row/element in Dataframe/Dataset
+      assert(data(0) == Row(
+        """text file 1 contents.
+          |From: None to: ??
+        """.stripMargin))
+      assert(data(1) == Row(
+        """text file 2 contents.""".stripMargin))
+      assert(data(2) == Row(
+        """text file 3 contents.""".stripMargin))
+    }
+  }
+
   test("SPARK-12562 verify write.text() can handle column name beyond `value`") {
     val df = spark.read.text(testFile).withColumnRenamed("value", "adwrasdf")
 
@@ -185,10 +233,9 @@ class TextSuite extends QueryTest with SharedSQLContext {
     val data = df.collect()
     assert(data(0) == Row("This is a test file for the text data source"))
     assert(data(1) == Row("1+1"))
-    // non ascii characters are not allowed in the code, so we disable the scalastyle here.
-    // scalastyle:off
+    // scalastyle:off nonascii
     assert(data(2) == Row("数据砖头"))
-    // scalastyle:on
+    // scalastyle:on nonascii
     assert(data(3) == Row("\"doh\""))
     assert(data.length == 4)
   }