[SPARK-14295][SPARK-14274][SQL] Implements buildReader() for LibSVM

## What changes were proposed in this pull request?

This PR implements `FileFormat.buildReader()` for the LibSVM data source. Besides that, a new interface method `prepareRead()` is added to `FileFormat`:

```scala
  def prepareRead(
      sqlContext: SQLContext,
      options: Map[String, String],
      files: Seq[FileStatus]): Map[String, String] = options
```

After migrating from `buildInternalScan()` to `buildReader()`, we lost the opportunity to collect necessary global information, since `buildReader()` works in a per-partition manner. For example, LibSVM needs to infer the total number of features if the `numFeatures` data source option is not set. Any necessary collected global information should be returned using the data source options map. By default, this method just returns the original options untouched.

An alternative approach is to absorb `inferSchema()` into `prepareRead()`, since schema inference is also some kind of global information gathering. However, this approach wasn't chosen because schema inference is optional, while `prepareRead()` must be called whenever a `HadoopFsRelation` based data source relation is instantiated.

One unaddressed problem is that, when `numFeatures` is absent, now the input data will be scanned twice. The `buildInternalScan()` code path doesn't need to do this because it caches the raw parsed RDD in memory before computing the total number of features. However, with `FileScanRDD`, the raw parsed RDD is created in a different way (e.g. partitioning) from the final RDD.

## How was this patch tested?

Tested using existing test suites.

Author: Cheng Lian <lian@databricks.com>

Closes #12088 from liancheng/spark-14295-libsvm-build-reader.

mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala

@@ -19,6 +19,7 @@ package org.apache.spark.ml.source.libsvm
 
 import java.io.IOException
 
+import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileStatus, Path}
 import org.apache.hadoop.io.{NullWritable, Text}
 import org.apache.hadoop.mapreduce.{Job, RecordWriter, TaskAttemptContext}
@@ -26,12 +27,16 @@ import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat
 
 import org.apache.spark.annotation.Since
 import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.mllib.linalg.{Vector, Vectors, VectorUDT}
+import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, DataFrameReader, Row, SQLContext}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.encoders.RowEncoder
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference, JoinedRow}
+import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection
+import org.apache.spark.sql.execution.datasources.{CaseInsensitiveMap, HadoopFileLinesReader, PartitionedFile}
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
 import org.apache.spark.util.SerializableConfiguration
@@ -110,13 +115,16 @@ class DefaultSource extends FileFormat with DataSourceRegister {
   @Since("1.6.0")
   override def shortName(): String = "libsvm"
 
+  override def toString: String = "LibSVM"
+
   private def verifySchema(dataSchema: StructType): Unit = {
     if (dataSchema.size != 2 ||
       (!dataSchema(0).dataType.sameType(DataTypes.DoubleType)
         || !dataSchema(1).dataType.sameType(new VectorUDT()))) {
-      throw new IOException(s"Illegal schema for libsvm data, schema=${dataSchema}")
+      throw new IOException(s"Illegal schema for libsvm data, schema=$dataSchema")
     }
   }
+
   override def inferSchema(
       sqlContext: SQLContext,
       options: Map[String, String],
@@ -127,6 +135,32 @@ class DefaultSource extends FileFormat with DataSourceRegister {
         StructField("features", new VectorUDT(), nullable = false) :: Nil))
   }
 
+  override def prepareRead(
+      sqlContext: SQLContext,
+      options: Map[String, String],
+      files: Seq[FileStatus]): Map[String, String] = {
+    def computeNumFeatures(): Int = {
+      val dataFiles = files.filterNot(_.getPath.getName startsWith "_")
+      val path = if (dataFiles.length == 1) {
+        dataFiles.head.getPath.toUri.toString
+      } else if (dataFiles.isEmpty) {
+        throw new IOException("No input path specified for libsvm data")
+      } else {
+        throw new IOException("Multiple input paths are not supported for libsvm data.")
+      }
+
+      val sc = sqlContext.sparkContext
+      val parsed = MLUtils.parseLibSVMFile(sc, path, sc.defaultParallelism)
+      MLUtils.computeNumFeatures(parsed)
+    }
+
+    val numFeatures = options.get("numFeatures").filter(_.toInt > 0).getOrElse {
+      computeNumFeatures()
+    }
+
+    new CaseInsensitiveMap(options + ("numFeatures" -> numFeatures.toString))
+  }
+
   override def prepareWrite(
       sqlContext: SQLContext,
       job: Job,
@@ -158,7 +192,7 @@ class DefaultSource extends FileFormat with DataSourceRegister {
     verifySchema(dataSchema)
     val dataFiles = inputFiles.filterNot(_.getPath.getName startsWith "_")
 
-    val path = if (dataFiles.length == 1) dataFiles(0).getPath.toUri.toString
+    val path = if (dataFiles.length == 1) dataFiles.head.getPath.toUri.toString
     else if (dataFiles.isEmpty) throw new IOException("No input path specified for libsvm data")
     else throw new IOException("Multiple input paths are not supported for libsvm data.")
 
@@ -176,4 +210,51 @@ class DefaultSource extends FileFormat with DataSourceRegister {
       externalRows.map(converter.toRow)
     }
   }
+
+  override def buildReader(
+      sqlContext: SQLContext,
+      dataSchema: StructType,
+      partitionSchema: StructType,
+      requiredSchema: StructType,
+      filters: Seq[Filter],
+      options: Map[String, String]): (PartitionedFile) => Iterator[InternalRow] = {
+    val numFeatures = options("numFeatures").toInt
+    assert(numFeatures > 0)
+
+    val sparse = options.getOrElse("vectorType", "sparse") == "sparse"
+
+    val broadcastedConf = sqlContext.sparkContext.broadcast(
+      new SerializableConfiguration(new Configuration(sqlContext.sparkContext.hadoopConfiguration))
+    )
+
+    (file: PartitionedFile) => {
+      val points =
+        new HadoopFileLinesReader(file, broadcastedConf.value.value)
+          .map(_.toString.trim)
+          .filterNot(line => line.isEmpty || line.startsWith("#"))
+          .map { line =>
+            val (label, indices, values) = MLUtils.parseLibSVMRecord(line)
+            LabeledPoint(label, Vectors.sparse(numFeatures, indices, values))
+          }
+
+      val converter = RowEncoder(requiredSchema)
+
+      val unsafeRowIterator = points.map { pt =>
+        val features = if (sparse) pt.features.toSparse else pt.features.toDense
+        converter.toRow(Row(pt.label, features))
+      }
+
+      def toAttribute(f: StructField): AttributeReference =
+        AttributeReference(f.name, f.dataType, f.nullable, f.metadata)()
+
+      // Appends partition values
+      val fullOutput = (requiredSchema ++ partitionSchema).map(toAttribute)
+      val joinedRow = new JoinedRow()
+      val appendPartitionColumns = GenerateUnsafeProjection.generate(fullOutput, fullOutput)
+
+      unsafeRowIterator.map { dataRow =>
+        appendPartitionColumns(joinedRow(dataRow, file.partitionValues))
+      }
+    }
+  }
 }

mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala

@@ -67,42 +67,14 @@ object MLUtils {
       path: String,
       numFeatures: Int,
       minPartitions: Int): RDD[LabeledPoint] = {
-    val parsed = sc.textFile(path, minPartitions)
-      .map(_.trim)
-      .filter(line => !(line.isEmpty || line.startsWith("#")))
-      .map { line =>
-        val items = line.split(' ')
-        val label = items.head.toDouble
-        val (indices, values) = items.tail.filter(_.nonEmpty).map { item =>
-          val indexAndValue = item.split(':')
-          val index = indexAndValue(0).toInt - 1 // Convert 1-based indices to 0-based.
-          val value = indexAndValue(1).toDouble
-          (index, value)
-        }.unzip
-
-        // check if indices are one-based and in ascending order
-        var previous = -1
-        var i = 0
-        val indicesLength = indices.length
-        while (i < indicesLength) {
-          val current = indices(i)
-          require(current > previous, s"indices should be one-based and in ascending order;"
-            + " found current=$current, previous=$previous; line=\"$line\"")
-          previous = current
-          i += 1
-        }
-
-        (label, indices.toArray, values.toArray)
-      }
+    val parsed = parseLibSVMFile(sc, path, minPartitions)
 
     // Determine number of features.
     val d = if (numFeatures > 0) {
       numFeatures
     } else {
       parsed.persist(StorageLevel.MEMORY_ONLY)
-      parsed.map { case (label, indices, values) =>
-        indices.lastOption.getOrElse(0)
-      }.reduce(math.max) + 1
+      computeNumFeatures(parsed)
     }
 
     parsed.map { case (label, indices, values) =>
@@ -110,6 +82,47 @@ object MLUtils {
     }
   }
 
+  private[spark] def computeNumFeatures(rdd: RDD[(Double, Array[Int], Array[Double])]): Int = {
+    rdd.map { case (label, indices, values) =>
+      indices.lastOption.getOrElse(0)
+    }.reduce(math.max) + 1
+  }
+
+  private[spark] def parseLibSVMFile(
+      sc: SparkContext,
+      path: String,
+      minPartitions: Int): RDD[(Double, Array[Int], Array[Double])] = {
+    sc.textFile(path, minPartitions)
+      .map(_.trim)
+      .filter(line => !(line.isEmpty || line.startsWith("#")))
+      .map(parseLibSVMRecord)
+  }
+
+  private[spark] def parseLibSVMRecord(line: String): (Double, Array[Int], Array[Double]) = {
+    val items = line.split(' ')
+    val label = items.head.toDouble
+    val (indices, values) = items.tail.filter(_.nonEmpty).map { item =>
+      val indexAndValue = item.split(':')
+      val index = indexAndValue(0).toInt - 1 // Convert 1-based indices to 0-based.
+    val value = indexAndValue(1).toDouble
+      (index, value)
+    }.unzip
+
+    // check if indices are one-based and in ascending order
+    var previous = -1
+    var i = 0
+    val indicesLength = indices.length
+    while (i < indicesLength) {
+      val current = indices(i)
+      require(current > previous, s"indices should be one-based and in ascending order;"
+        + " found current=$current, previous=$previous; line=\"$line\"")
+      previous = current
+      i += 1
+    }
+
+    (label, indices, values)
+  }
+
   /**
    * Loads labeled data in the LIBSVM format into an RDD[LabeledPoint], with the default number of
    * partitions.

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

@@ -299,6 +299,9 @@ case class DataSource(
             "It must be specified manually")
         }
 
+        val enrichedOptions =
+          format.prepareRead(sqlContext, caseInsensitiveOptions, fileCatalog.allFiles())
+
         HadoopFsRelation(
           sqlContext,
           fileCatalog,
@@ -306,7 +309,7 @@ case class DataSource(
           dataSchema = dataSchema.asNullable,
           bucketSpec = bucketSpec,
           format,
-          options)
+          enrichedOptions)
 
       case _ =>
         throw new AnalysisException(

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

@@ -59,6 +59,7 @@ private[sql] object FileSourceStrategy extends Strategy with Logging {
       if (files.fileFormat.toString == "TestFileFormat" ||
          files.fileFormat.isInstanceOf[parquet.DefaultSource] ||
          files.fileFormat.toString == "ORC" ||
+         files.fileFormat.toString == "LibSVM" ||
          files.fileFormat.isInstanceOf[csv.DefaultSource] ||
          files.fileFormat.isInstanceOf[text.DefaultSource] ||
          files.fileFormat.isInstanceOf[json.DefaultSource]) &&

sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala

@@ -438,6 +438,15 @@ trait FileFormat {
       options: Map[String, String],
       files: Seq[FileStatus]): Option[StructType]
 
+  /**
+   * Prepares a read job and returns a potentially updated data source option [[Map]]. This method
+   * can be useful for collecting necessary global information for scanning input data.
+   */
+  def prepareRead(
+      sqlContext: SQLContext,
+      options: Map[String, String],
+      files: Seq[FileStatus]): Map[String, String] = options
+
   /**
    * Prepares a write job and returns an [[OutputWriterFactory]].  Client side job preparation can
    * be put here.  For example, user defined output committer can be configured here