[SPARK-12667] Remove block manager's internal "external block store" API

This pull request removes the external block store API. This is rarely used, and the file system interface is actually a better, more standard way to interact with external storage systems.

There are some other things to remove also, as pointed out by JoshRosen. We will do those as follow-up pull requests.

Author: Reynold Xin <rxin@databricks.com>

Closes #10752 from rxin/remove-offheap.

2 files changed, 0 insertions, 143 deletions

diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala
deleted file mode 100644
index 8b739c9d7c..0000000000
--- a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala
+++ /dev/null
@@ -1,93 +0,0 @@
-/*
- * 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.
- */
-
-// scalastyle:off println
-package org.apache.spark.examples
-
-import java.util.Random
-
-import scala.math.exp
-
-import breeze.linalg.{DenseVector, Vector}
-import org.apache.hadoop.conf.Configuration
-
-import org.apache.spark._
-import org.apache.spark.storage.StorageLevel
-
-/**
- * Logistic regression based classification.
- * This example uses Tachyon to persist rdds during computation.
- *
- * This is an example implementation for learning how to use Spark. For more conventional use,
- * please refer to either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or
- * org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS based on your needs.
- */
-object SparkTachyonHdfsLR {
-  val D = 10   // Numer of dimensions
-  val rand = new Random(42)
-
-  def showWarning() {
-    System.err.println(
-      """WARN: This is a naive implementation of Logistic Regression and is given as an example!
-        |Please use either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or
-        |org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
-        |for more conventional use.
-      """.stripMargin)
-  }
-
-  case class DataPoint(x: Vector[Double], y: Double)
-
-  def parsePoint(line: String): DataPoint = {
-    val tok = new java.util.StringTokenizer(line, " ")
-    var y = tok.nextToken.toDouble
-    var x = new Array[Double](D)
-    var i = 0
-    while (i < D) {
-      x(i) = tok.nextToken.toDouble; i += 1
-    }
-    DataPoint(new DenseVector(x), y)
-  }
-
-  def main(args: Array[String]) {
-
-    showWarning()
-
-    val inputPath = args(0)
-    val sparkConf = new SparkConf().setAppName("SparkTachyonHdfsLR")
-    val conf = new Configuration()
-    val sc = new SparkContext(sparkConf)
-    val lines = sc.textFile(inputPath)
-    val points = lines.map(parsePoint).persist(StorageLevel.OFF_HEAP)
-    val ITERATIONS = args(1).toInt
-
-    // Initialize w to a random value
-    var w = DenseVector.fill(D){2 * rand.nextDouble - 1}
-    println("Initial w: " + w)
-
-    for (i <- 1 to ITERATIONS) {
-      println("On iteration " + i)
-      val gradient = points.map { p =>
-        p.x * (1 / (1 + exp(-p.y * (w.dot(p.x)))) - 1) * p.y
-      }.reduce(_ + _)
-      w -= gradient
-    }
-
-    println("Final w: " + w)
-    sc.stop()
-  }
-}
-// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala
deleted file mode 100644
index e46ac655be..0000000000
--- a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * 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.
- */
-
-// scalastyle:off println
-package org.apache.spark.examples
-
-import scala.math.random
-
-import org.apache.spark._
-import org.apache.spark.storage.StorageLevel
-
-/**
- *  Computes an approximation to pi
- *  This example uses Tachyon to persist rdds during computation.
- */
-object SparkTachyonPi {
-  def main(args: Array[String]) {
-    val sparkConf = new SparkConf().setAppName("SparkTachyonPi")
-    val spark = new SparkContext(sparkConf)
-
-    val slices = if (args.length > 0) args(0).toInt else 2
-    val n = 100000 * slices
-
-    val rdd = spark.parallelize(1 to n, slices)
-    rdd.persist(StorageLevel.OFF_HEAP)
-    val count = rdd.map { i =>
-      val x = random * 2 - 1
-      val y = random * 2 - 1
-      if (x * x + y * y < 1) 1 else 0
-    }.reduce(_ + _)
-    println("Pi is roughly " + 4.0 * count / n)
-
-    spark.stop()
-  }
-}
-// scalastyle:on println