最近突然对spark的spark-shell发生了兴趣

它是如何启动scala的REPL的，并且在此前写入了常用的环境变量的呢？

通过查看spark的源码，找到了SparkILoop.scala

import scala.tools.nsc.interpreter.{JPrintWriter, ILoop}/** *  A Spark-specific interactive shell. */class SparkILoop(in0: Option[BufferedReader], out: JPrintWriter)    extends ILoop(in0, out) {  def this(in0: BufferedReader, out: JPrintWriter) = this(Some(in0), out)  def this() = this(None, new JPrintWriter(Console.out, true))  def initializeSpark() {    intp.beQuietDuring {      processLine("""         @transient val sc = {           val _sc = org.apache.spark.repl.Main.createSparkContext()           println("Spark context available as sc.")           _sc         }        """)      processLine("""         @transient val sqlContext = {           val _sqlContext = org.apache.spark.repl.Main.createSQLContext()           println("SQL context available as sqlContext.")           _sqlContext         }        """)      processLine("import org.apache.spark.SparkContext._")      processLine("import sqlContext.implicits._")      processLine("import sqlContext.sql")      processLine("import org.apache.spark.sql.functions._")    }  }  ...}

可以看出SparkILoop继承自scala.tools.nsc.interpreter.ILoop

紧接着着看了ILoop的api doc

终于找到了启动ILoop的方法：

import scala.tools.nsc.interpreter.ILoopimport scala.tools.nsc.Settingsval loop = new ILooploop.process(new Settings)