歡迎轉載，轉載請注明出處，徽滬一郎
概要
今天不談Spark中什么復雜的技術實現，只稍為聊聊如何進行代碼跟讀。眾所周知，Spark使用scala進行開發，由於scala有眾多的語法糖，很多時候代碼跟着跟着就覺着線索跟丟掉了，另外Spark基於Akka來進行消息交互，那如何知道誰是接收方呢？
new Throwable().printStackTrace
代碼跟讀的時候，經常會借助於日志，針對日志中輸出的每一句，我們都很想知道它們的調用者是誰。但有時苦於對spark系統的了解程度不深，或者對scala認識不夠，一時半會之內無法找到答案，那么有沒有什么簡便的辦法呢？
我的辦法就是在日志出現的地方加入下面一句話
new Throwable().printStackTrace()
現在舉一個實際的例子來說明問題。
比如我們在啟動spark-shell之后，輸入一句非常簡單的sc.textFile("README.md")，會輸出下述的log
14/07/05 19:53:27 INFO MemoryStore: ensureFreeSpace(32816) called with curMem=0, maxMem=308910489
14/07/05 19:53:27 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 32.0 KB, free 294.6 MB)
14/07/05 19:53:27 DEBUG BlockManager: Put block broadcast_0 locally took 78 ms
14/07/05 19:53:27 DEBUG BlockManager: Putting block broadcast_0 without replication took 79 ms
res0: org.apache.spark.rdd.RDD[String] = README.md MappedRDD[1] at textFile at :13
那我很想知道是第二句日志所在的tryToPut函數是被誰調用的該怎么辦？
辦法就是打開MemoryStore.scala，找到下述語句
logInfo("Block %s stored as %s in memory (estimated size %s, free %s)".format(
blockId, valuesOrBytes, Utils.bytesToString(size), Utils.bytesToString(freeMemory)))
在這句話之上，添加如下語句
new Throwable().printStackTrace()
然后，重新進行源碼編譯
sbt/sbt assembly
再次打開spark-shell,執行sc.textFile("README.md"),就可以得到如下輸出，從中可以清楚知道tryToPut的調用者是誰
14/07/05 19:53:27 INFO MemoryStore: ensureFreeSpace(32816) called with curMem=0, maxMem=308910489
14/07/05 19:53:27 WARN MemoryStore: just show the calltrace by entering some modified code
java.lang.Throwable
at org.apache.spark.storage.MemoryStore.tryToPut(MemoryStore.scala:182)
at org.apache.spark.storage.MemoryStore.putValues(MemoryStore.scala:76)
at org.apache.spark.storage.MemoryStore.putValues(MemoryStore.scala:92)
at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:699)
at org.apache.spark.storage.BlockManager.put(BlockManager.scala:570)
at org.apache.spark.storage.BlockManager.putSingle(BlockManager.scala:821)
at org.apache.spark.broadcast.HttpBroadcast.(HttpBroadcast.scala:52)
at org.apache.spark.broadcast.HttpBroadcastFactory.newBroadcast(HttpBroadcastFactory.scala:35)
at org.apache.spark.broadcast.HttpBroadcastFactory.newBroadcast(HttpBroadcastFactory.scala:29)
at org.apache.spark.broadcast.BroadcastManager.newBroadcast(BroadcastManager.scala:62)
at org.apache.spark.SparkContext.broadcast(SparkContext.scala:787)
at org.apache.spark.SparkContext.hadoopFile(SparkContext.scala:556)
at org.apache.spark.SparkContext.textFile(SparkContext.scala:468)
at $line5.$read$$iwC$$iwC$$iwC$$iwC.(:13)
at $line5.$read$$iwC$$iwC$$iwC.(:18)
at $line5.$read$$iwC$$iwC.(:20)
at $line5.$read$$iwC.(:22)
at $line5.$read.(:24)
at $line5.$read$.(:28)
at $line5.$read$.()
at $line5.$eval$.(:7)
at $line5.$eval$.()
at $line5.$eval.$print()
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:483)
at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:788)
at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1056)
at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:614)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:645)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:609)
at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:796)
at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:841)
at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:753)
at org.apache.spark.repl.SparkILoop.processLine$1(SparkILoop.scala:601)
at org.apache.spark.repl.SparkILoop.innerLoop$1(SparkILoop.scala:608)
at org.apache.spark.repl.SparkILoop.loop(SparkILoop.scala:611)
at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply$mcZ$sp(SparkILoop.scala:936)
at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:884)
at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:884)
at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:884)
at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:982)
at org.apache.spark.repl.Main$.main(Main.scala:31)
at org.apache.spark.repl.Main.main(Main.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:483)
at org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:303)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:55)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
14/07/05 19:53:27 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 32.0 KB, free 294.6 MB)
14/07/05 19:53:27 DEBUG BlockManager: Put block broadcast_0 locally took 78 ms
14/07/05 19:53:27 DEBUG BlockManager: Putting block broadcast_0 without replication took 79 ms
res0: org.apache.spark.rdd.RDD[String] = README.md MappedRDD[1] at textFile at :13
git同步
對代碼作了修改之后，如果並不想提交代碼，那該如何將最新的內容同步到本地呢？
git reset --hard
git pull origin master
Akka消息跟蹤
追蹤消息的接收者是誰，相對來說比較容易，只要使用好grep就可以了，當然前提是要對actor model有一點點了解。
還是舉個實例吧，我們知道CoarseGrainedSchedulerBackend會發送LaunchTask消息出來，那么誰是接收方呢？只需要執行以下腳本即可。
grep LaunchTask -r core/src/main
從如下的輸出中，可以清楚看出CoarseGrainedExecutorBackend是LaunchTask的接收方，接收到該函數之后的業務處理，只需要去看看接收方的receive函數即可。
core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala: case LaunchTask(data) =>
core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala: logError("Received LaunchTask command but executor was null")
core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala: case class LaunchTask(data: SerializableBuffer) extends CoarseGrainedClusterMessage
core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala: executorActor(task.executorId) ! LaunchTask(new SerializableBuffer(serializedTask))
小結
今天的內容相對簡單，沒有技術含量，自己做個記述，免得時間久了，不記得。