6.job触发流程原理剖析与源码分析

从我们编写的一个小的spark  demo程序开始 :

val lines = sparkContext.textFile("")

val words = lines.flatMap(line => line.split("\t"))

val pairs = words.map(word => (word,1))

val counts = pairs.reduceByKey(_+_)

counts.foreach(count +. println(count_1+"_"+count_2))

在SparkContext中textFile方法源码如下:

/**

* Read a text file from HDFS, a local file system (available on all nodes), or any

* Hadoop-supported file system URI, and return it as an RDD of Strings.

*

*首先HadoopFile方法的调用会创建一个HadoopRDD , 该RDD中的元素就是一个一个的(key,value),其中key就是文本行的行号,value就是一行的文本值

*然后调用HadoopRDD的map()方法 , 该方法就会将HadoopRDD中的key移除掉 , 只保留value 

*最后就形成一个只包含文本行的MapPartitionRDD

*/

def textFile(path: String, minPartitions: Int = defaultMinPartitions): RDD[String] = {

assertNotStopped()

hadoopFile(path, classOf[TextInputFormat], classOf[LongWritable], classOf[Text],

minPartitions).map(pair => pair._2.toString).setName(path)

}

这其中的原因已经在代码中说明 , 这其中会调用hadoopFile方法 , 源码如下 :

/** Get an RDD for a Hadoop file with an arbitrary InputFormat

*

*'''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each

*record, directly caching the returned RDD or directly passing it to an aggregation or shuffle

*operation will create many references to the same object.

*If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first

*copy them using a `map` function.

*/

def hadoopFile[K, V](

path: String,

inputFormatClass: Class[_ <: InputFormat[K, V]],

keyClass: Class[K],

valueClass: Class[V],

minPartitions: Int = defaultMinPartitions

): RDD[(K, V)] = {

assertNotStopped()

// A Hadoop configuration can be about 10 KB, which is pretty big, so broadcast it.

// 将Hadoop的配置信息作为广播变量 , 方便在每个节点上都可以读取到相同的Hadoop配置信息

val confBroadcast = broadcast(new SerializableWritable(hadoopConfiguration))

val setInputPathsFunc = (jobConf: JobConf) => FileInputFormat.setInputPaths(jobConf, path)

// 创建HadoopRDD对象

new HadoopRDD(

  this,

  confBroadcast,

  Some(setInputPathsFunc),

  inputFormatClass,

  keyClass,

  valueClass,

  minPartitions).setName(path)

}

对于创建好的HadoopRDD并经过flatMap算子操作之后形成的算子最后调用reduceByKey的时候会经过一到隐式转换 , 因为在RDD中是没有reduceByKey方法的,

因此在调用reduceByKey时候其实是调用如下的方法 : 

/**

*其实在RDD里面是没有reduceByKey的 , 因此对RDD调用reduceByKey()方法时会触发scala的隐式转换;此时就会在作用域内寻找隐式转换

*因此就会在RDD中找到rddToPairRDDFunction()方法,然后调用RDD转换为PairRDDFunction , 在PairRDDFunction中就会调用reduceByKey方法

*/

implicit def rddToPairRDDFunctions[K, V](rdd: RDD[(K, V)])

(implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K] = null): PairRDDFunctions[K, V] = {

new PairRDDFunctions(rdd)

}

从上面的源码中可以看出最后真正的reduceBykey的方法在PairRDDFunctions中 , 源码如下 :

/**

*Merge the values for each key using an associative reduce function. This will also perform

*the merging locally on each mapper before sending results to a reducer, similarly to a

*"combiner" in MapReduce.

*/

def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)] = {

combineByKey[V]((v: V) => v, func, func, partitioner)

}

/**

*Merge the values for each key using an associative reduce function. This will also perform

*the merging locally on each mapper before sending results to a reducer, similarly to a

*"combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions.

*/

def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)] = {

reduceByKey(new HashPartitioner(numPartitions), func)

}

/**

*Merge the values for each key using an associative reduce function. This will also perform

*the merging locally on each mapper before sending results to a reducer, similarly to a

*"combiner" in MapReduce. Output will be hash-partitioned with the existing partitioner/

*parallelism level.

*/

def reduceByKey(func: (V, V) => V): RDD[(K, V)] = {

reduceByKey(defaultPartitioner(self), func)

}

最后 , 不管经过多少transformation算子操作 , 若是没有action的算子操作的话, 那么是不会运行transformation算子的 , 因此foreach方法其实就是触发上面所有算子的操作 , 

而RDD的fereach的源码如下 :

/**

*Applies a function f to all elements of this RDD.

*若是一个RDD调用了action的算子 , 比如foreach方法 , 那么其实就会触发job的运行 , 最后其实就会调用SparkContext的runjob方法

*/

def foreach(f: T => Unit) {

val cleanF = sc.clean(f)

// 调用SparkContext的runjob方法

sc.runJob(this, (iter: Iterator[T]) => iter.foreach(cleanF))

}

最后真正调用的就是SparkContext的runjob方法 进行真正的任务运行 :

/**

*Run a function on a given set of partitions in an RDD and pass the results to the given

*handler function. This is the main entry point for all actions in Spark. The allowLocal

*flag specifies whether the scheduler can run the computation on the driver rather than

*shipping it out to the cluster, for short actions like first().

*/

def runJob[T, U: ClassTag](

  rdd: RDD[T],

  func: (TaskContext, Iterator[T]) => U,

  partitions: Seq[Int],

  allowLocal: Boolean,

  resultHandler: (Int, U) => Unit) {

if (stopped) {

  throw new IllegalStateException("SparkContext has been shutdown")

}

val callSite = getCallSite

val cleanedFunc = clean(func)

logInfo("Starting job: " + callSite.shortForm)

if (conf.getBoolean("spark.logLineage", false)) {

  logInfo("RDD's recursive dependencies:\n" + rdd.toDebugString)

}

//最重要的就是这一行代码了 , 调用SparkContext中的DAGScheduler组件运行一个job

dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, allowLocal,

  resultHandler, localProperties.get)

progressBar.foreach(_.finishAll())

rdd.doCheckpoint()

}

其实SparkContext中运行job的组件就是之前讲到的DAGScheduler了 , 运行一个job就需要stage的划分了 ,  下面就是stage算法划分了