(PythonRDD.scala:234) at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)...at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint...(PythonRDD.scala:234) at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152...) at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63) 问题原因: 由于Python默认的字符编码集为unicode...(PythonRDD.scala:234) at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152
_jvm.PythonRDD(self...._jrdd_val = python_rdd.asJavaRDD() 这里和JVM里的PythonRDD建立了联系。..._javaAccumulator) 我们看到了sc.pythonExec对象,这个是传入到PythonRDD里的python命令。...private[spark] class PythonRDD( parent: RDD[_], func: PythonFunction, preservePartitoning...PythonRDD的compute方法里会调用PythonRunner的compute方法: val runner = PythonRunner(func, bufferSize, reuse_worker
org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:166) at org.apache.spark.api.python.PythonRDD.compute...(PythonRDD.scala:65) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364) at org.apache.spark.rdd.RDD.iterator..._jvm.PythonRDD.collectAndServe(self....RDD.scala:405) at org.apache.spark.rdd.RDD.collect(RDD.scala:1018) at org.apache.spark.api.python.PythonRDD...$.collectAndServe(PythonRDD.scala:193) at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala
同时Spark java进程启动了一个Python守护进程,这个进程是处理PythonRDD数据的。因为我起的Spark是local模式,所以只有一个Spark进程和一个Python进程。...如果是yarn模式,每一个executor都会启动一个Python进程,PythonRDD在Python守护进程里处理然后返回结果给Spark Task线程。..._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD...._jvm.PythonRDD(self...._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)这里创建了一个新对象,来看一下runJob的定义。
rdd.getNumPartitions()) #查看分区的状态 print(rdd.glom().collect()) ParallelCollectionRDD[0] at parallelize at PythonRDD.scala...print(rdd.getNumPartitions()) print(rdd.glom().collect()) ParallelCollectionRDD[0] at parallelize at PythonRDD.scala
用户 Python 脚本中定义的一系列处理逻辑最终遇到 action 方法后会触发 Job 的提交,提交 Job 时是直接通过 Py4j 调用 Java 的 PythonRDD.runJob 方法完成,...映射到 JVM 中,会转给 sparkContext.runJob 方法,Job 运行完成后,JVM 中会开启一个本地 Socket 等待 Python 进程拉取,对应地,Python 进程在调用 PythonRDD.runJob...Executor 端收到 Task 后,会通过 launchTask 运行 Task,最后会调用到 PythonRDD 的 compute 方法,来处理一个分区的数据,PythonRDD 的 compute
A is an RDD of type PythonRDD....A=sc.parallelize(range(3)) print (A) output:PythonRDD[1] at RDD at PythonRDD.scala:48 Collect: RDD content
数据流交互结构例如以下图所看到的: 由上图可知,用户提交的Python脚本中实现的RDD transformations操作会在本地转换为Java的PythonRDD对象。...在远程的worker节点上,PythonRDD对象所在的JVM进程会调起Python子进程并通过pipe进行进程间通信(如向Python子进程发送用户提交的Python脚本或待处理的数据)。
_jvm.PythonRDD.readRDDFromFile(self...._jvm.PythonRDD.setupBroadcast(self._path) if sc.
_jvm.PythonRDD.newAPIHadoopFile(self....而 PythonRDD (core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala),则是一个 Scala 中封装的伴生对象,提供了常用的
1,2,3,4,5]) rdd print(rdd) print(rdd.getNumPartitions() ) 输出结果: ParallelCollectionRDD[0] at parallelize at PythonRDD.scala
当我们执行pyspark当中的RDD时,spark context会通过Py4j启动一个使用JavaSparkContext的JVM,所有的RDD的转化操作都会被映射成Java中的PythonRDD对象...当我们的任务被传输到Workder进行执行的时候,PythonRDD会启动Python的子进程来传输代码和执行的结果。
PySpark worker启动机制 PySpark的工作原理是通过Spark里的PythonRDD启动一个(或者多个,以pythonExec, 和envVars为key)Python deamon进程
])# collect 算子,输出RDD为List对象# print(rdd) 输出的是类名,输出结果:ParallelCollectionRDD[0] at readRDDFromFile at PythonRDD.scala
DAGScheduler: Missing parents: List() 16/05/16 21:33:57 INFO DAGScheduler: Submitting ResultStage 0 (PythonRDD...DAGScheduler.scala:1006 16/05/16 21:33:57 INFO DAGScheduler: Submitting 8 missing tasks from ResultStage 0 (PythonRDD
我们可以看到,PythonRDD[1]与ParallelCollectionRDD[0]是连接的。现在,让我们继续添加转换,将列表的所有元素加20。
fav_score + cart_score + buy_score # 返回用户ID、分类ID、用户对分类的偏好打分 return r.userId, r.cateId, rating 返回一个PythonRDD
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