RDD.collect(),take(), first

本文深入探讨了RDD的基本操作,包括collect()方法将所有元素收集到驱动程序并序列化为列表的过程,以及take()方法用于获取数据集前几个元素的用法。这些操作对于理解Spark的数据处理流程至关重要。

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RDD.take(),

RDD.first()

 

.collect() 方法把RDD的所有元素返回给驱动,驱动将其序列化成了一个列表

.take() 返回前几个元素

>>> student.textFile("hdfs://master:9000/hesdless/Desktop/workspace/hdfs_op/sparkDir/student.txt") Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'RDD' object has no attribute 'textFile' >>> print(student.collect()) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/opt/module/spark-2.4.8-bin-hadoop2.7/python/pyspark/rdd.py", line 816, in collect sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd()) File "/opt/module/spark-2.4.8-bin-hadoop2.7/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__ File "/opt/module/spark-2.4.8-bin-hadoop2.7/python/pyspark/sql/utils.py", line 63, in deco return f(*a, **kw) File "/opt/module/spark-2.4.8-bin-hadoop2.7/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe. : org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: hdfs://master:9000/headless/Desktop/workspace/hdfs_op/student.txt at org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:287) at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:229) at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:315) at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:204) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:269) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:269) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2132) at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:990) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:385) at org.apache.spark.rdd.RDD.collect(RDD.scala:989) at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:166) at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.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:498) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at py4j.Gateway.invoke(Gateway.java:282) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:238) at java.lang.Thread.run(Thread.java:748) >>>
04-03
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