在我的本地机器上使用 Spark

Posted 2023-04-15

【中文标题】在我的本地机器上使用 Spark

【英文标题】：using Spark on my local machine

【发布时间】：2016-05-11 23:23:56

【问题描述】：

我下载了 Spark，它看起来可以工作。现在我想尝试使用 txt 文件，例如 hamlet.txt。据我了解，要在 Spark 中工作，我需要打开 spark-1.6.1/bin/pyspark 我把 hamlet.txt 放在 spark-1.6.1/bin/ 现在我输入： raw_hamlet = sc.textFile("hamlet.txt") raw_hamlet.take(5)

但是输出是：

Traceback (most recent call last):
File "", line 1, in 
File "/Applications/spark-1.6.1/python/pyspark/rdd.py", line 1267, in take
totalParts = self.getNumPartitions()
File "/Applications/spark-1.6.1/python/pyspark/rdd.py", line 356, in getNumPartitions
return self._jrdd.partitions().size()
File "/Applications/spark-1.6.1/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py", line 813, in call
File "/Applications/spark-1.6.1/python/pyspark/sql/utils.py", line 45, in deco
return f(*a, **kw)
File "/Applications/spark-1.6.1/python/lib/py4j-0.9-src.zip/py4j/protocol.py", line 308, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o50.partitions.
: org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: file:/Users/kate/hamlet.txt
at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:251)
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:270)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:199)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at org.apache.spark.api.java.JavaRDDLike$class.partitions(JavaRDDLike.scala:64)
at org.apache.spark.api.java.AbstractJavaRDDLike.partitions(JavaRDDLike.scala:46)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
at py4j.Gateway.invoke(Gateway.java:259)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:209)
at java.lang.Thread.run(Thread.java:745)

【参考方案1】：

1 - 将您的 "spark-1.6.1/bin/" 添加到您的 .bashrc

2 - source .bashrc

3 - 转到您拥有数据集的目录

4 - 从那里运行 pyspark 或 spark-submit。