加载原始HVAC.csv文件，使用函数

Question

我是Spark新手，我是Python代码。

完全遵循我的＆＃34; Learning Spark＆＃34;准则，我看到＆＃34;您不需要安装Hadoop来运行Spark＆＃34;

然而，当我只是尝试使用Pyspark计算一个文件中的行时，我得到以下错误。我错过了什么？

>>> lines = sc.textFile("README.md")
15/02/01 13:27:12 INFO MemoryStore: ensureFreeSpace(32728) called with curMem=0,
 maxMem=278019440
15/02/01 13:27:12 INFO MemoryStore: Block broadcast_0 stored as values in memory
 (estimated size 32.0 KB, free 265.1 MB)
>>> lines.count()
15/02/01 13:27:18 WARN NativeCodeLoader: Unable to load native-hadoop library fo
r your platform... using builtin-java classes where applicable
15/02/01 13:27:18 WARN LoadSnappy: Snappy native library not loaded
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\pyspark\rdd.py", line 847, in co
unt
    return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\pyspark\rdd.py", line 838, in su
m
    return self.mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\pyspark\rdd.py", line 759, in re
duce
    vals = self.mapPartitions(func).collect()
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\pyspark\rdd.py", line 723, in co
llect
    bytesInJava = self._jrdd.collect().iterator()
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\lib\py4j-0.8.2.1-src.zip\py4j\ja
va_gateway.py", line 538, in __call__
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\lib\py4j-0.8.2.1-src.zip\py4j\pr
otocol.py", line 300, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o26.collect.
: org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: fil
e:/C:/Spark/spark-1.1.0-bin-hadoop1/bin/README.md
        at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.j
ava:197)
        at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.ja
va:208)
        at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:179)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:204)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:202)
        at scala.Option.getOrElse(Option.scala:120)
        at org.apache.spark.rdd.RDD.partitions(RDD.scala:202)
        at org.apache.spark.rdd.MappedRDD.getPartitions(MappedRDD.scala:28)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:204)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:202)
        at scala.Option.getOrElse(Option.scala:120)
        at org.apache.spark.rdd.RDD.partitions(RDD.scala:202)
        at org.apache.spark.api.python.PythonRDD.getPartitions(PythonRDD.scala:5
6)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:204)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:202)
        at scala.Option.getOrElse(Option.scala:120)
        at org.apache.spark.rdd.RDD.partitions(RDD.scala:202)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:1135)
        at org.apache.spark.rdd.RDD.collect(RDD.scala:774)
        at org.apache.spark.api.java.JavaRDDLike$class.collect(JavaRDDLike.scala
:305)
        at org.apache.spark.api.java.JavaRDD.collect(JavaRDD.scala:32)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
        at java.lang.reflect.Method.invoke(Unknown Source)
        at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
        at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
        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:207)
        at java.lang.Thread.run(Unknown Source)

>>> lines.first()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\pyspark\rdd.py", line 1167, in f
irst
    return self.take(1)[0]
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\pyspark\rdd.py", line 1126, in t
ake
    totalParts = self._jrdd.partitions().size()
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\lib\py4j-0.8.2.1-src.zip\py4j\ja
va_gateway.py", line 538, in __call__
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\lib\py4j-0.8.2.1-src.zip\py4j\pr
otocol.py", line 300, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o20.partitions.
: org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: fil
e:/C:/Spark/spark-1.1.0-bin-hadoop1/bin/README.md
        at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.j
ava:197)
        at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.ja
va:208)
        at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:179)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:204)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:202)
        at scala.Option.getOrElse(Option.scala:120)
        at org.apache.spark.rdd.RDD.partitions(RDD.scala:202)
        at org.apache.spark.rdd.MappedRDD.getPartitions(MappedRDD.scala:28)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:204)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:202)
        at scala.Option.getOrElse(Option.scala:120)
        at org.apache.spark.rdd.RDD.partitions(RDD.scala:202)
        at org.apache.spark.api.java.JavaRDDLike$class.partitions(JavaRDDLike.sc
ala:50)
        at org.apache.spark.api.java.JavaRDD.partitions(JavaRDD.scala:32)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
        at java.lang.reflect.Method.invoke(Unknown Source)
        at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
        at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
        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:207)
        at java.lang.Thread.run(Unknown Source)

>>>

Answer 1

我没有尝试在Windows系统中运行spark，但在我看来问题是：

py4j.protocol.Py4JJavaError：调用时发生错误 o26.collect。：org.apache.hadoop.mapred.InvalidInputException：输入路径不存在：fil E：/ C：/Spark/spark-1.1.0-bin-hadoop1/bin/README.md

您必须正确引用要加载的文件。如果你从spark文件夹运行pyspark（即：C:\spark），那么lines = sc.textFile("README.md")是正确的。但是如果你从bin运行pyspark（即：C:\spark\bin），你必须引用它：lines = sc.textFile("../README.md")，或者使用文件的绝对路径。

Answer 2

我参加派对有点晚了。我遇到了类似的问题（ec2 spark cluster）。在我的例子中，hdfs dint有我正在寻找的文件。因此，我不得不使用以下命令手动添加我想要的文件

~/ephemeral-hdfs/bin/hadoop fs -put /dir/filename.txt filename.txt

希望这很有帮助。

Answer 3

这是我在Windows中托管的Spark群集上遇到此错误的解决方案：

加载原始HVAC.csv文件，使用函数

解析它

data = sc.textFile("wasb:///HdiSamples/SensorSampleData/hvac/HVAC.csv")

我们使用（wasb：///）允许Hadoop访问azure博客存储文件，三个斜杠是对运行节点容器文件夹的相对引用。

例如：如果Spark集群仪表板中文件资源管理器中文件的路径为：

sflcc1 \ sflccspark1 \ HdiSamples \ SensorSampleData \ HVAC

所以描述路径如下： sflcc1：是存储帐户的名称。 sflccspark：是集群节点名称。

因此我们引用具有相对三个斜杠的当前群集节点名称。

希望这有帮助。

PySpark错误：＆＃34;输入路径不存在＆＃34;

3 个答案:

加载原始HVAC.csv文件，使用函数