国产成人精品久久免费动漫-国产成人精品天堂-国产成人精品区在线观看-国产成人精品日本-a级毛片无码免费真人-a级毛片毛片免费观看久潮喷

您的位置:首頁技術(shù)文章
文章詳情頁

python - spark讀入文件,報錯 java.io.IOException:No input paths specified in job

瀏覽:90日期:2022-09-23 13:44:29

問題描述

想嘗試著處理一下文本,結(jié)果都載入不進來。。。文件路徑肯定沒問題求大神指教

fileName = 'file:///Users/liuchong/Desktop/Animal Farm.txt'liuDF = sqlContext.read.text(fileName).select(’value’)print type(liuDF)liuDF.show()

報錯:

---------------------------------------------------------------------------Py4JJavaError Traceback (most recent call last) in () 5 liuDF = sqlContext.read.text(fileName).select(’value’) 6 print type(liuDF)----> 7 liuDF.show() 8 #print liuDF.count() 9 def removePunctuation(column):/databricks/spark/python/pyspark/sql/dataframe.py in show(self, n, truncate) 255 +---+-----+ 256 '''--> 257 print(self._jdf.showString(n, truncate)) 258 259 def __repr__(self):/databricks/spark/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py in __call__(self, *args) 811 answer = self.gateway_client.send_command(command) 812 return_value = get_return_value(--> 813 answer, self.gateway_client, self.target_id, self.name) 814 815 for temp_arg in temp_args:/databricks/spark/python/pyspark/sql/utils.py in deco(*a, **kw) 43 def deco(*a, **kw): 44 try:---> 45 return f(*a, **kw) 46 except py4j.protocol.Py4JJavaError as e: 47 s = e.java_exception.toString()/databricks/spark/python/lib/py4j-0.9-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name) 306 raise Py4JJavaError( 307 'An error occurred while calling {0}{1}{2}.n'.--> 308 format(target_id, '.', name), value) 309 else: 310 raise Py4JError(Py4JJavaError: An error occurred while calling o77.showString.: java.io.IOException: No input paths specified in job at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:156) at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:208) 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.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.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.sql.execution.SparkPlan.executeTake(SparkPlan.scala:190) at org.apache.spark.sql.execution.Limit.executeCollect(basicOperators.scala:165) at org.apache.spark.sql.execution.SparkPlan.executeCollectPublic(SparkPlan.scala:174) at org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1499) at org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1499) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56) at org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:2086) at org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$execute$1(DataFrame.scala:1498) at org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$collect(DataFrame.scala:1505) at org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1375) at org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1374) at org.apache.spark.sql.DataFrame.withCallback(DataFrame.scala:2099) at org.apache.spark.sql.DataFrame.head(DataFrame.scala:1374) at org.apache.spark.sql.DataFrame.take(DataFrame.scala:1456)at org.apache.spark.sql.DataFrame.showString(DataFrame.scala:170) 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:497) 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)

問題解答

回答1:

No input paths specified in job

log里面說清楚了,輸入的路徑不存在。

回答2:

你確定文本名稱中間有空格?Animal Farm.txt'

回答3:

你是在集群里運行的?那建議把文件扔到hdfs里,路徑改為hdfs url。

標簽: Python 編程
主站蜘蛛池模板: 精品免费久久久久久久 | 欧美特欧美特级一片 | 久久国产精品自线拍免费 | 一区二区三区欧美 | 国产精品国产自线在线观看 | 白浆在线视频 | 在线观看毛片网站 | 国内精品不卡一区二区三区 | 色婷婷久久综合中文久久蜜桃 | 成人免费福利片在线观看 | 波多野结衣福利视频 | 亚洲男同视频网站 | 亚洲美女精品视频 | 爱爱亚洲 | 国产成人一区二区三区 | 日韩精品亚洲专区在线观看 | 国产码一区二区三区 | 婷婷丁香花麻豆 | 亚洲成人精品久久 | 男人的天堂在线免费视频 | 免费一区区三区四区 | 国产波多野结衣中文在线播放 | 高清国产露脸捆绑01经典 | 欧美一级在线观看视频 | 99热久久免费精品首页 | 久久亚洲国产的中文 | 波多野结衣在线观看一区二区 | 97国产成人精品视频 | 成人五级毛片免费播放 | 午夜两性试爱视频免费 | 国产成人综合久久精品亚洲 | 国产激情久久久久影 | 国产成人啪精品 | 九九视频在线观看6 | 免费成年人在线观看视频 | 亚洲精品欧美精品 | 久久精品视频久久 | 日韩欧美特级毛片 | 色三级大全高清视频在线观看 | 成人欧美一区二区三区视频xxx | 亚洲第一欧美 |