无法使用PySpark Apache Spark显示数据帧

from pyspark import SparkContext, SparkConf from pyspark.sql.session import SparkSession sc = SparkContext('local', 'First Test') spark = SparkSession(sc) path = 'file:///home/userss/test/sprk/myimage.jpg' image_df = spark.read.format("image").load(path)

--------------------------------------------------------------------------- Py4JJavaError Traceback (most recent call last) <ipython-input-3-bc05399bcdc0> in <module> 1 #image_df.printSchema() ----> 2 image_df.show() /usr/local/spark/python/pyspark/sql/dataframe.py in show(self, n, truncate, vertical) 438 """ 439 if isinstance(truncate, bool) and truncate: --> 440 print(self._jdf.showString(n, 20, vertical)) 441 else: 442 print(self._jdf.showString(n, int(truncate), vertical)) /usr/local/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py in __call__(self, *args) 1302 1303 answer = self.gateway_client.send_command(command) -> 1304 return_value = get_return_value( 1305 answer, self.gateway_client, self.target_id, self.name) 1306 /usr/local/spark/python/pyspark/sql/utils.py in deco(*a, **kw) 126 def deco(*a, **kw): 127 try: --> 128 return f(*a, **kw) 129 except py4j.protocol.Py4JJavaError as e: 130 converted = convert_exception(e.java_exception) /usr/local/spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name) 324 value = OUTPUT_CONVERTER[type](answer[2:], gateway_client) 325 if answer[1] == REFERENCE_TYPE: --> 326 raise Py4JJavaError( 327 "An error occurred while calling {0}{1}{2}.\n". 328 format(target_id, ".", name), value) Py4JJavaError: An error occurred while calling o29.showString. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, 0716e7b67344, executor driver): java.io.FileNotFoundException: /home/jovyan/work/6886140-light-blue-wallpaper.jpg (Permission denied) It is possible the underlying files have been updated. You can explicitly invalidate the cache in Spark by running 'REFRESH TABLE tableName' command in SQL or by recreating the Dataset/DataFrame involved. at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.org$apache$spark$sql$execution$datasources$FileScanRDD$$anon$$readCurrentFile(FileScanRDD.scala:124) at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:169) at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:93) at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source) at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729) at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:340) at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872) at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349) at org.apache.spark.rdd.RDD.iterator(RDD.scala:313) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at org.apache.spark.scheduler.Task.run(Task.scala:127) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:446) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:449) at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) at java.base/java.lang.Thread.run(Thread.java:834) Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2059) at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2008) at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2007) at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62) at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2007) at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:973) at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:973) at scala.Option.foreach(Option.scala:407) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:973) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2239) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2188) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2177) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:775) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2099) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2120) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2139) at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:467) at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:420) at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47) at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3627) at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2697) at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3618) at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100) at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160) at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87) at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64) at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3616) at org.apache.spark.sql.Dataset.head(Dataset.scala:2697) at org.apache.spark.sql.Dataset.take(Dataset.scala:2904) at org.apache.spark.sql.Dataset.getRows(Dataset.scala:300) at org.apache.spark.sql.Dataset.showString(Dataset.scala:337) at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.base/java.lang.reflect.Method.invoke(Method.java:566) 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.base/java.lang.Thread.run(Thread.java:834) Caused by: java.io.FileNotFoundException: /home/jovyan/work/6886140-light-blue-wallpaper.jpg (Permission denied) It is possible the underlying files have been updated. You can explicitly invalidate the cache in Spark by running 'REFRESH TABLE tableName' command in SQL or by recreating the Dataset/DataFrame involved. at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.org$apache$spark$sql$execution$datasources$FileScanRDD$$anon$$readCurrentFile(FileScanRDD.scala:124) at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:169) at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:93) at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source) at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729) at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:340) at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872) at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349) at org.apache.spark.rdd.RDD.iterator(RDD.scala:313) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at org.apache.spark.scheduler.Task.run(Task.scala:127) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:446) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:449) at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) ... 1 more

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