Spark cannot read locally from S3 without an S3a protocol; you’ll more than likely need a local copy of the data or you’ll need to utilize the proper jars to enable S3 communication from the edge to the datacenter.
https://stackoverflow.com/questions/30385981/how-to-access-s3a-files-from-apache-spark Here are the jars: https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-aws Looks like you already have them, in which case you’ll have to make small configuration changes, e.g. s3 --> s3a Keep in mind: The Amazon JARs have proven very brittle: the version of the Amazon libraries must match the versions against which the Hadoop binaries were built. https://hortonworks.github.io/hdp-aws/s3-s3aclient/index.html#using-the-s3a-filesystem-client From: Toy <noppani...@gmail.com> Date: Tuesday, January 23, 2018 at 11:33 AM To: "user@spark.apache.org" <user@spark.apache.org> Subject: I can't save DataFrame from running Spark locally Hi, First of all, my Spark application runs fine in AWS EMR. However, I'm trying to run it locally to debug some issue. My application is just to parse log files and convert to DataFrame then convert to ORC and save to S3. However, when I run locally I get this error java.io.IOException: /orc/dt=2018-01-23 doesn't exist at org.apache.hadoop.fs.s3.Jets3tFileSystemStore.get(Jets3tFileSystemStore.java:170) at org.apache.hadoop.fs.s3.Jets3tFileSystemStore.retrieveINode(Jets3tFileSystemStore.java:221) 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 org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:191) at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102) at com.sun.proxy.$Proxy22.retrieveINode(Unknown Source) at org.apache.hadoop.fs.s3.S3FileSystem.getFileStatus(S3FileSystem.java:340) at org.apache.hadoop.fs.FileSystem.exists(FileSystem.java:1426) at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:77) at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58) at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56) at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:74) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:135) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:132) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:113) at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:87) at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:87) at org.apache.spark.sql.execution.datasources.DataSource.write(DataSource.scala:492) at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:215) at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:198) at Vivace$$anonfun$processStream$1.apply(vivace.scala:193) at Vivace$$anonfun$processStream$1.apply(vivace.scala:170) Here's what I have in sbt scalaVersion := "2.11.8" val sparkVersion = "2.1.0" val hadoopVersion = "2.7.3" val awsVersion = "1.11.155" lazy val sparkAndDependencies = Seq( "org.apache.spark" %% "spark-core" % sparkVersion, "org.apache.spark" %% "spark-sql" % sparkVersion, "org.apache.spark" %% "spark-hive" % sparkVersion, "org.apache.spark" %% "spark-streaming" % sparkVersion, "org.apache.hadoop" % "hadoop-aws" % hadoopVersion, "org.apache.hadoop" % "hadoop-common" % hadoopVersion ) And this is where the code failed val sparrowWriter = sparrowCastedDf.write.mode("append").format("orc").option("compression", "zlib") sparrowWriter.save(sparrowOutputPath) sparrowOutputPath is something like s3://bucket/folder and it exists I checked it with aws command line I put a breakpoint there and the full path looks like this s3://bucket/orc/dt=2018-01-23 which exists. I have also set up the credentials like this sc.hadoopConfiguration.set("fs.s3.awsAccessKeyId", "key") sc.hadoopConfiguration.set("fs.s3.awsSecretAccessKey", "secret") My confusion is this code runs fine in the cluster but I get this error running locally.
