在Zeppelin中使用SparkSQL查询Hive表时，为什么会收到此IO异常？

时间：2017-06-02 20:25:52

标签： hadoop apache-spark hive amazon-emr apache-zeppelin

我按照this tutorial的第一部分创建了一个外部Hive表，并将其指向特定的S3 Bucket。在Hue界面中，我可以在成功创建后浏览数据样本。如果我切换到Zeppelin并运行以下命令：%sql show tables我可以在default数据库旁边看到我的表。

现在，如果我真的尝试查询表，我会收到java.io.IOException: Not a file: s3://my-bucket/my-subdirectory错误。错误是有道理的，但是Hive会让你指定一个S3存储桶而不是一个实际的S3文件，所以我不确定如何让它们都快乐！

请注意，此目录中只有一个文件，我没有尝试任何分区。该文件已压缩并命名为foo.txt.gz，并使用psql生成并转储postgres表。

编辑：显示代码

创建脚本

CREATE TABLE IF NOT EXISTS foo (
  name STRING,
  age INT
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
STORED AS TEXTFILE
LOCATION 's3://my-bucket/my-directory/'

在Zeppelin中查询

%sql SELECT * FROM foo LIMIT 100

java.io.IOException: Not a file: s3://my-bucket/my-directory
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:288)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:202)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:311)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1.apply(Dataset.scala:2371)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2765)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$execute$1(Dataset.scala:2370)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collect(Dataset.scala:2377)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2113)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2112)
at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2795)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2112)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2327)
at sun.reflect.GeneratedMethodAccessor114.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.zeppelin.spark.ZeppelinContext.showDF(ZeppelinContext.java:235)
at org.apache.zeppelin.spark.SparkSqlInterpreter.interpret(SparkSqlInterpreter.java:130)
at org.apache.zeppelin.interpreter.LazyOpenInterpreter.interpret(LazyOpenInterpreter.java:95)
at org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer$InterpretJob.jobRun(RemoteInterpreterServer.java:490)
at org.apache.zeppelin.scheduler.Job.run(Job.java:175)
at org.apache.zeppelin.scheduler.FIFOScheduler$1.run(FIFOScheduler.java:139)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)

1 个答案:

答案 0 :(得分：0)

这里可能存在一些与mbdir（）调用后blobstore客户端放入的伪目录标记有关的列表混淆，它们意味着在实际创建文件后删除的标记。

可能造成混淆

查看AWS网页中的S3浏览器，并查找blob“my-directory /”。如果它在那里，删除它

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