以下代码用于将数据从oracle加载到S3。
source_data = spark.read.format("jdbc").option("url", url).option("dbtable", "scott.emp").option("fetchSize","10000").option("user", user).option("password", password) .option("driver", driver).load()
hadoopConf = spark_context._jsc.hadoopConfiguration()
hadoopConf.set("fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
hadoopConf.setBoolean("fs.s3a.sse.enabled", True)
source_data.write.mode('overwrite').parquet("s3a://bucket1/folder1/emp")
我可以读取数据，但当我试图直接写入S3时，代码会抛出错误。错误：
org.apache.hadoop.fs.FileAlreadyExistsException: Can't make directory for path 's3a://bucket1/folder1' since it is a file.
at org.apache.hadoop.fs.s3a.S3AFileSystem.mkdirs(S3AFileSystem.java:861)
at org.apache.hadoop.fs.FileSystem.mkdirs(FileSystem.java:1881)
at org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter.setupJob(FileOutputCommitter.java:313)
at org.apache.spark.internal.io.HadoopMapReduceCommitProtocol.setupJob(HadoopMapReduceCommitProtocol.scala:162)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:139)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:159)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:83)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:81)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:677)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:677)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:80)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:677)
at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:286)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:272)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:230)
at org.apache.spark.sql.DataFrameWriter.parquet(DataFrameWriter.scala:567)
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:498)
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.lang.Thread.run(Thread.java:748)
问题：
- s3a是否有某种缓存需要清除
- s3a比s3好吗
S3A连接器有目录标记的概念；以"/"结束；以及文件；不会以"/"结束；，并且不允许您在文件下创建文件。
删除
- Hadoop CLI:
hadoop fs -rm -R s3a://bucket1/folder1 - AWS控制台/CLI:通常的方式
s3a是否有某种缓存需要清除？
是的，但除非你打开它，否则这不是问题所在。缓存是用来处理s3CRUD不一致的。
s3a是s3的好选择吗？
对于使用Hadoop API的应用程序，它是最好的开源S3连接器。电子病历是闭源的。presto有一些不错的触感，但它针对presto的特定用例进行了优化。