我正在使用sparkDataframe插入到配置单元表中。即使应用程序是使用用户名“myuser”提交的，一些hive暂存部件文件也是使用用户名“mapr”创建的。因此，在重命名暂存文件时，对配置单元表的最终写入失败，并表示访问被拒绝。命令：
resultdf.write.mode（“append”）.insertinto（不稳定）
错误：
线程“main”org.apache.hadoop.security.accesscontrolexception中出现异常：拒绝用户myuser（用户id 2547）访问rename/ded/data/db/da\u mydb.db/managed/da\u primary/.hive-staging\u hive\u 2017-12-27\u 13-25-22\u 586\u 3120774356819313410-1/-ext-10000/\u temporary/0/task\u 201712271525\u 0080\u m\u000000/part-00000 to/ded/data/db/da\u mydb.db/managed/da\u primary/.hive-staging\u hive\u 2017-12-27\u 13-25-22\u 586\u 31207774356819313410-1/-ext-10000/part-00000，网址：com.mapr.fs.maprfilesystem.rename（maprfilesystem）。java:1112)位于org.apache.hadoop.mapreduce.lib.output.fileoutputcommitter.mergepaths（fileoutputcommitter）。java:461)在org.apache.hadoop.mapreduce.lib.output.fileoutputcommitter.mergepaths（fileoutputcommitter）。java:475)位于org.apache.hadoop.mapreduce.lib.output.fileoutputcommitter.commitjobinternal（fileoutputcommitter。java:392)在org.apache.hadoop.mapreduce.lib.output.fileoutputcommitter.commitjob（fileoutputcommitter。java:364)在org.apache.hadoop.mapred.fileoutputcommitter.commitjob（fileoutputcommitter。java:136)位于org.apache.spark.sql.hive.sparkhivewritercontainer.commitjob（hivewritercontainers）。scala:108)位于org.apache.spark.sql.hive.execution.insertintohivetable.saveashivefile（insertintohivetable。scala:85)在org.apache.spark.sql.hive.execution.insertintohivetable.sideeffectresult$lzycompute（insertintohivetable。scala:201)位于org.apache.spark.sql.hive.execution.insertintohivetable.sideeffectresult（insertintohivetable。scala:127)在org.apache.spark.sql.hive.execution.insertintohivetable.doexecute（insertintohivetable。scala:276)在org.apache.spark.sql.execution.sparkplan$$anonfun$执行$5.apply（sparkplan。scala:132)在org.apache.spark.sql.execution.sparkplan$$anonfun$execute$5.apply（sparkplan。scala:130)在org.apache.spark.rdd.rddoperationscope$.withscope（rddoperationscope。scala:150)在org.apache.spark.sql.execution.sparkplan.execute（sparkplan。scala:130)在org.apache.spark.sql.execution.queryexecution.tordd$lzycompute（查询执行）。scala:55)在org.apache.spark.sql.execution.queryexecution.tordd（queryexecution。scala:55)位于org.apache.spark.sql.dataframewriter.insertinto（dataframewriter。scala:189)位于org.apache.spark.sql.dataframewriter.insertinto（dataframewriter。scala:166)在com.iri.suppchain.runkeying$.execxform（runkeying。scala:74)在com.iri.suppchain.runkeying$$anonfun$1.apply（runkeying。scala:36)在com.iri.suppchain.runkeying$$anonfun$1.apply（runkeying。scala:36)在scala.collection.immutable.list.foreach（list。scala:318)在com.iri.suppchain.runkeying$delayedinit$body.apply（runkeying。scala:36)在scala.function0$class.apply$mcv$sp（function0。scala:40)在scala.runtime.abstractfunction0.apply$mcv$sp（abstractfunction0。scala:12)
以下是环境细节：
Spark1.6.1
分布Map器