我想通过hivejdbc连接将整个配置单元表加载到spark内存中。并且已经在我的项目中添加了hive-site.xml、hdfs-site.xml。spark已连接配置单元，因为已成功获取列名（例如role\u id）。但是spark似乎将列名作为数据加载，并抛出一个异常。这是我的密码：

val df = spark.read.format("jdbc")
  .option("driver", CommonUtils.HIVE_DIRVER)
  .option("url", CommonUtils.HIVE_URL)
  .option("dbtable", "datasource_test.t_leave_map_base")
  .option("header", "true")
  .option("user", CommonUtils.HIVE_PASSWORD)
  .option("password", CommonUtils.HIVE_PASSWORD)
  .option("fetchsize", "20")
  .load()
df.registerTempTable("t_leave_map_base")
df.persist(StorageLevel.MEMORY_ONLY)
df.show()
df

获取错误：
java.lang.numberformatexception:对于输入字符串：“t\u leave\u map\u base.role\u id”位于java.lang.numberformatexception.forinputstring（numberformatexception）。java:65)~[na:1.8.0\u 25]在java.lang.long.parselong（long。java:589)~[na:1.8.0\u 25]在java.lang.long.valueof（long。java:803)~（na:1.8.0μ25）在org.apache.hive.jdbc.hivebaseresultset.getlong（hivebaseresultset。java:366)~[hive-jdbc-1.1.0-cdh5.12.0。jar:1.1.0-cdh5.12.0]位于org.apache.spark.sql.execution.datasources.jdbc.jdbcutils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$jdbcutils$$makegetter$8.apply（jdbcutils）。scala:409)~[spark-sql\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.sql.execution.datasources.jdbc.jdbcutils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$jdbcutils$$makegetter$8.apply（jdbcutils）。scala:408)~[spark-sql\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.sql.execution.datasources.jdbc.jdbcutils$$anon$1.getnext（jdbcutils。scala:330) ~[spark-sql\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.sql.execution.datasources.jdbc.jdbcutils$$anon$1.getnext（jdbcutils。scala:312)~[spark-sql\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.util.nextiterator.hasnext（nextiterator。scala:73)~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.interruptibleiterator.hasnext（interruptibleiterator。scala:37)~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]位于org.apache.spark.util.completioniterator.hasnext（completioniterator。scala:32)~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.sql.catalyst.expressions.generatedclass$generateEditor.processnext（未知源）~[na:na]位于org.apache.spark.sql.execution.bufferedrowiterator.hasnext（bufferedrowiterator）。java:43)~[spark-sql\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.sql.execution.whitestagecodegenexec$$anonfun$8$$anon$1.hasnext（whitestagecodegenexec。scala:395)~[spark-sql\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]位于org.apache.spark.sql.execution.columnar.inmemoryrelation$$anonfun$1$$anon$1.hasnext（inmemoryrelation）。scala:133)~[spark-sql\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.storage.memory.memorystore.putiteratorasvalues（内存存储）。scala:215)~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.storage.blockmanager$$anonfun$doputiterator$1.apply（blockmanager。scala:1038)~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.storage.blockmanager$$anonfun$doputiterator$1.apply（blockmanager。scala:1029)~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.storage.blockmanager.doput（blockmanager。scala:969)~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.storage.blockmanager.doputierator（blockmanager。scala:1029)~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.storage.blockmanager.getorelseupdate（blockmanager。scala:760)~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.rdd.rdd.getorcompute（rdd。scala:334) ~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.rdd.rdd.iterator（rdd。scala:285)~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.rdd.mappartitionsrdd.compute（mappartitionsrdd。scala:38)~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.rdd.rdd.computeorreadcheckpoint（rdd。scala:323)~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.rdd.rdd.iterator（rdd。scala:287)~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.rdd.mappartitionsrdd.compute（mappartitionsrdd。scala:38) ~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.rdd.rdd.computeorreadcheckpoint（rdd。scala:323)~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.rdd.rdd.iterator（rdd。scala:287)~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.scheduler.resulttask.runtask（resulttask。scala:87)~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.scheduler.task.run（task。scala:108)~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]在org.apache.spark.executor.executor$taskrunner.run（executor。scala:338) ~[spark-core\ 2.11-2.2.0.cloudera2。jar:2.2.0.cloudera2]位于java.util.concurrent.threadpoolexecutor.runworker（threadpoolexecutor。java:1142)~[na:1.8.0\u 25]位于java.util.concurrent.threadpoolexecutor$worker.run（threadpoolexecutor。java:617)~[na:1.8.0\u25]在java.lang.thread.run（thread。java:745)[na:1.8.0掼25]
调试项目，所有fetchedrows都是列的名称：

我想知道sparksql是否支持这种方式加载配置单元表？