mysql - 从Apache Spark访问带有文本列的MySql表

我正在尝试使用Jdbc从Apache Spark读取MySql表。
我收到以下异常：

17/02/26 09:00:18 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID 0)
java.sql.SQLException: Value '  5023512432017-02-14 16:25:4654617a68ad457d2c2017-02-14 16:07:280000-00-00 00:00:0282.460741.7354    1024.1963sphoneUTRAN13966003659671810.162.223.143354854252                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     ' can not be represented as java.sql.Timestamp
at com.mysql.jdbc.SQLError.createSQLException(SQLError.java:996)
at com.mysql.jdbc.SQLError.createSQLException(SQLError.java:935)
at com.mysql.jdbc.SQLError.createSQLException(SQLError.java:924)
at com.mysql.jdbc.SQLError.createSQLException(SQLError.java:870)
at com.mysql.jdbc.ResultSetRow.getTimestampFast(ResultSetRow.java:928)
at com.mysql.jdbc.BufferRow.getTimestampFast(BufferRow.java:555)
at com.mysql.jdbc.ResultSetImpl.getTimestampInternal(ResultSetImpl.java:5943)
at com.mysql.jdbc.ResultSetImpl.getTimestamp(ResultSetImpl.java:5609)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$11.apply(JdbcUtils.scala:378)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$11.apply(JdbcUtils.scala:377)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:286)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:268)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:377)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:231)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:225)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:826)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:826)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
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)

我使用的相同代码可用于其他表，只需更改名称即可。
产生错误的表是通过Workbench Import从csv文件导入的。
起作用的表由插入填充。
这是工作表：

CREATE TABLE `Events` (
  `time` datetime NOT NULL,
  `userID` varchar(45) NOT NULL,
  `action` varchar(45) DEFAULT NULL,
  `duration` double DEFAULT NULL,
  KEY `userid` (`userID`)
) ENGINE=InnoDB DEFAULT CHARSET=latin1;

这是产生错误的表：

CREATE TABLE `small_session` (
  `ne_id` int(11) DEFAULT NULL,
  `lastupdatetime` datetime DEFAULT NULL,
  `session_id` text,
  `start_time` datetime DEFAULT NULL,
  `stop_time` datetime DEFAULT NULL,
  `UPLOAD_KB` double DEFAULT NULL,
  `DOWNLOAD_KB` double DEFAULT NULL,
  `TOTAL_KB` double DEFAULT NULL,
  `APN` text,
  `RAT` text,
  `imei` bigint(20) DEFAULT NULL,
  `ip_address` text,
  `CID` int(11) DEFAULT NULL,
  `mcc` int(11) DEFAULT NULL,
  `mnc` int(11) DEFAULT NULL
) ENGINE=InnoDB DEFAULT CHARSET=latin1;

这是我使用的代码。此位产生错误：

val tableName = "small_session"
dbDriver.read(sparkSession, tableName).createOrReplaceTempView(tableName)
val sqlDF = sparkSession.sql("SELECT * FROM small_session")
sqlDF.show


def read(spark: SparkSession, table: String): DataFrame = {
    val jdbcDF = spark.read
       .jdbc(mySqlURL, table, connectionProperties)
    jdbcDF
}

这是datetime列上的例外吗？帮助将不胜感激。

谢谢。

基里尔。

最佳答案

好的，罪犯是一列text类型的列。

显然，这是从文件导入String数据时Mysql Workbench创建的。
在将这些列更改为varchar之后，一切正常。

感谢所有贡献者的帮助。

基里尔。