spark 写入 mysql 报错

2023-12-15 18:30:02

报错信息如下：

"C:\Program Files\Java\jdk1.8.0_291\bin\java.exe" "-javaagent:D:\Hadoopruanjian\IDEA\IntelliJ IDEA 2021.3.2\lib\idea_rt.jar=60971:D:\Hadoopruanjian\IDEA\IntelliJ IDEA 2021.3.2\bin" -Dfile.encoding=UTF-8 -classpath "C:\Program Files\Java\jdk1.8.0_291\jre\lib\charsets.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\deploy.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\ext\access-bridge-64.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\ext\cldrdata.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\ext\dnsns.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\ext\jaccess.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\ext\jfxrt.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\ext\localedata.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\ext\nashorn.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\ext\sunec.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\ext\sunjce_provider.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\ext\sunmscapi.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\ext\sunpkcs11.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\ext\zipfs.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\javaws.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\jce.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\jfr.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\jfxswt.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\jsse.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\management-agent.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\plugin.jar;C:\Program Files\Java\jdk1.8.0_291\jre\lib\resources.jar;C:\Program 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Files\Java\jdk1.8.0_291\lib\tools.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\hadoop\hadoop-mapreduce-client-core\2.8.5\hadoop-mapreduce-client-core-2.8.5.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\hadoop\hadoop-yarn-common\2.8.5\hadoop-yarn-common-2.8.5.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\hadoop\hadoop-yarn-api\2.8.5\hadoop-yarn-api-2.8.5.jar;D:\Hadoopruanjian\mavenwenjian\javax\xml\bind\jaxb-api\2.2.2\jaxb-api-2.2.2.jar;D:\Hadoopruanjian\mavenwenjian\javax\xml\stream\stax-api\1.0-2\stax-api-1.0-2.jar;D:\Hadoopruanjian\mavenwenjian\javax\activation\activation\1.1\activation-1.1.jar;D:\Hadoopruanjian\mavenwenjian\javax\servlet\servlet-api\2.5\servlet-api-2.5.jar;D:\Hadoopruanjian\mavenwenjian\com\sun\jersey\jersey-client\1.9\jersey-client-1.9.jar;D:\Hadoopruanjian\mavenwenjian\com\google\inject\guice\3.0\guice-3.0.jar;D:\Hadoopruanjian\mavenwenjian\javax\inject\javax.inject\1\javax.inject-1.jar;D:\Hadoopruanjian\mavenwenjian\aopalliance\aopalliance\1.0\aopalliance-1.0.jar;D:\Hadoopruanjian\mavenwenjian\com\sun\jersey\contribs\jersey-guice\1.9\jersey-guice-1.9.jar;D:\Hadoopruanjian\mavenwenjian\com\google\inject\extensions\guice-servlet\3.0\guice-servlet-3.0.jar;D:\Hadoopruanjian\mavenwenjian\io\netty\netty\3.6.2.Final\netty-3.6.2.Final.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\hadoop\hadoop-hdfs\2.8.5\hadoop-hdfs-2.8.5.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\hadoop\hadoop-hdfs-client\2.8.5\hadoop-hdfs-client-2.8.5.jar;D:\Hadoopruanjian\mavenwenjian\com\squareup\okhttp\okhttp\2.4.0\okhttp-2.4.0.jar;D:\Hadoopruanjian\mavenwenjian\com\squareup\okio\okio\1.4.0\okio-1.4.0.jar;D:\Hadoopruanjian\mavenwenjian\org\mortbay\jetty\jetty\6.1.26\jetty-6.1.26.jar;D:\Hadoopruanjian\mavenwenjian\org\mortbay\jetty\jetty-util\6.1.26\jetty-util-6.1.26.jar;D:\Hadoopruanjian\mavenwenjian\commons-daemon\commons-daemon\1.0.13\commons-daemon-1.0.13.jar;D:\Hadoopruanjian\mavenwenjian\xmlenc\xmlenc\0.52\xmlenc-0.52.jar;D:\Hadoopruanjian\mavenwenjian\io\netty\netty-all\4.0.23.Final\netty-all-4.0.23.Final.jar;D:\Hadoopruanjian\mavenwenjian\org\fusesource\leveldbjni\leveldbjni-all\1.8\leveldbjni-all-1.8.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\hadoop\hadoop-common\3.1.3\hadoop-common-3.1.3.jar;D:\Hadoopruanjian\mavenwenjian\com\google\guava\guava\27.0-jre\guava-27.0-jre.jar;D:\Hadoopruanjian\mavenwenjian\com\google\guava\failureaccess\1.0\failureaccess-1.0.jar;D:\Hadoopruanjian\mavenwenjian\com\google\guava\listenablefuture\9999.0-empty-to-avoid-conflict-with-guava\listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar;D:\Hadoopruanjian\mavenwenjian\org\checkerframework\checker-qual\2.5.2\checker-qual-2.5.2.jar;D:\Hadoopruanjian\mavenwenjian\com\google\j2objc\j2objc-annotations\1.1\j2objc-annotations-1.1.jar;D:\Hadoopruanjian\mavenwenjian\org\codehaus\mojo\animal-sniffer-annotations\1.17\animal-sniffer-annotations-1.17.jar;D:\Hadoopruanjian\mavenwenjian\commons-cli\commons-cli\1.2\commons-cli-1.2.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\httpcomponents\httpclient\4.5.2\httpclient-4.5.2.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\httpcomponents\httpcore\4.4.4\httpcore-4.4.4.jar;D:\Hadoopruanjian\mavenwenjian\commons-net\commons-net\3.6\commons-net-3.6.jar;D:\Hadoopruanjian\mavenwenjian\commons-collections\commons-collections\3.2.2\commons-collections-3.2.2.jar;D:\Hadoopruanjian\mavenwenjian\org\eclipse\jetty\jetty-util\9.3.24.v20180605\jetty-util-9.3.24.v20180605.jar;D:\Hadoopruanjian\mavenwenjian\javax\servlet\jsp\jsp-api\2.1\jsp-api-2.1.jar;D:\Hadoopruanjian\mavenwenjian\com\sun\jersey\jersey-core\1.19\jersey-core-1.19.jar;D:\Hadoopruanjian\mavenwenjian\javax\ws\rs\jsr311-api\1.1.1\jsr311-api-1.1.1.jar;D:\Hadoopruanjian\mavenwenjian\com\sun\jersey\jersey-servlet\1.19\jersey-servlet-1.19.jar;D:\Hadoopruanjian\mavenwenjian\com\sun\jersey\jersey-json\1.19\jersey-json-1.19.jar;D:\Hadoopruanjian\mavenwenjian\org\codehaus\jettison\jettison\1.1\jettison-1.1.jar;D:\Hadoopruanjian\mavenwenjian\com\sun\xml\bind\jaxb-impl\2.2.3-1\jaxb-impl-2.2.3-1.jar;D:\Hadoopruanjian\mavenwenjian\org\codehaus\jackson\jackson-core-asl\1.9.2\jackson-core-asl-1.9.2.jar;D:\Hadoopruanjian\mavenwenjian\org\codehaus\jackson\jackson-mapper-asl\1.9.2\jackson-mapper-asl-1.9.2.jar;D:\Hadoopruanjian\mavenwenjian\org\codehaus\jackson\jackson-jaxrs\1.9.2\jackson-jaxrs-1.9.2.jar;D:\Hadoopruanjian\mavenwenjian\org\codehaus\jackson\jackson-xc\1.9.2\jackson-xc-1.9.2.jar;D:\Hadoopruanjian\mavenwenjian\com\sun\jersey\jersey-server\1.19\jersey-server-1.19.jar;D:\Hadoopruanjian\mavenwenjian\commons-lang\commons-lang\2.6\commons-lang-2.6.jar;D:\Hadoopruanjian\mavenwenjian\commons-beanutils\commons-beanutils\1.9.3\commons-beanutils-1.9.3.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\commons\commons-configuration2\2.1.1\commons-configuration2-2.1.1.jar;D:\Hadoopruanjian\mavenwenjian\org\slf4j\slf4j-log4j12\1.7.25\slf4j-log4j12-1.7.25.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\avro\avro\1.7.7\avro-1.7.7.jar;D:\Hadoopruanjian\mavenwenjian\com\thoughtworks\paranamer\paranamer\2.3\paranamer-2.3.jar;D:\Hadoopruanjian\mavenwenjian\org\xerial\snappy\snappy-java\1.0.5\snappy-java-1.0.5.jar;D:\Hadoopruanjian\mavenwenjian\com\google\re2j\re2j\1.1\re2j-1.1.jar;D:\Hadoopruanjian\mavenwenjian\com\google\code\gson\gson\2.2.4\gson-2.2.4.jar;D:\Hadoopruanjian\mavenwenjian\com\jcraft\jsch\0.1.54\jsch-0.1.54.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\curator\curator-client\2.13.0\curator-client-2.13.0.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\curator\curator-recipes\2.13.0\curator-recipes-2.13.0.jar;D:\Hadoopruanjian\mavenwenjian\com\google\code\findbugs\jsr305\3.0.0\jsr305-3.0.0.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\commons\commons-compress\1.18\commons-compress-1.18.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\kerby\kerb-simplekdc\1.0.1\kerb-simplekdc-1.0.1.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\kerby\kerb-client\1.0.1\kerb-client-1.0.1.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\kerby\kerby-config\1.0.1\kerby-config-1.0.1.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\kerby\kerb-core\1.0.1\kerb-core-1.0.1.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\kerby\kerby-pkix\1.0.1\kerby-pkix-1.0.1.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\kerby\kerby-asn1\1.0.1\kerby-asn1-1.0.1.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\kerby\kerby-util\1.0.1\kerby-util-1.0.1.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\kerby\kerb-common\1.0.1\kerb-common-1.0.1.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\kerby\kerb-crypto\1.0.1\kerb-crypto-1.0.1.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\kerby\kerb-util\1.0.1\kerb-util-1.0.1.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\kerby\token-provider\1.0.1\token-provider-1.0.1.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\kerby\kerb-admin\1.0.1\kerb-admin-1.0.1.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\kerby\kerb-server\1.0.1\kerb-server-1.0.1.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\kerby\kerb-identity\1.0.1\kerb-identity-1.0.1.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\kerby\kerby-xdr\1.0.1\kerby-xdr-1.0.1.jar;D:\Hadoopruanjian\mavenwenjian\com\fasterxml\jackson\core\jackson-databind\2.7.8\jackson-databind-2.7.8.jar;D:\Hadoopruanjian\mavenwenjian\com\fasterxml\jackson\core\jackson-annotations\2.7.0\jackson-annotations-2.7.0.jar;D:\Hadoopruanjian\mavenwenjian\com\fasterxml\jackson\core\jackson-core\2.7.8\jackson-core-2.7.8.jar;D:\Hadoopruanjian\mavenwenjian\org\codehaus\woodstox\stax2-api\3.1.4\stax2-api-3.1.4.jar;D:\Hadoopruanjian\mavenwenjian\com\fasterxml\woodstox\woodstox-core\5.0.3\woodstox-core-5.0.3.jar;D:\Hadoopruanjian\mavenwenjian\io\dropwizard\metrics\metrics-core\3.2.6\metrics-core-3.2.6.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\hbase\hbase-protocol\2.2.3\hbase-protocol-2.2.3.jar;D:\Hadoopruanjian\mavenwenjian\org\apache\hbase\hbase-mapreduce\2.2.3\hbase-mapreduce-2.2.3.jar" environment
==============================================mysql环境初始化中=====================================================
table7 ? ? ? ? 进入MySQL中 ? ………………………………………………
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/D:/jars/slf4j-log4j12-1.7.30.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/D:/Hadoopruanjian/mavenwenjian/org/slf4j/slf4j-log4j12/1.7.25/slf4j-log4j12-1.7.25.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
0 ? ?[main] INFO ?ru.yandex.clickhouse.ClickHouseDriver ?- Driver registered
5881 [Executor task launch worker for task 0.0 in stage 0.0 (TID 0)] ERROR org.apache.spark.executor.Executor ?- Exception in task 0.0 in stage 0.0 (TID 0)
java.sql.BatchUpdateException: Data truncation: Incorrect datetime value: '1912-04-18 00:00:00' for column 'birthday' at row 1
?? ?at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
?? ?at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
?? ?at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
?? ?at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
?? ?at com.mysql.jdbc.Util.handleNewInstance(Util.java:404)
?? ?at com.mysql.jdbc.Util.getInstance(Util.java:387)
?? ?at com.mysql.jdbc.SQLError.createBatchUpdateException(SQLError.java:1154)
?? ?at com.mysql.jdbc.PreparedStatement.executeBatchSerially(PreparedStatement.java:1773)
?? ?at com.mysql.jdbc.PreparedStatement.executeBatchInternal(PreparedStatement.java:1257)
?? ?at com.mysql.jdbc.StatementImpl.executeBatch(StatementImpl.java:959)
?? ?at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.savePartition(JdbcUtils.scala:687)
?? ?at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$saveTable$1(JdbcUtils.scala:856)
?? ?at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$saveTable$1$adapted(JdbcUtils.scala:854)
?? ?at org.apache.spark.rdd.RDD.$anonfun$foreachPartition$2(RDD.scala:1020)
?? ?at org.apache.spark.rdd.RDD.$anonfun$foreachPartition$2$adapted(RDD.scala:1020)
?? ?at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2242)
?? ?at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
?? ?at org.apache.spark.scheduler.Task.run(Task.scala:131)
?? ?at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)
?? ?at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)
?? ?at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)
?? ?at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
?? ?at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
?? ?at java.lang.Thread.run(Thread.java:748)
Caused by: com.mysql.jdbc.MysqlDataTruncation: Data truncation: Incorrect datetime value: '1912-04-18 00:00:00' for column 'birthday' at row 1
?? ?at com.mysql.jdbc.MysqlIO.checkErrorPacket(MysqlIO.java:3876)
?? ?at com.mysql.jdbc.MysqlIO.checkErrorPacket(MysqlIO.java:3814)
?? ?at com.mysql.jdbc.MysqlIO.sendCommand(MysqlIO.java:2478)
?? ?at com.mysql.jdbc.MysqlIO.sqlQueryDirect(MysqlIO.java:2625)
?? ?at com.mysql.jdbc.ConnectionImpl.execSQL(ConnectionImpl.java:2551)
?? ?at com.mysql.jdbc.PreparedStatement.executeInternal(PreparedStatement.java:1861)
?? ?at com.mysql.jdbc.PreparedStatement.executeUpdateInternal(PreparedStatement.java:2073)
?? ?at com.mysql.jdbc.PreparedStatement.executeBatchSerially(PreparedStatement.java:1751)
?? ?... 16 more
5928 [task-result-getter-0] ERROR org.apache.spark.scheduler.TaskSetManager ?- Task 0 in stage 0.0 failed 1 times; aborting job
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0) (192.168.248.1 executor driver): java.sql.BatchUpdateException: Data truncation: Incorrect datetime value: '1912-04-18 00:00:00' for column 'birthday' at row 1
?? ?at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
?? ?at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
?? ?at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
?? ?at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
?? ?at com.mysql.jdbc.Util.handleNewInstance(Util.java:404)
?? ?at com.mysql.jdbc.Util.getInstance(Util.java:387)
?? ?at com.mysql.jdbc.SQLError.createBatchUpdateException(SQLError.java:1154)
?? ?at com.mysql.jdbc.PreparedStatement.executeBatchSerially(PreparedStatement.java:1773)
?? ?at com.mysql.jdbc.PreparedStatement.executeBatchInternal(PreparedStatement.java:1257)
?? ?at com.mysql.jdbc.StatementImpl.executeBatch(StatementImpl.java:959)
?? ?at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.savePartition(JdbcUtils.scala:687)
?? ?at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$saveTable$1(JdbcUtils.scala:856)
?? ?at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$saveTable$1$adapted(JdbcUtils.scala:854)
?? ?at org.apache.spark.rdd.RDD.$anonfun$foreachPartition$2(RDD.scala:1020)
?? ?at org.apache.spark.rdd.RDD.$anonfun$foreachPartition$2$adapted(RDD.scala:1020)
?? ?at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2242)
?? ?at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
?? ?at org.apache.spark.scheduler.Task.run(Task.scala:131)
?? ?at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)
?? ?at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)
?? ?at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)
?? ?at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
?? ?at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
?? ?at java.lang.Thread.run(Thread.java:748)
Caused by: com.mysql.jdbc.MysqlDataTruncation: Data truncation: Incorrect datetime value: '1912-04-18 00:00:00' for column 'birthday' at row 1
?? ?at com.mysql.jdbc.MysqlIO.checkErrorPacket(MysqlIO.java:3876)
?? ?at com.mysql.jdbc.MysqlIO.checkErrorPacket(MysqlIO.java:3814)
?? ?at com.mysql.jdbc.MysqlIO.sendCommand(MysqlIO.java:2478)
?? ?at com.mysql.jdbc.MysqlIO.sqlQueryDirect(MysqlIO.java:2625)
?? ?at com.mysql.jdbc.ConnectionImpl.execSQL(ConnectionImpl.java:2551)
?? ?at com.mysql.jdbc.PreparedStatement.executeInternal(PreparedStatement.java:1861)
?? ?at com.mysql.jdbc.PreparedStatement.executeUpdateInternal(PreparedStatement.java:2073)
?? ?at com.mysql.jdbc.PreparedStatement.executeBatchSerially(PreparedStatement.java:1751)
?? ?... 16 more

Driver stacktrace:
?? ?at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2253)
?? ?at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2202)
?? ?at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2201)
?? ?at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
?? ?at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
?? ?at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
?? ?at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2201)
?? ?at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1078)
?? ?at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1078)
?? ?at scala.Option.foreach(Option.scala:407)
?? ?at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1078)
?? ?at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2440)
?? ?at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2382)
?? ?at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2371)
?? ?at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
?? ?at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:868)
?? ?at org.apache.spark.SparkContext.runJob(SparkContext.scala:2202)
?? ?at org.apache.spark.SparkContext.runJob(SparkContext.scala:2223)
?? ?at org.apache.spark.SparkContext.runJob(SparkContext.scala:2242)
?? ?at org.apache.spark.SparkContext.runJob(SparkContext.scala:2267)
?? ?at org.apache.spark.rdd.RDD.$anonfun$foreachPartition$1(RDD.scala:1020)
?? ?at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
?? ?at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
?? ?at org.apache.spark.rdd.RDD.withScope(RDD.scala:414)
?? ?at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:1018)
?? ?at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.saveTable(JdbcUtils.scala:854)
?? ?at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:82)
?? ?at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:46)
?? ?at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:70)
?? ?at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:68)
?? ?at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:90)
?? ?at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:180)
?? ?at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:218)
?? ?at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
?? ?at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:215)
?? ?at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:176)
?? ?at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:132)
?? ?at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:131)
?? ?at org.apache.spark.sql.DataFrameWriter.$anonfun$runCommand$1(DataFrameWriter.scala:989)
?? ?at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
?? ?at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
?? ?at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
?? ?at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:772)
?? ?at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
?? ?at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:989)
?? ?at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:438)
?? ?at org.apache.spark.sql.DataFrameWriter.saveInternal(DataFrameWriter.scala:415)
?? ?at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:301)
?? ?at org.apache.spark.sql.DataFrameWriter.jdbc(DataFrameWriter.scala:817)
?? ?at until.writeMysql(until.scala:40)
?? ?at environment$.writeMysqlTable(environment.scala:19)
?? ?at environment$.Mysql(environment.scala:110)
?? ?at environment$.main(environment.scala:149)
?? ?at environment.main(environment.scala)
Caused by: java.sql.BatchUpdateException: Data truncation: Incorrect datetime value: '1912-04-18 00:00:00' for column 'birthday' at row 1
?? ?at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
?? ?at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
?? ?at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
?? ?at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
?? ?at com.mysql.jdbc.Util.handleNewInstance(Util.java:404)
?? ?at com.mysql.jdbc.Util.getInstance(Util.java:387)
?? ?at com.mysql.jdbc.SQLError.createBatchUpdateException(SQLError.java:1154)
?? ?at com.mysql.jdbc.PreparedStatement.executeBatchSerially(PreparedStatement.java:1773)
?? ?at com.mysql.jdbc.PreparedStatement.executeBatchInternal(PreparedStatement.java:1257)
?? ?at com.mysql.jdbc.StatementImpl.executeBatch(StatementImpl.java:959)
?? ?at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.savePartition(JdbcUtils.scala:687)
?? ?at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$saveTable$1(JdbcUtils.scala:856)
?? ?at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$saveTable$1$adapted(JdbcUtils.scala:854)
?? ?at org.apache.spark.rdd.RDD.$anonfun$foreachPartition$2(RDD.scala:1020)
?? ?at org.apache.spark.rdd.RDD.$anonfun$foreachPartition$2$adapted(RDD.scala:1020)
?? ?at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2242)
?? ?at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
?? ?at org.apache.spark.scheduler.Task.run(Task.scala:131)
?? ?at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)
?? ?at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)
?? ?at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)
?? ?at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
?? ?at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
?? ?at java.lang.Thread.run(Thread.java:748)
Caused by: com.mysql.jdbc.MysqlDataTruncation: Data truncation: Incorrect datetime value: '1912-04-18 00:00:00' for column 'birthday' at row 1
?? ?at com.mysql.jdbc.MysqlIO.checkErrorPacket(MysqlIO.java:3876)
?? ?at com.mysql.jdbc.MysqlIO.checkErrorPacket(MysqlIO.java:3814)
?? ?at com.mysql.jdbc.MysqlIO.sendCommand(MysqlIO.java:2478)
?? ?at com.mysql.jdbc.MysqlIO.sqlQueryDirect(MysqlIO.java:2625)
?? ?at com.mysql.jdbc.ConnectionImpl.execSQL(ConnectionImpl.java:2551)
?? ?at com.mysql.jdbc.PreparedStatement.executeInternal(PreparedStatement.java:1861)
?? ?at com.mysql.jdbc.PreparedStatement.executeUpdateInternal(PreparedStatement.java:2073)
?? ?at com.mysql.jdbc.PreparedStatement.executeBatchSerially(PreparedStatement.java:1751)
?? ?... 16 more

Process finished with exit code 1
?

解决办法如下：

由于错误信息显示?Data truncation: Incorrect datetime value: '1912-04-18 00:00:00' for column 'birthday' at row 1，这通常意味着值?'1912-04-18 00:00:00'?不被 MySQL 的?TIMESTAMP?类型支持。

MySQL 的?TIMESTAMP?类型有一定的日期范围限制。它表达的时间范围大约是?1970-01-01 00:00:01 UTC?到?2038-01-19 03:14:07 UTC。如果你尝试将一个不在此范围内的时间戳存储到?TIMESTAMP?类型的列中，就会发生数据截断错误。

在你的案例中，由于?'1912-04-18'?这个日期时间值低于?TIMESTAMP?类型的下限，MySQL 就会报错。要解决这个问题，你有几个选项：

  1. 使用?DATETIME?类型代替?TIMESTAMP?类型。

    • MySQL 的?DATETIME?类型可以存储的范围是?1000-01-01 00:00:00?到?9999-12-31 23:59:59，所以更适合存储更宽的时间范围。
    • 修改你的表结构，将?birthday?列的类型从?TIMESTAMP?改为?DATETIME，然后再尝试写入数据。

  2. 如果你不希望或不能更改数据库结构，那么你需要对你的数据应用某种业务逻辑，以确保所有日期时间值都位于?TIMESTAMP?的有效范围内。可能的做法是将不在范围内的日期时间设置为一个默认值，例如?1970-01-01 00:00:01，或者在写入前从数据集中过滤掉这些值。

要修改表的列类型，你可以使用如下的 SQL 语句执行结构调整：

ALTER TABLE your_table_name 
CHANGE COLUMN birthday birthday DATETIME NOT NULL;

务必备份相关数据以防意外丢失。如果你需要在写入之前清洗数据，你可以在 Spark DataFrame 中如下操作：

import org.apache.spark.sql.functions._
import spark.implicits._

// 假设你的DataFrame是df
val processedDF = df.withColumn("birthday", 
  when($"birthday" < to_timestamp(lit("1970-01-01 00:00:01")), to_timestamp(lit("1970-01-01 00:00:01")))
  .otherwise($"birthday")
)

// 然后将processedDF写入数据库

在写入数据之后，确保你的 MySQL 连接配置和 Spark 配置项正确无误。

文章来源:https://blog.csdn.net/W2484980893/article/details/134871042
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