当我执行以下代码行时，我得到以下堆栈跟踪:
Hi I am getting the following stack trace when I execute the following lines of code:
transactionDF.write.format("jdbc")
.option("url",SqlServerUri)
.option("driver", driver)
.option("dbtable", fullQualifiedName)
.option("user", SqlServerUser).option("password",SqlServerPassword)
.mode(SaveMode.Append).save()
以下是堆栈跟踪:
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply_3$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
at org.apache.spark.sql.execution.LocalTableScanExec$$anonfun$1.apply(LocalTableScanExec.scala:41)
at org.apache.spark.sql.execution.LocalTableScanExec$$anonfun$1.apply(LocalTableScanExec.scala:41)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.AbstractTraversable.map(Traversable.scala:104)
at org.apache.spark.sql.execution.LocalTableScanExec.<init>(LocalTableScanExec.scala:41)
at org.apache.spark.sql.execution.SparkStrategies$BasicOperators$.apply(SparkStrategies.scala:394)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:62)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:62)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439)
at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:92)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:77)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:74)
at scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157)
at scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:157)
at scala.collection.AbstractIterator.foldLeft(Iterator.scala:1336)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2.apply(QueryPlanner.scala:74)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2.apply(QueryPlanner.scala:66)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:92)
at org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:84)
at org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:80)
at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:89)
at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:89)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$toString$3.apply(QueryExecution.scala:237)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$toString$3.apply(QueryExecution.scala:237)
at org.apache.spark.sql.execution.QueryExecution.stringOrError(QueryExecution.scala:112)
at org.apache.spark.sql.execution.QueryExecution.toString(QueryExecution.scala:237)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:54)
at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2788)
at org.apache.spark.sql.Dataset.foreachPartition(Dataset.scala:2319)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.saveTable(JdbcUtils.scala:670)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:77)
at org.apache.spark.sql.execution.datasources.DataSource.write(DataSource.scala:518)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:215)
at com.test.spark.jobs.ingestion.test$.main(test.scala:193)
at com.test.spark.jobs.ingestion.test.main(test.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:743)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:187)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:212)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:126)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
我尝试调试它，我相信查询执行会给出空指针异常
I tried debugging it and I believe query execution is giving null pointer exception
我不确定这意味着什么.我在我的本地机器上运行它，而不是在任何集群上
I am not sure what it means. I am running this on my local machine and not on any cluster
任何帮助将不胜感激.
我想通了(Alteast 我认为这就是原因).对于面临类似情况的其他人:在创建表时，我将每一列都设置为空，因此我认为它允许在表中插入空值.但是我正在构建数据框的 Avro 模式具有可空性 = false.因此，dataframe.create 正在读取 null 并因此引发 NPE 错误.当我执行 Dataframe.write 时出现错误(这让我认为这是一个 jdbc 错误)但实际的 NPE 在创建数据帧时发生
I figured it out (Alteast I think this is the reason). For others facing a similar situation: While I was creating the table, I made every column as null so I assumed it would allow null insertion in the table. But the Avro schema I was building the dataframe had nullable = false. So, dataframe.create was reading null and hence raising a NPE error. The error was raised when I did Dataframe.write (which made me think it was a jdbc error) but the actual NPE happened while creating the dataframe
这篇关于Spark在执行jdbc保存时给出空指针异常的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持跟版网！