Spark Cassandra Connector中错误的查询错误处理

Question

我有一个Spark Streaming应用程序，它有多个数据流(DStreams)写在同一个Cassandra表中。当在大量随机数据上测试我的应用程序时，我从Spark Cassandra Connector接收到一个错误，其中几乎没有有助于调试的信息。错误看起来像这样:

java.util.concurrent.ExecutionException: com.datastax.driver.core.exceptions.InvalidQueryException: Key may not be empty
    at com.baynote.shaded.com.google.common.util.concurrent.AbstractFuture$Sync.getValue(AbstractFuture.java:299)
    at com.baynote.shaded.com.google.common.util.concurrent.AbstractFuture$Sync.get(AbstractFuture.java:286)
    at com.baynote.shaded.com.google.common.util.concurrent.AbstractFuture.get(AbstractFuture.java:116)
    at com.datastax.spark.connector.rdd.CassandraJoinRDD$$anonfun$fetchIterator$1.apply(CassandraJoinRDD.scala:268)
    at com.datastax.spark.connector.rdd.CassandraJoinRDD$$anonfun$fetchIterator$1.apply(CassandraJoinRDD.scala:268)
    at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
    at com.datastax.spark.connector.util.CountingIterator.hasNext(CountingIterator.scala:12)
    at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
    at org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:189)
    at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:64)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
    at org.apache.spark.scheduler.Task.run(Task.scala:89)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
Caused by: com.datastax.driver.core.exceptions.InvalidQueryException: Key may not be empty
    at com.datastax.driver.core.Responses$Error.asException(Responses.java:136)
    at com.datastax.driver.core.DefaultResultSetFuture.onSet(DefaultResultSetFuture.java:179)
    at com.datastax.driver.core.RequestHandler.setFinalResult(RequestHandler.java:184)
    at com.datastax.driver.core.RequestHandler.access$2500(RequestHandler.java:43)
    at com.datastax.driver.core.RequestHandler$SpeculativeExecution.setFinalResult(RequestHandler.java:798)
    at com.datastax.driver.core.RequestHandler$SpeculativeExecution.onSet(RequestHandler.java:617)
    at com.datastax.driver.core.Connection$Dispatcher.channelRead0(Connection.java:1005)
    at com.datastax.driver.core.Connection$Dispatcher.channelRead0(Connection.java:928)
    at io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:105)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
    at io.netty.handler.timeout.IdleStateHandler.channelRead(IdleStateHandler.java:266)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
    at io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:103)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
    at io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:244)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
    at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:846)
    at io.netty.channel.epoll.AbstractEpollStreamChannel$EpollStreamUnsafe.epollInReady(AbstractEpollStreamChannel.java:831)
    at io.netty.channel.epoll.EpollEventLoop.processReady(EpollEventLoop.java:346)
    at io.netty.channel.epoll.EpollEventLoop.run(EpollEventLoop.java:254)
    at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:111)
    ... 1 more

问题是，我不能告诉哪个DStream，哪个数据导致它。我可以检查每个写到Cassandra的DStream，或者写我自己的数据验证器，但我正在寻找一个更通用的解决方案。

另一个问题是，如果错误杀死整个作业，而不是忽略它并继续写入其他数据。基本上，在简单的非spark写入的情况下，我会捕获异常，记录它并继续写入其余的数据。有一种方法可以在Spark Cassandra连接器中做这样的事情吗?

对于这两个问题，我能做些什么吗?

Answer 1

我认为我们应该考虑两种情况:

1. 验证输入数据以确保Key(在cassandra列中)的数据不是Null或无效的数据格式

2. 你的数据是RDD，所以你可以在调用保存方法之前排序忽略无效数据