Structured Streaming开发过程遇到的一些bug

1.org.apache.spark.SparkException: Task not serializable

将Structured Streaming 程序打包至集群运行的时候，报了Task not serializable异常，具体错误日志忘记保存下来了，参考

https://blog.youkuaiyun.com/javastart/article/details/51206715了这篇文章，不过我的Caused by: java.io.NotSerializableException: java.lang.Object ....

经过检查发现：我的scala类里面引入了  java.lang.Long 这个包，后来将该包的引用去掉，错误解决。

2.CSV不支持以 "|"  作为分隔符，下面是SCVUtils.scala的部分源码：

/**
   * Helper method that converts string representation of a character to actual character.
   * It handles some Java escaped strings and throws exception if given string is longer than one
   * character.
   */
  @throws[IllegalArgumentException]
  def toChar(str: String): Char = {
    if (str.charAt(0) == '\\') {
      str.charAt(1)
      match {
        case 't' => '\t'
        case 'r' => '\r'
        case 'b' => '\b'
        case 'f' => '\f'
        case '\"' => '\"' // In case user changes quote char and uses \" as delimiter in options
        case '\'' => '\''
        case 'u' if str == """\u0000""" => '\u0000'
        case _ =>
          throw new IllegalArgumentException(s"Unsupported special character for delimiter: $str")
      }
    } else if (str.length == 1) {
      str.charAt(0)
    } else {
      throw new IllegalArgumentException(s"Delimiter cannot be more than one character: $str")
    }
  }

3.运行中的Structured Streming应用通过 yarn application -kill之后,重新启动Structured Streaming程序导致的报错：

java.lang.IllegalStateException: /xxxx/xxx/xxx/xxx/_spark_metadata/99.compact doesn't exist when compacting batch 109 (compactInterval: 10)
        at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4$$anonfun$apply$1.apply(CompactibleFileStreamLog.scala:174)
        at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4$$anonfun$apply$1.apply(CompactibleFileStreamLog.scala:174)
        at scala.Option.getOrElse(Option.scala:121)
        at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4.apply(CompactibleFileStreamLog.scala:173)
        at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4.apply(CompactibleFileStreamLog.scala:172)
        at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
        at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
        at scala.collection.immutable.NumericRange.foreach(NumericRange.scala:73)
        at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
        at scala.collection.AbstractTraversable.map(Traversable.scala:104)
        at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog.compact(CompactibleFileStreamLog.scala:172)
        at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog.add(CompactibleFileStreamLog.scala:156)
        at org.apache.spark.sql.execution.streaming.ManifestFileCommitProtocol.commitJob(ManifestFileCommitProtocol.scala:64)
        at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:213)
        at org.apache.spark.sql.execution.streaming.FileStreamSink.addBatch(FileStreamSink.scala:131)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3$$anonfun$apply$16.apply(MicroBatchExecution.scala:475)
        at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3.apply(MicroBatchExecution.scala:473)
        at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
        at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch(MicroBatchExecution.scala:472)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:133)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
        at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
        at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:121)
        at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:117)
        at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:279)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189)

       解决办法有多种，我的解决办法是：需要停止Stuctured Streaming程序时，在hdfs对应目录里touch一个标志文件，在driver端的程序里，定时判断该标志文件是否存在，如果存在则通过调用 StreamingQuery.stop() 来停止应用程序。参考：http://www.imooc.com/article/270337

4.字段数过多：Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.debug.maxToStringFields' in SparkEnv.conf.

在程序里修改配置参数：

spark.conf.set("spark.debug.maxToStringFields", "300") //修改最大字段数限制

5.Structured Streaming 监控hdfs文件，自动识别分区目录：

spark.conf.set("spark.sql.streaming.schemaInference", true) //自动识别分区目录

官网解释：

默认情况下，基于文件的源的结构化流需要您指定架构，而不是依靠Spark自动推断它。此限制可确保即使在出现故障的情况下，也将使用一致的架构进行流式查询。对于临时用例，您可以通过设置spark.sql.streaming.schemaInference为重新启用架构推断true。

当命名的子目录/key=value/存在且列表将自动递归到这些目录中时，确实会发生分区发现。如果这些列出现在用户提供的模式中，则Spark将根据正在读取的文件的路径填充它们。构成分区方案的目录必须在查询开始时存在，并且必须保持静态。例如，可以添加/data/year=2016/何时/data/year=2015/存在，但更改分区列（即通过创建目录/data/date=2016-04-17/）无效。

 

未完待续。。。

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