如何从Kafka读取JSON数据并使用Spark结构化流存储到HDFS？

Question

我正在尝试从Kafka读取JSON消息并将它们存储在具有火花结构化流的HDFS中。

我遵循了下面的示例，当我的代码如下所示时：

df = spark \
  .read \
  .format("kafka") \
  .option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
  .option("subscribe", "topic1") \
  .load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
df.writeStream.format("json").option("checkpointLocation", "some/hdfs/path").start(/data")

然后我得到hdfs中具有二进制值的行。

{"value":"BINARY DATA","topic":"test_hdfs2","partition":0,"offset":3463075,"timestamp":"2018-07-24T20:51:33.655Z","timestampType":0}

这些行按预期连续写入，但采用二进制格式。

我发现了这个帖子：

https://databricks.com/blog/2017/04/26/processing-data-in-apache-kafka-with-structured-streaming-in-apache-spark-2-2.html

我正在尝试实现这个示例：

schema = StructType().add("a", IntegerType()).add("b", StringType())
df.select( \
  col("key").cast("string"),
  from_json(col("value").cast("string"), schema))

但是在这里我得到了一个奇怪的behvaiur。我有一个写入hdfs的小文件，其中包含多个空json行-{}

作业很快就会失败，出现以下异常：

18/07/24 22:25:47 ERROR datasources.FileFormatWriter: Aborting job
null. java.lang.IllegalStateException:
hdfs://SOME_PATH/_spark_metadata/399.compact doesn't exist when
compacting batch 409 (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:123)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3$$anonfun$apply$16.apply(MicroBatchExecution.scala:477)
        at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3.apply(MicroBatchExecution.scala:475)
        at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
        at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch(MicroBatchExecution.scala:474)
        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$$anon$1.run(StreamExecution.scala:189)
18/07/24 22:25:47 ERROR streaming.MicroBatchExecution: Query [id =
4f6c4ebc-f330-4697-b2db-7989b93dfba3, runId =
57575397-9fda-4370-9dcb-4550ae1576ec] terminated with error
org.apache.spark.SparkException: Job aborted.
        at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:224)
        at org.apache.spark.sql.execution.streaming.FileStreamSink.addBatch(FileStreamSink.scala:123)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3$$anonfun$apply$16.apply(MicroBatchExecution.scala:477)
        at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3.apply(MicroBatchExecution.scala:475)
        at org.apache.spark.sql.execution.streaming.ProgressReporter$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$$runBatch(MicroBatchExecution.scala:474)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$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)
Caused by: java.lang.IllegalStateException:
hdfs://SOME_PATH/_spark_metadata/399.compact doesn't exist when
compacting batch 409 (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)
        ... 17 more

你知道如何以正确的方式实现吗？

Answer 1

如果您看到错误（399.compact在压缩批次409（compactInterval：10）时不存在），那么它清楚地表明错误是由于找不到压缩文件夹。基本上，在spark结构化流媒体中，每次批处理运行都会创建一个。压缩\u spark\u metadata下的文件夹，并为了防止过去运行时产生大量压缩文件的开销，它会定期尝试合并这些文件。

我认为默认是每10个批次尝试压缩一次。所以在这里，当它运行批次409并尝试压缩它时，它没有找到前一个并失败。一种选择是设置。这不是真正的业务错误，只会引发簿记错误，并防止您的应用程序终止在下面添加失败OnDataLoss为false。

spark.readStream
      .format("kafka")
      .option("kafka.bootstrap.servers", conf.servers)
      .option("subscribe", conf.topics)

      .option("failOnDataLoss",false)

使用以下特性增加压实间隔

spark.conf.set("spark.sql.streaming.fileSink.log.cleanupDelay", 60000)