Google Cloud Dataproc 上的 IllegalStateException
IllegalStateException on Google Cloud Dataproc
我正在尝试在 Google Cloud Dataproc 上编写一个简单的普通协作过滤应用程序 运行。
数据位于 BigQuery 中。
我已经根据本教程实现了这个：https://cloud.google.com/dataproc/docs/tutorials/bigquery-sparkml
现在的问题是，当 运行 这个（稍微修改过的）示例时，我得到一个 IllegalStateException。更具体地说，这里是堆栈跟踪：
17/09/25 10:55:37 ERROR org.apache.spark.scheduler.TaskSetManager: Task 0 in stage 0.0 failed 4 times; aborting job
Traceback (most recent call last):
File "/tmp/af84ad68-0259-4ca1-b464-a118a96f0742/marketing-pages-collaborative-filtering.py", line 109, in <module>
compute_recommendations()
File "/tmp/af84ad68-0259-4ca1-b464-a118a96f0742/marketing-pages-collaborative-filtering.py", line 59, in compute_recommendations
conf=conf)
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/context.py", line 646, in newAPIHadoopRDD
File "/usr/lib/spark/python/lib/py4j-0.10.3-src.zip/py4j/java_gateway.py", line 1133, in __call__
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 63, in deco
File "/usr/lib/spark/python/lib/py4j-0.10.3-src.zip/py4j/protocol.py", line 319, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.newAPIHadoopRDD.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 (TID 3, marketing-pages-collaborative-filtering-w-1.c.dg-dev-personalization.internal): java.lang.IllegalStateException: Found known file 'data-000000000002.json' with index 2, which isn't less than or equal to than endFileNumber 1!
at com.google.cloud.hadoop.repackaged.com.google.common.base.Preconditions.checkState(Preconditions.java:197)
at com.google.cloud.hadoop.io.bigquery.DynamicFileListRecordReader.setEndFileMarkerFile(DynamicFileListRecordReader.java:327)
at com.google.cloud.hadoop.io.bigquery.DynamicFileListRecordReader.nextKeyValue(DynamicFileListRecordReader.java:177)
at org.apache.spark.rdd.NewHadoopRDD$$anon.hasNext(NewHadoopRDD.scala:182)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:389)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
at scala.collection.AbstractIterator.to(Iterator.scala:1336)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1336)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1336)
at org.apache.spark.rdd.RDD$$anonfun$take$$anonfun.apply(RDD.scala:1324)
at org.apache.spark.rdd.RDD$$anonfun$take$$anonfun.apply(RDD.scala:1324)
at org.apache.spark.SparkContext$$anonfun$runJob.apply(SparkContext.scala:1899)
at org.apache.spark.SparkContext$$anonfun$runJob.apply(SparkContext.scala:1899)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1454)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage.apply(DAGScheduler.scala:1442)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage.apply(DAGScheduler.scala:1441)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1441)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed.apply(DAGScheduler.scala:811)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed.apply(DAGScheduler.scala:811)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1667)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1622)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1611)
at org.apache.spark.util.EventLoop$$anon.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1873)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1886)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1899)
at org.apache.spark.rdd.RDD$$anonfun$take.apply(RDD.scala:1324)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:358)
at org.apache.spark.rdd.RDD.take(RDD.scala:1298)
at org.apache.spark.api.python.SerDeUtil$.pairRDDToPython(SerDeUtil.scala:203)
at org.apache.spark.api.python.PythonRDD$.newAPIHadoopRDD(PythonRDD.scala:582)
at org.apache.spark.api.python.PythonRDD.newAPIHadoopRDD(PythonRDD.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 py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.IllegalStateException: Found known file 'data-000000000002.json' with index 2, which isn't less than or equal to than endFileNumber 1!
at com.google.cloud.hadoop.repackaged.com.google.common.base.Preconditions.checkState(Preconditions.java:197)
at com.google.cloud.hadoop.io.bigquery.DynamicFileListRecordReader.setEndFileMarkerFile(DynamicFileListRecordReader.java:327)
at com.google.cloud.hadoop.io.bigquery.DynamicFileListRecordReader.nextKeyValue(DynamicFileListRecordReader.java:177)
at org.apache.spark.rdd.NewHadoopRDD$$anon.hasNext(NewHadoopRDD.scala:182)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:389)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
at scala.collection.AbstractIterator.to(Iterator.scala:1336)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1336)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1336)
at org.apache.spark.rdd.RDD$$anonfun$take$$anonfun.apply(RDD.scala:1324)
at org.apache.spark.rdd.RDD$$anonfun$take$$anonfun.apply(RDD.scala:1324)
at org.apache.spark.SparkContext$$anonfun$runJob.apply(SparkContext.scala:1899)
at org.apache.spark.SparkContext$$anonfun$runJob.apply(SparkContext.scala:1899)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
... 1 more
17/09/25 10:55:37 INFO org.spark_project.jetty.server.ServerConnector: Stopped ServerConnector@1dfdb336{HTTP/1.1}{0.0.0.0:4040}
ERROR: (gcloud.dataproc.jobs.submit.pyspark) Job [af84ad68-0259-4ca1-b464-a118a96f0742] entered state [ERROR] while waiting for [DONE].
我想我已经确定了问题所在，但我找不到问题的原因。相关的代码片段是这样的：
table_rdd = spark.sparkContext.newAPIHadoopRDD(
"com.google.cloud.hadoop.io.bigquery.JsonTextBigQueryInputFormat",
"org.apache.hadoop.io.LongWritable",
"com.google.gson.JsonObject",
conf=conf)
table_json = table_rdd.map(lambda x: x[1])
visit_data = sparkSession.read.json(table_json)
首先我根据 Google 的教程创建 RDD。下一步是从 RDD 中提取 JSON 元素，然后将其读入 table，我们可以查询。
stacktrace 显示分配 conf 时发生异常，但代码在我调用 sparkSession.read.json(table_json) 之前一直有效，因为据我所知，spark 懒惰地工作，然后才尝试访问从 BigQuery 导出的实际 JSON 文件.
现在的问题是 Spark 找到的 JSON 个文件多于应有的数量。
根据 BigQuery Hadoop 库代码中的这个 comment，即使所有内容都适合一个分片，最小值也是两个，这样 BigQuery 就可以识别导出。它还在那里说它会生成一个所谓的结束标记文件，据我所知，它只是一个空的 JSON 文件。
但是当 运行 代码时，BigQuery 生成的导出文件超过 2 个必要文件（1 个包含数据，1 个作为结束标记）。它最多生成 5 个 JSON 个文件，有时只包含来自 BigQuery 的 1 或 2 行。
我很确定这就是问题所在，导出在某种程度上是错误的。但我无法找出为什么会发生这种情况以及如何解决它。感谢任何帮助。
更新：
我尝试了其他方法。我删除了 BigQuery 中的 table 并从头开始重新填充它。这解决了导出问题。现在只有两个文件。但我认为问题仍然存在。我将尝试通过 Cloud Functions 添加一些行（这会在我的应用程序中发生），然后更新行为。
更新 2：
因此，在等待一天并使用云函数通过流式插入添加一些行之后，问题再次发生。出口以某种方式按天分区。如果每天都有自己的碎片，那将不是问题，但不幸的是，这不会发生。
这是 BigQuery 中的一个错误（它 returns 输出文件计数统计不包括零记录文件）。此问题的修复已提交，其推出将在大约一周内完成。
同时，该问题的解决方法可能是在配置 DataProc 作业时在 hadoop 配置中将标志 "mapred.bq.input.sharded.export.enable" (a.k.a. ENABLE_SHARDED_EXPORT_KEY) 设置为 false。
更新：
截至 2017 年 10 月 6 日，修复程序现已 100% 在 BigQuery 上推出。
我正在尝试在 Google Cloud Dataproc 上编写一个简单的普通协作过滤应用程序 运行。 数据位于 BigQuery 中。 我已经根据本教程实现了这个：https://cloud.google.com/dataproc/docs/tutorials/bigquery-sparkml
现在的问题是，当 运行 这个（稍微修改过的）示例时，我得到一个 IllegalStateException。更具体地说，这里是堆栈跟踪：
17/09/25 10:55:37 ERROR org.apache.spark.scheduler.TaskSetManager: Task 0 in stage 0.0 failed 4 times; aborting job
Traceback (most recent call last):
File "/tmp/af84ad68-0259-4ca1-b464-a118a96f0742/marketing-pages-collaborative-filtering.py", line 109, in <module>
compute_recommendations()
File "/tmp/af84ad68-0259-4ca1-b464-a118a96f0742/marketing-pages-collaborative-filtering.py", line 59, in compute_recommendations
conf=conf)
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/context.py", line 646, in newAPIHadoopRDD
File "/usr/lib/spark/python/lib/py4j-0.10.3-src.zip/py4j/java_gateway.py", line 1133, in __call__
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 63, in deco
File "/usr/lib/spark/python/lib/py4j-0.10.3-src.zip/py4j/protocol.py", line 319, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.newAPIHadoopRDD.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 (TID 3, marketing-pages-collaborative-filtering-w-1.c.dg-dev-personalization.internal): java.lang.IllegalStateException: Found known file 'data-000000000002.json' with index 2, which isn't less than or equal to than endFileNumber 1!
at com.google.cloud.hadoop.repackaged.com.google.common.base.Preconditions.checkState(Preconditions.java:197)
at com.google.cloud.hadoop.io.bigquery.DynamicFileListRecordReader.setEndFileMarkerFile(DynamicFileListRecordReader.java:327)
at com.google.cloud.hadoop.io.bigquery.DynamicFileListRecordReader.nextKeyValue(DynamicFileListRecordReader.java:177)
at org.apache.spark.rdd.NewHadoopRDD$$anon.hasNext(NewHadoopRDD.scala:182)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:389)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
at scala.collection.AbstractIterator.to(Iterator.scala:1336)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1336)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1336)
at org.apache.spark.rdd.RDD$$anonfun$take$$anonfun.apply(RDD.scala:1324)
at org.apache.spark.rdd.RDD$$anonfun$take$$anonfun.apply(RDD.scala:1324)
at org.apache.spark.SparkContext$$anonfun$runJob.apply(SparkContext.scala:1899)
at org.apache.spark.SparkContext$$anonfun$runJob.apply(SparkContext.scala:1899)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1454)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage.apply(DAGScheduler.scala:1442)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage.apply(DAGScheduler.scala:1441)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1441)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed.apply(DAGScheduler.scala:811)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed.apply(DAGScheduler.scala:811)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1667)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1622)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1611)
at org.apache.spark.util.EventLoop$$anon.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1873)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1886)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1899)
at org.apache.spark.rdd.RDD$$anonfun$take.apply(RDD.scala:1324)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:358)
at org.apache.spark.rdd.RDD.take(RDD.scala:1298)
at org.apache.spark.api.python.SerDeUtil$.pairRDDToPython(SerDeUtil.scala:203)
at org.apache.spark.api.python.PythonRDD$.newAPIHadoopRDD(PythonRDD.scala:582)
at org.apache.spark.api.python.PythonRDD.newAPIHadoopRDD(PythonRDD.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.IllegalStateException: Found known file 'data-000000000002.json' with index 2, which isn't less than or equal to than endFileNumber 1!
at com.google.cloud.hadoop.repackaged.com.google.common.base.Preconditions.checkState(Preconditions.java:197)
at com.google.cloud.hadoop.io.bigquery.DynamicFileListRecordReader.setEndFileMarkerFile(DynamicFileListRecordReader.java:327)
at com.google.cloud.hadoop.io.bigquery.DynamicFileListRecordReader.nextKeyValue(DynamicFileListRecordReader.java:177)
at org.apache.spark.rdd.NewHadoopRDD$$anon.hasNext(NewHadoopRDD.scala:182)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:389)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
at scala.collection.AbstractIterator.to(Iterator.scala:1336)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1336)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1336)
at org.apache.spark.rdd.RDD$$anonfun$take$$anonfun.apply(RDD.scala:1324)
at org.apache.spark.rdd.RDD$$anonfun$take$$anonfun.apply(RDD.scala:1324)
at org.apache.spark.SparkContext$$anonfun$runJob.apply(SparkContext.scala:1899)
at org.apache.spark.SparkContext$$anonfun$runJob.apply(SparkContext.scala:1899)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
... 1 more
17/09/25 10:55:37 INFO org.spark_project.jetty.server.ServerConnector: Stopped ServerConnector@1dfdb336{HTTP/1.1}{0.0.0.0:4040}
ERROR: (gcloud.dataproc.jobs.submit.pyspark) Job [af84ad68-0259-4ca1-b464-a118a96f0742] entered state [ERROR] while waiting for [DONE].
我想我已经确定了问题所在，但我找不到问题的原因。相关的代码片段是这样的：
table_rdd = spark.sparkContext.newAPIHadoopRDD(
"com.google.cloud.hadoop.io.bigquery.JsonTextBigQueryInputFormat",
"org.apache.hadoop.io.LongWritable",
"com.google.gson.JsonObject",
conf=conf)
table_json = table_rdd.map(lambda x: x[1])
visit_data = sparkSession.read.json(table_json)
首先我根据 Google 的教程创建 RDD。下一步是从 RDD 中提取 JSON 元素，然后将其读入 table，我们可以查询。
stacktrace 显示分配 conf 时发生异常，但代码在我调用 sparkSession.read.json(table_json) 之前一直有效，因为据我所知，spark 懒惰地工作，然后才尝试访问从 BigQuery 导出的实际 JSON 文件.
现在的问题是 Spark 找到的 JSON 个文件多于应有的数量。 根据 BigQuery Hadoop 库代码中的这个 comment，即使所有内容都适合一个分片，最小值也是两个，这样 BigQuery 就可以识别导出。它还在那里说它会生成一个所谓的结束标记文件，据我所知，它只是一个空的 JSON 文件。
但是当 运行 代码时，BigQuery 生成的导出文件超过 2 个必要文件（1 个包含数据，1 个作为结束标记）。它最多生成 5 个 JSON 个文件，有时只包含来自 BigQuery 的 1 或 2 行。
我很确定这就是问题所在，导出在某种程度上是错误的。但我无法找出为什么会发生这种情况以及如何解决它。感谢任何帮助。
更新：
我尝试了其他方法。我删除了 BigQuery 中的 table 并从头开始重新填充它。这解决了导出问题。现在只有两个文件。但我认为问题仍然存在。我将尝试通过 Cloud Functions 添加一些行（这会在我的应用程序中发生），然后更新行为。
更新 2：
因此，在等待一天并使用云函数通过流式插入添加一些行之后，问题再次发生。出口以某种方式按天分区。如果每天都有自己的碎片，那将不是问题，但不幸的是，这不会发生。
这是 BigQuery 中的一个错误（它 returns 输出文件计数统计不包括零记录文件）。此问题的修复已提交，其推出将在大约一周内完成。
同时，该问题的解决方法可能是在配置 DataProc 作业时在 hadoop 配置中将标志 "mapred.bq.input.sharded.export.enable" (a.k.a. ENABLE_SHARDED_EXPORT_KEY) 设置为 false。
更新：
截至 2017 年 10 月 6 日，修复程序现已 100% 在 BigQuery 上推出。