存算分离3.3.12版本插入StarRocks报错

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【详述】使用Spark Connector工具,执行Spark DataFrame 插入到StarRocks,报错
【背景】前两天从3.3.3 升级到 3.3.12版本
【业务影响】
【是否存算分离】是
【StarRocks版本】3.3.12
【集群规模】3fe + 7be(fe与be独立部署)
【机器信息】48C/148G/万兆
【联系方式】tanheyuan@outlook.com
【附件】

  • fe.log/beINFO/相应截图
  • 报错信息:
    org.apache.spark.SparkException: Job aborted due to stage failure:
    Aborting TaskSet 80.0 because task 8 (partition 8)
    cannot run anywhere due to node and executor blacklist.
    Most recent failure:
    Lost task 8.0 in stage 80.0 (TID 68091, xxxx.com, executor 1): java.io.IOException: Failed to load 20000 batch data on BE: http://IP:8040/api/bi_test/business_ph_on_order/_stream_load? node and exceeded the max 3 retry times.
    at com.starrocks.connector.spark.sql.StarrocksSourceProvider$$anonfun$createRelation$1$$anonfun$com$starrocks$connector$spark$sql$StarrocksSourceProvider$$anonfun$$flush$1$1.apply$mcV$sp(StarrocksSourceProvider.scala:128)
    at scala.util.control.Breaks.breakable(Breaks.scala:38)
    at com.starrocks.connector.spark.sql.StarrocksSourceProvider$$anonfun$createRelation$1.com$starrocks$connector$spark$sql$StarrocksSourceProvider$$anonfun$$flush$1(StarrocksSourceProvider.scala:102)
    at com.starrocks.connector.spark.sql.StarrocksSourceProvider$$anonfun$createRelation$1.apply(StarrocksSourceProvider.scala:92)
    at com.starrocks.connector.spark.sql.StarrocksSourceProvider$$anonfun$createRelation$1.apply(StarrocksSourceProvider.scala:77)
    at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$28.apply(RDD.scala:979)
    at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$28.apply(RDD.scala:979)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2274)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2274)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:123)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:413)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1551)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:419)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)
    Caused by: com.starrocks.connector.spark.exception.StreamLoadException: stream load error: Automatically created partitions exceeded the maximum limit: 4096. You can modify this restriction on by setting max_automatic_partition_number larger.
    at com.starrocks.connector.spark.StarRocksStreamLoad.load(StarRocksStreamLoad.java:212)
    at com.starrocks.connector.spark.StarRocksStreamLoad.loadV2(StarRocksStreamLoad.java:196)
    at com.starrocks.connector.spark.sql.StarrocksSourceProvider$$anonfun$createRelation$1$$anonfun$com$starrocks$connector$spark$sql$StarrocksSourceProvider$$anonfun$$flush$1$1$$anonfun$apply$mcV$sp$1.apply$mcVI$sp(StarrocksSourceProvider.scala:106)
    at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:160)
    at com.starrocks.connector.spark.sql.StarrocksSourceProvider$$anonfun$createRelation$1$$anonfun$com$starrocks$connector$spark$sql$StarrocksSourceProvider$$anonfun$$flush$1$1.apply$mcV$sp(StarrocksSourceProvider.scala:104)
    … 16 more

Blacklisting behavior can be configured via spark.blacklist.*.

Caused by:
org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:2027)
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1972)
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1971)
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1971)
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:987)
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:987)
scala.Option.foreach(Option.scala:257)
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:987)
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2207)
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2156)
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2145)
org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:794)
org.apache.spark.SparkContext.runJob(SparkContext.scala:2234)
org.apache.spark.SparkContext.runJob(SparkContext.scala:2255)
org.apache.spark.SparkContext.runJob(SparkContext.scala:2274)
org.apache.spark.SparkContext.runJob(SparkContext.scala:2299)

Automatically created partitions exceeded the maximum limit: 4096. You can modify this restriction on by setting max_automatic_partition_number larger.