Apache spark Mesos上Apache Spark的自定义状态存储提供程序

我已经为Apache Spark 2.3.0编写了自定义状态存储和状态存储提供程序，并尝试使用附加参数部署作业：

--conf spark.sql.streaming.stateStore.providerClass=com.sample.state.CustomStateStoreProvider

对于运行Spark作业，我使用Marathon和Mesos，作业在启动后立即失败，例外情况如下：

java.lang.ClassNotFoundException: com.sample.state.CustomStateStoreProvider 
    at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
    at java.lang.Class.forName0(Native Method)
    at java.lang.Class.forName(Class.java:348)
    at org.apache.spark.util.Utils$.classForName(Utils.scala:235)
    at org.apache.spark.sql.execution.streaming.state.StateStoreProvider$.create(StateStore.scala:213)
    at org.apache.spark.sql.execution.streaming.StateStoreWriter$class.stateStoreCustomMetrics(statefulOperators.scala:121)
    at org.apache.spark.sql.execution.streaming.StateStoreWriter$class.metrics(statefulOperators.scala:86)
    at org.apache.spark.sql.execution.streaming.StateStoreSaveExec.metrics$lzycompute(statefulOperators.scala:251)
    at org.apache.spark.sql.execution.streaming.StateStoreSaveExec.metrics(statefulOperators.scala:251)
    at org.apache.spark.sql.execution.SparkPlanInfo$.fromSparkPlan(SparkPlanInfo.scala:58)
    at org.apache.spark.sql.execution.SparkPlanInfo$$anonfun$fromSparkPlan$1.apply(SparkPlanInfo.scala:62)
    at org.apache.spark.sql.execution.SparkPlanInfo$$anonfun$fromSparkPlan$1.apply(SparkPlanInfo.scala:62)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
    at scala.collection.immutable.List.map(List.scala:285)
    at org.apache.spark.sql.execution.SparkPlanInfo$.fromSparkPlan(SparkPlanInfo.scala:62)
    at org.apache.spark.sql.execution.SparkPlanInfo$$anonfun$fromSparkPlan$1.apply(SparkPlanInfo.scala:62)
    at org.apache.spark.sql.execution.SparkPlanInfo$$anonfun$fromSparkPlan$1.apply(SparkPlanInfo.scala:62)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
    at scala.collection.immutable.List.map(List.scala:285)
    at org.apache.spark.sql.execution.SparkPlanInfo$.fromSparkPlan(SparkPlanInfo.scala:62)
    at org.apache.spark.sql.execution.SparkPlanInfo$$anonfun$fromSparkPlan$1.apply(SparkPlanInfo.scala:62)
    at org.apache.spark.sql.execution.SparkPlanInfo$$anonfun$fromSparkPlan$1.apply(SparkPlanInfo.scala:62)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
    at scala.collection.immutable.List.map(List.scala:285)
    at org.apache.spark.sql.execution.SparkPlanInfo$.fromSparkPlan(SparkPlanInfo.scala:62)
    at org.apache.spark.sql.execution.SparkPlanInfo$$anonfun$fromSparkPlan$1.apply(SparkPlanInfo.scala:62)
    at org.apache.spark.sql.execution.SparkPlanInfo$$anonfun$fromSparkPlan$1.apply(SparkPlanInfo.scala:62)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
    at scala.collection.immutable.List.map(List.scala:285)
    at org.apache.spark.sql.execution.SparkPlanInfo$.fromSparkPlan(SparkPlanInfo.scala:62)
    at org.apache.spark.sql.execution.SparkPlanInfo$$anonfun$fromSparkPlan$1.apply(SparkPlanInfo.scala:62)
    at org.apache.spark.sql.execution.SparkPlanInfo$$anonfun$fromSparkPlan$1.apply(SparkPlanInfo.scala:62)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
    at scala.collection.immutable.List.map(List.scala:285)
    at org.apache.spark.sql.execution.SparkPlanInfo$.fromSparkPlan(SparkPlanInfo.scala:62)
    at org.apache.spark.sql.execution.SparkPlanInfo$$anonfun$fromSparkPlan$1.apply(SparkPlanInfo.scala:62)
    at org.apache.spark.sql.execution.SparkPlanInfo$$anonfun$fromSparkPlan$1.apply(SparkPlanInfo.scala:62)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
    at scala.collection.immutable.List.map(List.scala:285)
    at org.apache.spark.sql.execution.SparkPlanInfo$.fromSparkPlan(SparkPlanInfo.scala:62)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75)
    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)

以下是运行作业的命令：

/spark/bin/spark-submit \
    --repositories "http://127.0.0.1:80/sbt-all" \
    --packages com.sample:pipelines:0.1.0 \
    --class com.sample.TestApplication \
    --conf spark.sql.streaming.stateStore.providerClass=com.sample.state.CustomStateStoreProvider \
    /spark/examples/jars/spark-examples_2.11-2.3.0.jar

com.sample.TestApplication

和

com.sample.state.CustomStateStoreProvider

这两个类都位于

com.sample:pipelines:0.1.0

包中，我已经检查了好几次了。如果没有

spark.sql.streaming.stateStore.providerClass

参数，应用程序将启动并运行良好

我已经尝试使用驱动程序和执行程序的附加类路径提交作业，并使用

--jars

参数将JAR放在HDFS中或通过HTTP提交作业

---

注意：当我尝试在本地启动作业时，没有任何问题，并且在这种情况下一切正常。

通常，需要将

spark.sql.streaming.stateStore.providerClass

参数的值括在引号中：

--conf spark.sql.streaming.stateStore.providerClass=“com.sample.state.CustomStateStoreProvider”

。没有它，值后的空格将包含在值中，Spark将查找

com.sample.state.CustomStateStoreProvider

类（行尾有空格符号）其他一切都很好：）

嗨，Alex你能为状态存储粘贴你的代码样本吗？我在spark 2.3上加入streams时也遇到了HDFS state storm OOM的问题。@Arnnrodman嗨，我会问我的经理，如果一切正常，我想把我的代码作为库共享。@Arnnrodman现在您可以使用我的存储库中的一些代码作为示例。Thx@AlexChermenin我正在尝试两种方法：flatMapGroupsWithState/mapwithstate与Spark 2.3高级API流到流与您的代码连接（rocks…）