PredictionIO+Universal Recommender虽然可以帮助中小企业快速的搭建部署基于用户行为协同过滤的个性化推荐引擎，单纯从引擎层面来看，开发成本近乎于零，但仍然需要一些前提条件。比如说，组织内部最好已经搭建了较稳定的Hadoop，Spark集群，至少要拥有一部分熟悉Spark平台的开发和运维人员，否则会需要技术团队花费很长时间来踩坑，试错。
本文列举了一些PredictionIO+Universal Recommender的使用过程中会遇到的Spark平台相关的异常信息，以及其解决思路和最终的解决办法，供参考。
1，执行训练时，发生java.lang.StackOverflowError错误
这个问题比较简单，查看文档，执行训练时，通过参数指定内存大小可以避免该问题，例如：
pio train -- --driver-memory 8g --executor-memory 8g --verbose
2，执行训练时，发生找不到EmptyRDD方法的错误
Exception in thread "main" java.lang.NoSuchMethodError: org.apache.spark.SparkContext.emptyRDD(Lscala/reflect/ClassTag;)Lorg/apache/spark/rdd/EmptyRDD; at com.actionml.URAlgorithm.getRanksRDD(URAlgorithm.scala:534) at com.actionml.URAlgorithm.calcAll(URAlgorithm.scala:340) at com.actionml.URAlgorithm.train(URAlgorithm.scala:285) at com.actionml.URAlgorithm.train(URAlgorithm.scala:175)
这个是编译和执行环境的Spark版本不一致导致的。
Spark2.1.1 ，查看github上的spark源码发现
这个emptyRDD方法，虽然存在
/** Get an RDD that has no partitions or elements. */def emptyRDD[T: ClassTag]: RDD[T] = new EmptyRDD[T](this)
返回值类型和老版本相比，却发生了变化，不是EmptyRDD。所以在1.4.0下编译通过，2.1.1下执行失败。该方法的不同版本产生了不兼容。
如果采用我上一篇备忘录中所记述的方式修改过build.sbt，是可以避免这个问题的。
3，yarn和spark使用的jersey版本不一致的问题
[INFO] [ServerConnector] Started ServerConnector@bd93bc3{HTTP/1.1}{0.0.0.0:4040}
[INFO] [Server] Started @6428ms
Exception in thread "main" java.lang.NoClassDefFoundError: com/sun/jersey/api/client/config/ClientConfig
at org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:55)
at org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.createTimelineClient(YarnClientImpl.java:181)
at org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:168)
at org.apache.hadoop.service.AbstractService.init(AbstractService.java:163)
at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:151)
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56)
at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:156)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:509)
at org.apache.predictionio.workflow.WorkflowContext$.apply(WorkflowContext.scala:45)
at org.apache.predictionio.workflow.CoreWorkflow$.runTrain(CoreWorkflow.scala:59)
at org.apache.predictionio.workflow.CreateWorkflow$.main(CreateWorkflow.scala:250)
at org.apache.predictionio.workflow.CreateWorkflow.main(CreateWorkflow.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:497)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:738)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:187)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:212)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:126)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.ClassNotFoundException: com.sun.jersey.api.client.config.ClientConfig
at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
... 21 more
修改方法：engine.json中的sparkConf中设置
"spark.hadoop.yarn.timeline-service.enabled": "false",
更深入了解此问题，参考：https://markobigdata.com/2016/08/01/apache-spark-2-0-0-installation-and-configuration/
4，yarn的空参数处理BUG
[INFO] [ContextHandler] Stopped o.s.j.s.ServletContextHandler@7772d266{/jobs,null,UNAVAILABLE}
[WARN] [YarnSchedulerBackend$YarnSchedulerEndpoint] Attempted to request executors before the AM has registered!
[WARN] [MetricsSystem] Stopping a MetricsSystem that is not running
Exception in thread "main" java.lang.ArrayIndexOutOfBoundsException: 1
at org.apache.spark.deploy.yarn.YarnSparkHadoopUtil$$anonfun$setEnvFromInputString$1.apply(YarnSparkHadoopUtil.scala:154)
at org.apache.spark.deploy.yarn.YarnSparkHadoopUtil$$anonfun$setEnvFromInputString$1.apply(YarnSparkHadoopUtil.scala:152)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at org.apache.spark.deploy.yarn.YarnSparkHadoopUtil$.setEnvFromInputString(YarnSparkHadoopUtil.scala:152)
at org.apache.spark.deploy.yarn.Client$$anonfun$setupLaunchEnv$6.apply(Client.scala:775)
at org.apache.spark.deploy.yarn.Client$$anonfun$setupLaunchEnv$6.apply(Client.scala:773)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.deploy.yarn.Client.setupLaunchEnv(Client.scala:773)
at org.apache.spark.deploy.yarn.Client.createContainerLaunchContext(Client.scala:867)
at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:170)
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56)
at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:156)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:509)
at org.apache.predictionio.workflow.WorkflowContext$.apply(WorkflowContext.scala:45)
at org.apache.predictionio.workflow.CoreWorkflow$.runTrain(CoreWorkflow.scala:59)
at org.apache.predictionio.workflow.CreateWorkflow$.main(CreateWorkflow.scala:250)
at org.apache.predictionio.workflow.CreateWorkflow.main(CreateWorkflow.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:497)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:738)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:187)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:212)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:126)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
是yarn的一个bug，无法正常处理空参数
解决方式：修改spark-env.sh，强制设置一个假参数，可以绕过这个问题
修改 spark/conf/spark-env.sh，增加下面这句话
export SPARK_YARN_USER_ENV="HADOOP_CONF_DIR=/home/hadoop/apache-hadoop/etc/hadoop"
5，yarn的软连接BUG
[WARN] [TaskSetManager] Lost task 3.0 in stage 173.0 (TID 250, bigdata01, executor 3): java.lang.Error: Multiple ES-Hadoop versions detected in the classpath; please use only one jar:file:/home/hadoop/apache-hadoop/hadoop/var/yarn/local-dir/usercache/hadoop/appcache/application_1504083960020_0030/container_e235_1504083960020_0030_01_000005/universal-recommender-assembly-0.6.0-deps.jar jar:file:/home/hadoop/apache-hadoop/hadoop-2.7.2/var/yarn/local-dir/usercache/hadoop/appcache/application_1504083960020_0030/container_e235_1504083960020_0030_01_000005/universal-recommender-assembly-0.6.0-deps.jar at org.elasticsearch.hadoop.util.Version.<clinit>(Version.java:73) at org.elasticsearch.hadoop.rest.RestService.createWriter(RestService.java:570) at org.elasticsearch.spark.rdd.EsRDDWriter.write(EsRDDWriter.scala:58) at org.elasticsearch.spark.rdd.EsSpark$$anonfun$doSaveToEs$1.apply(EsSpark.scala:107) at org.elasticsearch.spark.rdd.EsSpark$$anonfun$doSaveToEs$1.apply(EsSpark.scala:107) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) at org.apache.spark.scheduler.Task.run(Task.scala:99) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745)
这不知道算不算一个BUG，总之，yarn的配置中如果使用了软连接来指定hadoop文件夹的路径，将有可能发生此问题。参考 https://interset.zendesk.com/hc/en-us/articles/230751687-PhoenixToElasticSearchJob-Fails-with-Multiple-ES-Hadoop-versions-detected-in-the-classpath-
解决方式也很简单，nodemanager修改所有采用Hadoop文件夹的软连接的配置，改为真正的路径即可。
6，Spark的JOB执行出错
[WARN] [Utils] Service 'sparkDriver' could not bind on port 0. Attempting port 1. [ERROR] [SparkContext] Error initializing SparkContext. Exception in thread "main" java.net.BindException: Cannot assign requested address: Service 'sparkDriver' failed after 60 retries (starting from 0)! Consider explicitly setting the appropriate port for the service 'sparkDriver' (for example spark.ui.port for SparkUI) to an available port or increasing spark.port.maxRetries. at sun.nio.ch.Net.bind0(Native Method) at sun.nio.ch.Net.bind(Net.java:437) at sun.nio.ch.Net.bind(Net.java:429) at sun.nio.ch.ServerSocketChannelImpl.bind(ServerSocketChannelImpl.java:223) at io.netty.channel.socket.nio.NioServerSocketChannel.doBind(NioServerSocketChannel.java:127) at io.netty.channel.AbstractChannel$AbstractUnsafe.bind(AbstractChannel.java:501) at io.netty.channel.DefaultChannelPipeline$HeadContext.bind(DefaultChannelPipeline.java:1218) at io.netty.channel.AbstractChannelHandlerContext.invokeBind(AbstractChannelHandlerContext.java:506) at io.netty.channel.AbstractChannelHandlerContext.bind(AbstractChannelHandlerContext.java:491) at io.netty.channel.DefaultChannelPipeline.bind(DefaultChannelPipeline.java:965) at io.netty.channel.AbstractChannel.bind(AbstractChannel.java:210) at io.netty.bootstrap.AbstractBootstrap$2.run(AbstractBootstrap.java:353) at io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:408) at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:455) at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:140) at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:144) at java.lang.Thread.run(Thread.java:745)
这个错误,网上的有很多文章让修改spark-env.sh ,增加 export SPARK_LOCAL_IP="127.0.0.1"
但这些网文其实只适用于单机SPARK的情况。这个IP是SPARK回调本机的地址，所以应该设置为本机的IP地址（用ifconfig查看本机真实IP）