排查过程：在EMR集群上按小时跑的spark sql 任务有时会失败，在driver端的日志中可以看到报错： org.apache.spark.sql.catalyst.errors.package$TreeNodeException...图片查看错误栈对应的代码 org.apache.spark.sql.execution.exchange.BroadcastExchangeExec....$anonfun$relationFuture$1(BroadcastExchangeExec.scala:169)错误栈：Caused by: org.apache.spark.util.SparkFatalExceptionat...org.apache.spark.sql.execution.exchange.BroadcastExchangeExec..../spark/blob/branch-3.0/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/BroadcastExchangeExec.scala
在spark开发过程中，一直想在程序中进行master的开发，如下代码： val conf = new SparkConf().setMaster("spark://hostname:7077").setAppName...("Spark Pi") 但是直接进行此项操作，老是碰到org.apache.spark.serializer.JavaDeserializationStream错误，找了很多资料，有各种各样的解决办法...于是终于费劲地找到原因如下: 报错的意思应该是没有将jar包提交到spark的worker上面 导致运行的worker找不到被调用的类，才会报上述错误，因此设置个JAR，果然搞定。 ...val conf = new SparkConf().setMaster("spark://ubuntu-bigdata-5:7077").setAppName("Spark Pi") .setJars
org.apache.spark.sql.sources.v2.reader....org.apache.spark.sql.catalyst.util.DateTimeUtilsimport org.apache.spark.sql.sources.v2.reader.InputPartitionReaderimport...org.apache.spark.sql.catalyst.InternalRowimport org.apache.spark.sql.sources.v2.writer....org.apache.spark.sql.catalyst.InternalRowimport org.apache.spark.sql.sources.v2.writer....org.apache.spark.sql.sources.v2.writer.
恭喜老铁，跟我遇到了一样的问题，接下来是解决方法： 遇到的问题： org.apache.spark.sql.AnalysisException: Table or view not found: `traintext...:67) at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:128) at org.apache.spark.sql.catalyst.trees.TreeNode...:67) at org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:57) at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed...(QueryExecution.scala:48) at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:63) at org.apache.spark.sql.SparkSession.sql...+table val data=spark.sql(sql) data.show(); } } 2.看看自己的项目中是否配置hive-site.xml（重点，我自己就是这个错误
所需修改的项目位置：apache-atlas-sources-2.1.0\addons\hive-bridge ①.org/apache/atlas/hive/bridge/HiveMetaStoreBridge.java...metastoreEvent.getIHMSHandler() : null; 改为：C:\Users\Heaton\Desktop\apache-atlas-2.1.0-sources\apache-atlas-sources...数据血缘 打包spark-atlas-connector atlas 官方文档中并不支持 spark sql 的解析，需要使用第三方的包。...-${version}.jar 放到一个固定目录 比如/opt/resource 测试spark hook 首先进入spark-sql client spark-sql --master yarn \...REST API http://atlas.apache.org/api/v2/index.html DiscoveryREST http://hostname:21000/api/atlas/v2/search
所需修改的项目位置：apache-atlas-sources-2.1.0\addons\hive-bridge ①.org/apache/atlas/hive/bridge/HiveMetaStoreBridge.java...metastoreEvent.getIHMSHandler() : null; 改为：C:\Users\Heaton\Desktop\apache-atlas-2.1.0-sources\apache-atlas-sources...数据血缘 打包spark-atlas-connector atlas 官方文档中并不支持 spark sql 的解析，需要使用第三方的包。...-${version}.jar 放到一个固定目录 比如/opt/resource 测试spark hook 首先进入spark-sql client spark-sql --master yarn...REST API http://atlas.apache.org/api/v2/index.html DiscoveryREST http://hostname:21000/api/atlas/v2/search
>> 问题1 使用SparkSQL（2.4版本）往存储格式为parquet的Hive分区表中存储NullType类型的数据时报错： org.apache.spark.sql.AnalysisException...既然是保存数据，我们很容易联想到FileFormatWriter，再结合错误信息： org.apache.spark.sql.execution.datasources.FileFormatWriter...2 id, map("k1","v1","k2","v2") map 2）报错信息 org.apache.spark.sql.AnalysisException: Cannot have map type...:85) at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127) at org.apache.spark.sql.catalyst.analysis.CheckAnalysis...问题分析 根据报错信息，我们查看org.apache.spark.sql.catalyst.analysis.CheckAnalysis的checkAnalysis方法，第362行源码处理逻辑（错误信息是不是很熟悉呢
/ slaves 五 ：编写脚本 一 ：基础环境配置 本部分具体步骤可以参考Hadoop集群搭建中的前半部分关于Linux环境搭建以及系统环境配置 二 ：安装包下载 下载链接 ：http://spark.apache.org.../sbin/start-all.sh 有一处错误提示 hadoop01 JAVA_HOME is not set 进入hadoop01节点,在spark-env.sh 出添加JAVA_HOME=/home...: org/apache/hadoop/fs/ FSDataInputStream 解决方式 ： 1 ：将master的防火墙关闭 2 ：检查slave节点的spark文件是否与master节点的文件一致...error 异常提示提炼出来如下几句： java.lang.IllegalArgumentException: Error while instantiating ‘org.apache.spark.sql.hive.HiveSessionState...connection exception: java.net.ConnectException: Connection refused; For more details see: http://wiki.apache.org
a1.sinks.k1.type = org.apache.spark.streaming.flume.sink.SparkSink #a1.sources.k1.channels = c1 a1.sinks.k1...---Spark代码 import org.apache.spark.sql.SparkSession import org.apache.spark.storage.StorageLevel import...org.apache.spark.streaming.flume.FlumeUtils import org.apache.spark.streaming. object FlumeSparkStreaming...import org.apache.spark.sql.SparkSession import org.apache.spark.streaming. import org.apache.spark.streaming.kafka010...._ import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent import org.apache.spark.streaming.kafka010
，在此基础上我们只需要在插件中处理这些数据抽象即可，同时借助于Flink和Spark提供的SQL接口，还可以将每一次处理完的数据注册成表，方便用SQL进行处理，减少代码的开发量。...或者org.apache.seatunnel.core.flink.FlinkStarter，实际上这个类只做一个工作：将所有参数拼接成spark-submit或者flink命令，而后脚本接收到spark-submit...或者flink命令并提交到集群中；提交到集群中真正执行job的类实际上是org.apache.seatunnel.spark.SeatunnelSpark或是org.apache.seatunnel.flink.SeatunnelFlink...开始，用户根据将配置文件从脚本传入，脚本调用org.apache.seatunnel.core.spark.SparkStarter或者org.apache.seatunnel.core.flink.FlinkStarter...或是org.apache.seatunnel.flink.SeatunnelFlink，读者如果想直接深入了解作业启动核心流程的话推荐阅读这两个类的源码，连接器V2和连接器V1的启动流程基本一致。
spark task, with exception 'org.apache.hadoop.hive.ql.metadata.HiveException(Failed to create spark...FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.spark.SparkTask Spark与hive...hive与spark版本必须对应着 重新编译完报 Exception in thread "main" java.lang.NoClassDefFoundError: org/slf4j/impl/...，但是slaves仍然是上面错误 用scala....运行时的日志，查看加载jar包的地方,添加上述jar 5.异常 java.lang.RuntimeException: org.apache.hadoop.ipc.RemoteException(
java.lang.NoClassDefFoundError: org/apache/spark/sql/SparkSession$ 在【pom】中有【scope】的这个子节点，把这个子节点的限制去掉就行...目录 java.lang.NoClassDefFoundError: org/apache/spark/sql/SparkSession$ scope provided的作用 Demo问题： springboot
import org.apache.spark.streaming....import org.apache.spark.streaming....import org.apache.spark.SparkConf import org.apache.spark.streaming.kafka.KafkaUtils import org.apache.spark.streaming...import org.apache.spark.streaming.....jar \ hadoop000:9092 streamingtopic 报错： java.lang.NoClassDefFoundError: org/apache/hadoop/hbase/client
import org.apache.spark.sql.sources.v2....import org.apache.spark.sql.sources.v2.reader....import org.apache.spark.sql.sources.v2.reader...., schema: Array[org.apache.spark.sql.types.DataType]): org.apache.spark.sql.catalyst.expressions.UnsafeRow...]): org.apache.spark.sql.Row => org.apache.spark.sql.catalyst.expressions.UnsafeRow = { val converter
{Row, SparkSession} import org.apache.spark.sql.expressions.MutableAggregationBuffer import org.apache.spark.sql.expressions.UserDefinedAggregateFunction...Aggregator 定义一个Aggregator import org.apache.spark.sql....import org.apache.spark.sql.sources.v2....import org.apache.spark.sql.sources.v2.reader....import org.apache.spark.sql.sources.v2.reader.
如：org/apache/hadoop/hbase/CompatibilityFactory没有找到类， E xception in thread "main" java.lang.NoClassDefFoundError...: org/apache/hadoop/hbase/client/Scan 如此等等。...正常需要引用的包如下： org.apache.spark... org.apache.hbase...version> 除此之外，会需要hbase-hadoop-compact.jar等几个库，把这些库加载后，就不会出现这样的错误
spark task, with exception 'org.apache.hadoop.hive.ql.metadata.HiveException(Failed to create spark client...FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.spark.SparkTask Spark与hive...hive与spark版本必须对应着 重新编译完报 Exception in thread "main" java.lang.NoClassDefFoundError: org/slf4j/impl/StaticLoggerBinder...在spark-env.sh里面添加 export SPARK_DIST_CLASSPATH=$(hadoop classpath) spark master可以起来了，但是slaves仍然是上面错误...运行时的日志，查看加载jar包的地方,添加上述jar 5.异常 java.lang.RuntimeException: org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.security.authorize.AuthorizationException
现象 在spark-shell中执行streaming application时，频繁出现以下错误。...$KafkaRDDIterator.getNext(KafkaRDD.scala:164) at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala...(ExternalSorter.scala:202) at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala...:56) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68) at org.apache.spark.scheduler.ShuffleMapTask.runTask...(ShuffleMapTask.scala:41) at org.apache.spark.scheduler.Task.run(Task.scala:64) at org.apache.spark.executor.Executor
要求Spark版本2.3以上，亲测2.2无效 配置 config("spark.sql.sources.partitionOverwriteMode","dynamic") 注意 1、saveAsTable...方法无效，会全表覆盖写，需要用insertInto，详情见代码 2、insertInto需要主要DataFrame列的顺序要和Hive表里的顺序一致，不然会数据错误！...package com.dkl.blog.spark.hive import org.apache.spark.sql.SparkSession /** Created by dongkelun on...") .master("local") .config("spark.sql.parquet.writeLegacyFormat", true) .config("spark.sql.sources.partitionOverwriteMode...","dynamic") .enableHiveSupport() .getOrCreate() import spark.sql val data = Array(("001", "张三",
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