今天新开发的Structured streaming部署到集群时,总是报这个错:

SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/data4/yarn/nm/filecache/25187/slf4j-log4j12-1.7.16.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/opt/cloudera/parcels/CDH-5.7.2-1.cdh5.7.2.p0.18/jars/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
Exception in thread "stream execution thread for [id = 0ab981e9-e3f4-42ae-b0d7-db32b249477a, runId = daa27209-8817-4dee-b534-c415d10d418a]" java.lang.AbstractMethodError
at org.apache.spark.internal.Logging$class.initializeLogIfNecessary(Logging.scala:99)
at org.apache.spark.sql.kafka010.KafkaSourceProvider$.initializeLogIfNecessary(KafkaSourceProvider.scala:369)
at org.apache.spark.internal.Logging$class.log(Logging.scala:46)
at org.apache.spark.sql.kafka010.KafkaSourceProvider$.log(KafkaSourceProvider.scala:369)
at org.apache.spark.internal.Logging$class.logDebug(Logging.scala:58)
at org.apache.spark.sql.kafka010.KafkaSourceProvider$.logDebug(KafkaSourceProvider.scala:369)
at org.apache.spark.sql.kafka010.KafkaSourceProvider$ConfigUpdater.set(KafkaSourceProvider.scala:439)
at org.apache.spark.sql.kafka010.KafkaSourceProvider$.kafkaParamsForDriver(KafkaSourceProvider.scala:394)
at org.apache.spark.sql.kafka010.KafkaSourceProvider.createSource(KafkaSourceProvider.scala:90)
at org.apache.spark.sql.execution.datasources.DataSource.createSource(DataSource.scala:277)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$1$$anonfun$applyOrElse$1.apply(MicroBatchExecution.scala:80)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$1$$anonfun$applyOrElse$1.apply(MicroBatchExecution.scala:77)
at scala.collection.mutable.MapLike$class.getOrElseUpdate(MapLike.scala:194)
at scala.collection.mutable.AbstractMap.getOrElseUpdate(Map.scala:80)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$1.applyOrElse(MicroBatchExecution.scala:77)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$1.applyOrElse(MicroBatchExecution.scala:75)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.logicalPlan$lzycompute(MicroBatchExecution.scala:75)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.logicalPlan(MicroBatchExecution.scala:61)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:265)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189)

百度了一下说是版本不一致导致的。于是重新检查各个jar包,发现spark-sql-kafka的版本是2.2,而spark的版本是2.3,修改spark-sql-kafka的版本后,顺利执行。

Spark踩坑——java.lang.AbstractMethodError的更多相关文章

  1. spark 运行报错:java.lang.AbstractMethodError

    报错日志如下: Caused by: java.lang.AbstractMethodError: sparkCore.JavaWordCount$2.call(Ljava/lang/Object;) ...

  2. Spark踩坑记——Spark Streaming+Kafka

    [TOC] 前言 在WeTest舆情项目中,需要对每天千万级的游戏评论信息进行词频统计,在生产者一端,我们将数据按照每天的拉取时间存入了Kafka当中,而在消费者一端,我们利用了spark strea ...

  3. Spark踩坑记——数据库(Hbase+Mysql)

    [TOC] 前言 在使用Spark Streaming的过程中对于计算产生结果的进行持久化时,我们往往需要操作数据库,去统计或者改变一些值.最近一个实时消费者处理任务,在使用spark streami ...

  4. Spark踩坑记——共享变量

    [TOC] 前言 Spark踩坑记--初试 Spark踩坑记--数据库(Hbase+Mysql) Spark踩坑记--Spark Streaming+kafka应用及调优 在前面总结的几篇spark踩 ...

  5. Spark踩坑记——从RDD看集群调度

    [TOC] 前言 在Spark的使用中,性能的调优配置过程中,查阅了很多资料,之前自己总结过两篇小博文Spark踩坑记--初试和Spark踩坑记--数据库(Hbase+Mysql),第一篇概况的归纳了 ...

  6. [转]Spark 踩坑记:数据库(Hbase+Mysql)

    https://cloud.tencent.com/developer/article/1004820 Spark 踩坑记:数据库(Hbase+Mysql) 前言 在使用Spark Streaming ...

  7. Spark踩坑记:共享变量

    收录待用,修改转载已取得腾讯云授权 前言 前面总结的几篇spark踩坑博文中,我总结了自己在使用spark过程当中踩过的一些坑和经验.我们知道Spark是多机器集群部署的,分为Driver/Maste ...

  8. Spark踩坑记——数据库(Hbase+Mysql)转

    转自:http://www.cnblogs.com/xlturing/p/spark.html 前言 在使用Spark Streaming的过程中对于计算产生结果的进行持久化时,我们往往需要操作数据库 ...

  9. Spark踩坑记:Spark Streaming+kafka应用及调优

    前言 在WeTest舆情项目中,需要对每天千万级的游戏评论信息进行词频统计,在生产者一端,我们将数据按照每天的拉取时间存入了Kafka当中,而在消费者一端,我们利用了spark streaming从k ...

随机推荐

  1. C# 使用 HttpPost 请求调用 WebService

    之前调用 WebService 都是直接添加服务引用,然后调用 WebService 方法的,最近发现还可以使用 Http 请求调用 WebService.这里还想说一句,还是 web api 的调用 ...

  2. DOM-DOMTree-查找

    1. 什么是DOM: Document Object Model 专门操作网页内容的API js=ES+DOM+BOM DOM是由W3C指定的API标准 为什么: 为了统一各个浏览器操作网页内容的AP ...

  3. kbmmw 5.06.20 发布

    经过1个多月的测试,终于发布正式版. 5.06.20 July 11 2018           Important notes (changes that may break existing c ...

  4. 树莓派无法挂载exfat格式硬盘

    ubutnu系统 挂载硬盘时报错: mount: unknown filesystem type 'exfat' 这是因为树莓派默认无法识别 exfat, 需要安装 exfat-fuse . sudo ...

  5. 25、UIView的setNeedsLayout, layoutIfNeeded 和 layoutSubviews 方法之间的关系解释

    layoutSubviews总结 ios layout机制相关方法 - (CGSize)sizeThatFits:(CGSize)size- (void)sizeToFit——————- - (voi ...

  6. 2018.12.12 codeforces 931E. Game with String(概率dp)

    传送门 感觉这题难点在读懂题. 题目简述:给你一个字符串s,设将其向左平移k个单位之后的字符串为t,现在告诉你t的第一个字符,然后你可以另外得知t的任意一个字符,求用最优策略猜对k的概率. 解析: 预 ...

  7. 牛客训练三:处女座的比赛(hash打表)

    题目链接:传送门 思路:由于MOD只有9983大小,所以四位小写字母的字符串组合有26+26^2+26^3+26^4=475254种组合. 所以只要每次枚举出从1到475254中的hash值对应的字符 ...

  8. GDI基础(3):绘制图片

    1.CBitmap位图类封装了Windows GDI中的位图和操作位图的成员函数.CPen.CBrush.CFont.CBitmap是常用的Windows GDI对象,和CFont一样,CBitmap ...

  9. pat树之专题(30分)

    (好好复习是王道) 1115. Counting Nodes in a BST (30) 分析:简单题——将bst树构造出来,然后给每个节点打上高度.最后求出树的高度.然后count树高的节点数加上树 ...

  10. Java理论学时第六节。课后作业。

    package Fuction; class Grandparent { public Grandparent() { System.out.println("GrandParent Cre ...