Spark踩坑——java.lang.AbstractMethodError
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
- 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;) ...
- 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 ...
- 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 ...
- 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 ...
- 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 ...
- [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 ...
- 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 ...
- 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 ...
- 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
- Java中的几种对象(POJO,PO,DTO,DAO,BO)
j2ee中,经常提到几种对象(object),理解他们的含义有助于我们更好的理解面向对象的设计思维. POJO(plain old java object):普通的java对象,有别于特殊的j ...
- mysql练习题3
USE day44; -- 1 查出所有员工的名字,薪资,格式为 -- <名字:egon> <薪资:3000> SELECT '姓名:',name,'薪资:',salary f ...
- php使用include报错require_once(../include.php): failed to open stream: No such file or directo
引入路径的问题,建议加入include_once $_SERVER['DOCUMENT_ROOT']."/include.php";意思是获取网站根目中的include.php 截 ...
- Web 开发
Django(发音:[`dʒæŋɡəʊ]) 是一个开放源代码的Web应用框架,由Python写成.采用了MTV的框架模式,模型(Model).模板(Template)和视图(Views).
- 2019.01.22 bzoj3333: 排队计划（逆序对+线段树）
传送门 题意简述:给出一个序列,支持把ppp~nnn中所有小于等于apa_pap的'扯出来排序之后再放回去,要求动态维护全局逆序对. 思路:我们令fif_ifi表示第iii个位置之后比它大的数的个 ...
- ajax from 提交
$.ajax({ cache: true, type: "POST", url:aj ...
- dj forms表单组件
手动的一个个去校验前端传过来的字段数据,是很麻烦的,利用django 的forms组件,对需要校验的字段定义好,能够大大提高效率. 校验字段功能 from django.db import model ...
- Arria10中的IOPLL与fPLL
最近在用Arria10.从480降到270的过程中,IOPLL出现问题,大概是说几个Bank的IOPLL已经被占用,没有空间再给别的IOPLL去适配. 因为在工程中,所用的PLL多达35个之多,其中明 ...
- SQL编写-谁和谁是好朋友
problem: 用户表 姓名 张三 李四 王五 好友表 姓名 好友姓名 张三 李四 张三 王五 李四 张三 用户表里面姓名必须在好友表里面互加为好友,请问如何找出还没有加为好友的姓名 ...
- oss上传大文件
最近公司做工程项目,实现文件云存储上传. 网上找了一天,发现网上很多代码都存在相似问题,最后终于找到了一个满足我需求的项目. 工程如下: 这里对项目的文件传输功能做出分析,怎么实现文件上传的,如何进行 ...