yarn

@vue/cli 4.1.1安装

女生的网名这么多〃 提交于 2019-12-13 16:13:24
按照安装步骤,先卸载,再安装,最终,查看vue -V 的版本都是3.8.2,也就是说并没有安装成功,于是,考虑用yarn去安装 1,首先清除缓存; yarn cache clean 2,yarn设置淘宝镜像 yarn config set registry https://registry.npm.taobao.org -g 3,安装 yarn global add @vue/cli 4,查看版本 安装成功了。 /*--> */ /*--> */ 来源: https://www.cnblogs.com/wang715100018066/p/12035657.html

How Container failure is handled for a YARN MapReduce job?

穿精又带淫゛_ 提交于 2019-12-13 13:50:24
问题 How are software/hardware failures handled in YARN? Specifically, what happens in case of container(s) failure/crash? 回答1: Container and task failures are handled by node-manager. When a container fails or dies, node-manager detects the failure event and launches a new container to replace the failing container and restart the task execution in the new container. In the event of application-master failure, the resource-manager detects the failure and start a new instance of the application

分布式资源调度——YARN框架

柔情痞子 提交于 2019-12-13 13:16:19
YARN产生背景 YARN是Hadoop2.x才有的,所以在介绍YARN之前,我们先看一下MapReduce1.x时所存在的问题: 单点故障 节点压力大 不易扩展 MapReduce1.x时的架构如下: 可以看到,1.x时也是Master/Slave这种主从结构,在集群上的表现就是一个JobTracker带多个TaskTracker。 JobTracker:负责资源管理和作业调度 TaskTracker:定期向JobTracker汇报本节点的健康状况、资源使用情况以及作业执行情况。还可以接收来自JobTracker的命令,例如启动任务或结束任务等。 那么这种架构存在哪些问题呢: 整个集群中只有一个JobTracker,就代表着会存在单点故障的情况 JobTracker节点的压力很大,不仅要接收来自客户端的请求,还要接收大量TaskTracker节点的请求 由于JobTracker是单节点,所以容易成为集群中的瓶颈,而且也不易域扩展 JobTracker承载的职责过多,基本整个集群中的事情都是JobTracker来管理 1.x版本的整个集群只支持MapReduce作业,其他例如Spark的作业就不支持了 由于1.x版本不支持其他框架的作业,所以导致我们需要根据不同的框架去搭建多个集群。这样就会导致资源利用率比较低以及运维成本过高,因为多个集群会导致服务环境比较复杂。如下图:

react中执行yarn eject配置antd-mobile的按需加载

人盡茶涼 提交于 2019-12-13 13:02:56
在使用react做项目时如果用antd-mobile,如果使用按需加载,则需要修改它的配置文件 如果我们不操作yarn eject,则直接操作下面的步骤即可: 在 create-react-app 搭建脚手架时 cnpm install -g create-react-app create-react-app reactDemo cd reactDemo cnpm start 引入 antd-mobile 因为配置文件隐藏了,从而我们需要引入 react-app-rewired 并修改 package.json 里的启动配置 cnpm install react-app-rewired --save-dev cnpm install babel-plugin-import --save-dev 或者 yarn add react-app-rewired --dev yarn add babel-plugin-import --dev /* package.json 的配置需要做如下修改*/ "scripts": { - "start": "react-scripts start", + "start": "react-app-rewired start", - "build": "react-scripts build", + "build": "react-app-rewired

Apache Hue 集成Yarn

巧了我就是萌 提交于 2019-12-13 11:50:36
修改hue.ini [ [ yarn_clusters ] ] [ [ [ default ] ] ] resourcemanager_host = node - 1 resourcemanager_port = 8032 submit_to = True resourcemanager_api_url = http : / / node - 1 : 8088 history_server_api_url = http : / / node - 1 : 19888 开启yarn日志聚集服务 yarn - site . xml MapReduce 是在各个机器上运行的, 在运行过程中产生的日志存在于各个机器上,为了能够统一查看各个机器的运行日志,将日志集中存放在 HDFS 上, 这个过程就是日志聚集。 < property > ##是否启用日志聚集功能。 < name > yarn.log-aggregation-enable </ name > < value > true </ value > </ property > < property > ##设置日志保留时间,单位是秒。 < name > yarn.log-aggregation.retain-seconds </ name > < value > 106800 </ value > </ property >

Yarn Resource Manage didn't allocate containers when asking for containers with different resources

大憨熊 提交于 2019-12-13 07:14:45
问题 I used synchronous AMRMClient in application master, using addContainerRequest method of AMRMClient to add container requests, using getMatchingRequests and removeContainerRequest methods of AMRMClient to remove container requests. However, when program add container requests with different resources, Resource Manager no longer allocated any resource to application master and it lead to deadlock. Have somebody once faced such problem? 回答1: Container request at the same priority should have

Yarn container lauch failed exception and mapred-site.xml configuration

早过忘川 提交于 2019-12-13 06:51:37
问题 I have 7 nodes in my Hadoop cluster [8GB RAM and 4VCPUs to each nodes], 1 Namenode + 6 datanodes. EDIT-1@ARNON: I followed the link, mad calculation according to the hardware configruation on my nodes and have added the update mapred-site and yarn-site.xml files in my question. Still my application is crashing with the same exection My mapreduce application has 34 input splits with a block size of 128MB. mapred-site.xml has the following properties: mapreduce.framework.name = yarn mapred

How can I submit a Cascading job to a remote YARN cluster from Java?

谁都会走 提交于 2019-12-13 05:49:49
问题 I know that I can submit a Cascading job by packaging it into a JAR, as detailed in the Cascading user guide. That job will then run on my cluster if I manually submit it using hadoop jar CLI command. However, in the original Hadoop 1 Cascading version, it was possible to submit a job to the cluster by setting certain properties on the Hadoop JobConf . Setting fs.defaultFS and mapred.job.tracker caused the local Hadoop library to automatically attempt to submit the job to the Hadoop1

install knox error. IndexError: list index out of range

一曲冷凌霜 提交于 2019-12-13 03:07:49
问题 After upgrading from HDP 2.7 to HDP 3.1, I manually uninstalled many services, such as spark2,hive,hbase and Knox, for some reason. When I tried to install Knox, the installation failed. env: ambari 2.7 HDP 3.1 enabled kerberos used openldap stderr: /var/lib/ambari-agent/data/errors-15457.txt Traceback (most recent call last): File "/var/lib/ambari-agent/cache/stacks/HDP/3.0/services/KNOX/package/scripts/knox_gateway.py", line 215, in <module> KnoxGateway().execute() File "/usr/lib/ambari

Standalone Spark application in IntelliJ

左心房为你撑大大i 提交于 2019-12-13 03:00:43
问题 I am trying to run a spark application (written in Scala) on a local server for debug. It seems that YARN is the default in the version of spark (2.2.1) that I have in the sbt build definitions, and according to an error I'm consistently getting, there is no spark/YARN server listening: Client:920 - Failed to connect to server: 0.0.0.0/0.0.0.0:8032: retries get failed due to exceeded maximum allowed retries number According to netstat indeed there is really no port 8032 on my local server, in