yarn

How to control how many executors to run in yarn-client mode?

由 依然范特西╮ 提交于 2019-12-10 10:58:57
问题 I have a Hadoop cluster of 5 nodes where Spark runs in yarn-client mode. I use --num-executors for the number of executors. The maximum number of executors I am able to get is 20. Even if I specify more, I get only 20 executors. Is there any upper limit on the number of executors that can get allocated ? Is it a configuration or the decision is made on the basis of the resources available ? 回答1: Apparently your 20 running executors consume all available memory. You can try decreasing Executor

Run my own application master on a specific node in a YARN cluster

由 人走茶凉 提交于 2019-12-10 10:39:59
问题 First of all, I'm using Hadoop-2.6.0. I want to launch my own app master on a specific node in a YARN cluster in order to open a server on a predetermined IP address and port. To that end, I wrote a driver program in which I created a ResourceRequest object and called setResourceName method to set a hostname, and attached it to a ApplicationSubmissionContext object by calling setAMContainerResourceRequest method. I tried several times but couldn't launch the app master on a specific node.

Akeneo installation / NODE_PATH=node_modules not recognized / yarn run webpack Error

由 南楼画角 提交于 2019-12-10 10:38:47
问题 I have already asked this question on git (https://github.com/akeneo/pim-community-dev/issues/7191) but unfortunately nobody has answered me yet, thought I would try SO. I follow-up the akeneo-install-instruction (pim-community-standard-v2.0) https://docs.akeneo.com/latest/install_pim/manual/installation_ce_archive.html#initializing-akeneo yarn run webpack https://github.com/akeneo/pim-community-dev/blob/2.0/webpack.config.js Gives me an error: $ yarn run sync && NODE_PATH=node_modules

Disk Spill during MapReduce

由 女生的网名这么多〃 提交于 2019-12-10 10:29:36
问题 I have a pretty basic question that I am trying to find an answer for. I was looking through the documentation to understand where is the data spilled to during the map phase, shuffle phase and reduce phase? As in if Mapper A has 16 GB of RAM, but if the allocated memory for a mapper has exceeded then the data is spilled. Is the data spilled to HDFS or will the data be spilled to a tmp folder on the disk? During the shuffle phase, is the data streamed from one node to another node and is

Spark Failure : Caused by: org.apache.spark.shuffle.FetchFailedException: Too large frame: 5454002341

由 僤鯓⒐⒋嵵緔 提交于 2019-12-10 10:27:07
问题 I am generating a hierarchy for a table determining the parent child. Below is the configuration used, even after getting the error with regards to the too large frame: Spark properties --conf spark.yarn.executor.memoryOverhead=1024mb \ --conf yarn.nodemanager.resource.memory-mb=12288mb \ --driver-memory 32g \ --driver-cores 8 \ --executor-cores 32 \ --num-executors 8 \ --executor-memory 256g \ --conf spark.maxRemoteBlockSizeFetchToMem=15g import org.apache.log4j.{Level, Logger}; import org

HADOOP YARN - Application is added to the scheduler and is not yet activated. Skipping AM assignment as cluster resource is empty

由 丶灬走出姿态 提交于 2019-12-10 10:12:42
问题 I am evaluating YARN for a project. I am trying to get the simple distributed shell example to work. I have gotten the application to the SUBMITTED phase, but it never starts. This is the information reported from this line: ApplicationReport report = yarnClient.getApplicationReport(appId); Application is added to the scheduler and is not yet activated. Skipping AM assignment as cluster resource is empty. Details : AM Partition = DEFAULT_PARTITION; AM Resource Request = memory:1024, vCores:1;

安装vue-cli（笔记）

由 谁说我不能喝 提交于 2019-12-10 08:36:44
目录 一、安装vue-cli 2.x 1.安装2.x最新版本 2.指定2.x版本 二、安装vue-cli 4.x 1.安装4.x最新版本 2.安装3.x指定版本 三、卸载vue-cli 一、安装vue-cli 2.x 1.安装2.x最新版本 npm install -g vue-cli #OR yarn global add vue-cli 2.指定2.x版本 npm install -g vue-cli@2.x.x #OR yarn global add vue-cli@2.x.x 二、安装vue-cli 4.x 1.安装4.x最新版本 npm install -g @vue/cli #OR yarn global add @vue/cli 2.安装3.x指定版本 npm install -g @vue/cli@3.x.x #OR yarn global add @vue/cli@3.x.x 三、卸载vue-cli npm uninstall -g vue-cli #OR yarn global remove vue-cli Vue-CLI Link： Vue CLI 🛠️ Vue.js 开发的标准工具 来源： CSDN 作者： Road to be king 链接： https://blog.csdn.net/u011046452/article/details

spark on yarn run double times when error [duplicate]

由 你离开我真会死。 提交于 2019-12-10 06:57:56
问题 This question already has answers here : How to limit the number of retries on Spark job failure? (3 answers) Closed 2 years ago . I use the model that spark on yarn,when i meet a problem the spark would restart automatic. I want to run exact once whatever successful or fail. Is there any conf or api can set? I'm using spark version 1.5. 回答1: You have to set spark.yarn.maxAppAttempts property to 1. Default value for this is yarn.resourcemanager.am.max-attempts which is by default 2. Set the

yarn---npm的替代品

由 自作多情 提交于 2019-12-10 06:15:21
yarn的简介： Yarn是facebook发布的一款取代npm的包管理工具。 yarn的特点： 速度超快。 Yarn 缓存了每个下载过的包，所以再次使用时无需重复下载。 同时利用并行下载以最大化资源利用率，因此安装速度更快。 超级安全。 在执行代码之前，Yarn 会通过算法校验每个安装包的完整性。 超级可靠。 使用详细、简洁的锁文件格式和明确的安装算法，Yarn 能够保证在不同系统上无差异的工作。 yarn的安装: 下载node.js，使用npm安装 npm install -g yarn 查看版本：yarn --version 安装node.js,下载yarn的安装程序: 提供一个.msi文件，在运行时将引导您在Windows上安装Yarn Yarn 淘宝源安装，分别复制粘贴以下代码行到黑窗口运行即可 yarn config set registry https://registry.npm.taobao.org -g yarn config set sass_binary_site http://cdn.npm.taobao.org/dist/node-sass -g yarn的常用命令： //安装yarn npm install -g yarn 安装成功后，查看版本号： yarn --version 创建文件夹 yarn md yarn 进入yarn文件夹 cd yarn

SparkOnYarn-Container启动流程

由 陌路散爱 提交于 2019-12-10 02:43:54
Spark On Yarn程序的启动流程和之前的那篇《Yarn初步了解》文章中的“应用程序的”开发流程是一样的。先在某个NodeManager(NM)节点上启动Application Master(AM)，再由AM向ResourceManager(RM)申请资源创建Container，最后AM收到RM创建成功的消息后，由AM向RM发送请求启动这些Container，然后就可以在这些Container中运行任务了。 Spark - Yarn Client流程： 设置使用的资源分配算法，该算法会同时考虑CPU以及内存资源，让所有Application的“主要资源占比”资源尽可能的均等。 在之前的《SparkContext初始化流程》和《SparkOnYarn与StandAlone模式的区别》文章中曾经提起过Spark On Yarn Client模式下的启动流程。这里对与Yarn进行交互的地方再详细说明下： 核心在于SparkContex启动的时候初始化TaskScheduler以及YarnClientSchedulerBackend。前者用于task的调度，后者用于资源的申请，在Yarn模式下资源就是申请Container。流程图如下所示： 1. 提交AM到RM并启动AM 看下YarnClientSchedulerBackend类中的client.submitApplication(
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