yarn

Can a PySpark Kernel(JupyterHub) run in yarn-client mode?

由 大兔子大兔子 提交于 2021-02-08 10:34:00
问题 My Current Setup: Spark EC2 Cluster with HDFS and YARN JuputerHub(0.7.0) PySpark Kernel with python27 The very simple code that I am using for this question: rdd = sc.parallelize([1, 2]) rdd.collect() The PySpark kernel that works as expected in Spark standalone has the following environment variable in the kernel json file: "PYSPARK_SUBMIT_ARGS": "--master spark://<spark_master>:7077 pyspark-shell" However, when I try to run in yarn-client mode it is getting stuck forever, while the log

Can a PySpark Kernel(JupyterHub) run in yarn-client mode?

由 佐手、 提交于 2021-02-08 10:31:57
问题 My Current Setup: Spark EC2 Cluster with HDFS and YARN JuputerHub(0.7.0) PySpark Kernel with python27 The very simple code that I am using for this question: rdd = sc.parallelize([1, 2]) rdd.collect() The PySpark kernel that works as expected in Spark standalone has the following environment variable in the kernel json file: "PYSPARK_SUBMIT_ARGS": "--master spark://<spark_master>:7077 pyspark-shell" However, when I try to run in yarn-client mode it is getting stuck forever, while the log

“Application priority” in Yarn

由 自闭症网瘾萝莉.ら 提交于 2021-02-07 19:17:57
问题 I am using Hadoop 2.9.0. Is it possible to submit jobs with different priorities in YARN? According to some JIRA tickets it seems that application priorities have now been implemented. I tried using the YarnClient , and setting a priority to the ApplicationSubmissionContext before submitting the job. I also tried using the CLI and using updateApplicationPriority . However, nothing seems to be changing the application priority, it always remains 0. Have I misunderstood the concept of

Why does Spark (on Google Dataproc) not use all vcores?

由 杀马特。学长 韩版系。学妹 提交于 2021-02-07 12:31:55
问题 I'm running a spark job on a Google DataProc cluster. But looks like Spark is not using all the vcores available in the cluster as you can see below Based on some other questions like this and this, i have setup the cluster to use DominantResourceCalculator to consider both vcpus and memory for resource allocation gcloud dataproc clusters create cluster_name --bucket="profiling- job-default" \ --zone=europe-west1-c \ --master-boot-disk-size=500GB \ --worker-boot-disk-size=500GB \ --master

auxService:mapreduce_shuffle does not exist on hive

由 百般思念 提交于 2021-02-07 03:06:27
问题 I am using hive 1.2.0 and hadoop 2.6.0. whenever I am running hive on my machine... select query works fine but in case of count(*) it shows following error: Diagnostic Messages for this Task: Container launch failed for container_1434646588807_0001_01_000005 : org.apache.hadoop.yarn.exceptions.InvalidAuxServiceException: The auxService:mapreduce_shuffle does not exist at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method) at sun.reflect.NativeConstructorAccessorImpl

YARN not preempting resources based on fair shares when running a Spark job

由 只愿长相守 提交于 2021-02-06 09:50:10
问题 I have a problem with re-balancing Apache Spark jobs resources on YARN Fair Scheduled queues. For the tests I've configured Hadoop 2.6 (tried 2.7 also) to run in pseudo-distributed mode with local HDFS on MacOS. For job submission used "Pre-build Spark 1.4 for Hadoop 2.6 and later" (tried 1.5 also) distribution from Spark's website. When tested with basic configuration on Hadoop MapReduce jobs, Fair Scheduler works as expected: When resources of the cluster exceed some maximum, fair shares

Spark Driver memory and Application Master memory

由 倾然丶 夕夏残阳落幕 提交于 2021-02-05 20:26:50
问题 Am I understanding the documentation for client mode correctly? client mode is opposed to cluster mode where the driver runs within the application master? In client mode the driver and application master are separate processes and therefore spark.driver.memory + spark.yarn.am.memory must be less than the machine's memory? In client mode is the driver memory is not included in the application master memory setting? 回答1: client mode is opposed to cluster mode where the driver runs within the

多租户技术

由 China☆狼群 提交于 2021-02-04 11:57:17
1 多租户概念 多租户技术( Multi Tenancy Technology )又称多重租赁技术，用于实现如何在多用户的环境下共用相同的系统或程序组件，并且仍可确保各用户间数据的隔离性。 具体的多租户技术有多种，数据库通常有以下三种： 1.1 独立数据库 这是第一种方案，即一个租户一个数据库。这种方案的用户数据隔离级别最高、安全性最好，但成本也高。 优点:为不同的租户提供独立的数据库，有助于简化数据模型的扩展设计，满足不同租户的独特需求;如果出现故障，则恢复数据比较简单。 缺点:增大了数据库的安装数量，随之带来维护成本和购置成本的增加。这种方案与传统的一个客户、一套数据、一套部署类似，差别只在于软件统一部署在运营商那里。如果面对的是银行、医院等要求数据隔离级别非常高的租户，则可以选择这种模式，提高租用的定价。如果定价较低，产品走低价路线，那么这种方案对运营商来说是无法承受的。 1.2 共享数据库，隔离数据架构 这是第二种方案，即多个或所有租户共享Database,但一个Tenant一个Schema。 优点:为安全性要求较高的租户提供了一定程度的逻辑数据隔离，但并不是完全隔离；每个数据库可以支持更多的租户数量。 缺点；如果出现故障，则数据恢复比较困难，因为恢复数据库将涉及其他租户的数据；如果需要跨租户统计数据，则存在一定的困难。 1.3 共享数据库，共享数据架构 这是第三种方案

hadoop组件启动和关闭命令

由 岁酱吖の 提交于 2021-02-04 07:49:53
一、启动相关组件之前 一般安装完hadoop之后需要格式化一遍hdfs： hdfs namenode -format 然后再进行其他组件的启动，hadoop相关组件都是用位于...hadoop/sbin目录下的脚本启动的， 二、启动组件 一般启动相关的就可以了： # 开启hdfs start -dfs. sh # 开启yarn start -yarn. sh 然后查看进程都开起来了没有，正常是： [root@harry etc]# jps 6531 NodeManager 6264 SecondaryNameNode 6077 DataNode 6670 Jps 5983 NameNode 三、关闭服务 stop-all. sh 来源： oschina 链接： https://my.oschina.net/u/4403012/blog/4000886

K8S搭建及部署Hadoop集群

由 ⅰ亾dé卋堺 提交于 2021-02-01 19:50:19
K8s服务器配置 服务器节点 cat >> /etc/hosts << EOF 192.168.207.133 k8s-master 192.168.207.134 k8s-node1 192.168.207.135 k8s-node2 EOF kubeadm 初始化 kubeadm init \ --apiserver-advertise-address=192.168.207.133 \ --image-repository registry.aliyuncs.com/google_containers \ --kubernetes-version v1.19.0 \ --service-cidr=10.96.0.0/12 \ --pod-network-cidr=10.244.0.0/16 \ --ignore-preflight-errors=all 搭建Hadoop集群 编写hadoop.yaml apiVersion: v1 kind: ConfigMap metadata: name: kube-hadoop-conf data: HDFS_MASTER_SERVICE: hadoop-hdfs-master HDOOP_YARN_MASTER: hadoop-yarn-master --- apiVersion: v1 kind: Service metadata:
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