Hadoop

Does Hive preserve file order when selecting data

由 不打扰是莪最后的温柔 提交于 2021-02-19 04:05:44
问题 If I do select * from table1; in which order data will retrieve File order Or random order 回答1: Without ORDER BY the order is not guaranteed. Data is being read in parallel by many processes (mappers), after splits were calculated, each process starts reading some piece of file or few files, depending on splits calculated. All parallel processes can process different volume of data and running on different nodes, the load is not the same each time, so they start returning rows and finishing

Hadoop MapReduce job I/O Exception due to premature EOF from inputStream

由 拈花ヽ惹草 提交于 2021-02-18 22:50:42
问题 I ran a MapReduce program using the command hadoop jar <jar> [mainClass] path/to/input path/to/output . However, my job was hanging at: INFO mapreduce.Job: map 100% reduce 29% . Much later, I terminated and checked the datanode log (I am running in pseudo-distributed mode). It contained the following exception: java.io.IOException: Premature EOF from inputStream at org.apache.hadoop.io.IOUtils.readFully(IOUtils.java:201) at org.apache.hadoop.hdfs.protocol.datatransfer.PacketReceiver

Hadoop MapReduce job I/O Exception due to premature EOF from inputStream

由 ☆樱花仙子☆ 提交于 2021-02-18 22:49:18
问题 I ran a MapReduce program using the command hadoop jar <jar> [mainClass] path/to/input path/to/output . However, my job was hanging at: INFO mapreduce.Job: map 100% reduce 29% . Much later, I terminated and checked the datanode log (I am running in pseudo-distributed mode). It contained the following exception: java.io.IOException: Premature EOF from inputStream at org.apache.hadoop.io.IOUtils.readFully(IOUtils.java:201) at org.apache.hadoop.hdfs.protocol.datatransfer.PacketReceiver

Python write to hdfs file

由 时光毁灭记忆、已成空白 提交于 2021-02-18 22:00:58
问题 What is the best way to create/write/update a file in remote HDFS from local python script? I am able to list files and directories but writing seems to be a problem. I have searched hdfs and snakebite but none of them give a clean way to do this. 回答1: try HDFS liberary.. its really good You can use write(). https://hdfscli.readthedocs.io/en/latest/api.html#hdfs.client.Client.write Example: to create connection: from hdfs import InsecureClient client = InsecureClient('http://host:port', user=

Reg : Efficiency among query optimizers in hive

由 做~自己de王妃 提交于 2021-02-18 18:13:30
问题 After reading about query optimization techniques I came to know about the below techniques. 1. Indexing - bitmap and BTree 2. Partitioning 3. Bucketing I got the difference between partitioning and bucketing, and when to use them but I'm still confused how indexes actually work. Where is the metadata for index is stored? Is it the namenode which is storing it? I.e., actually while creating partitions or buckets we can see multiple directories in hdfs which explains the query performance

Reg : Efficiency among query optimizers in hive

由 早过忘川 提交于 2021-02-18 18:13:25
问题 After reading about query optimization techniques I came to know about the below techniques. 1. Indexing - bitmap and BTree 2. Partitioning 3. Bucketing I got the difference between partitioning and bucketing, and when to use them but I'm still confused how indexes actually work. Where is the metadata for index is stored? Is it the namenode which is storing it? I.e., actually while creating partitions or buckets we can see multiple directories in hdfs which explains the query performance

Reg : Efficiency among query optimizers in hive

由 ♀尐吖头ヾ 提交于 2021-02-18 18:12:30
问题 After reading about query optimization techniques I came to know about the below techniques. 1. Indexing - bitmap and BTree 2. Partitioning 3. Bucketing I got the difference between partitioning and bucketing, and when to use them but I'm still confused how indexes actually work. Where is the metadata for index is stored? Is it the namenode which is storing it? I.e., actually while creating partitions or buckets we can see multiple directories in hdfs which explains the query performance

Reg : Efficiency among query optimizers in hive

由 大憨熊 提交于 2021-02-18 18:11:08
问题 After reading about query optimization techniques I came to know about the below techniques. 1. Indexing - bitmap and BTree 2. Partitioning 3. Bucketing I got the difference between partitioning and bucketing, and when to use them but I'm still confused how indexes actually work. Where is the metadata for index is stored? Is it the namenode which is storing it? I.e., actually while creating partitions or buckets we can see multiple directories in hdfs which explains the query performance

集群、分布式、微服务概念和区别

由 不想你离开。 提交于 2021-02-18 17:23:57
概念: 集群是个物理形态，分布式是个工作方式。 1.分布式：一个业务分拆多个子业务，部署在不同的服务器上 2.集群：同一个业务，部署在多个服务器上 分布式是指将不同的业务分布在不同的地方。而集群指的是将几台服务器集中在一起，实现同一业务。 分布式中的每一个节点，都可以做集群。而集群并不一定就是分布式的。 举例：就比如新浪网，访问的人多了，他可以做一个集群，前面放一个响应服务器，后面几台服务器完成同一业务，如果有业务访问的时候，响应服务器看哪台服务器的负载不是很重，就将给哪一台去完成。 而分布式，从窄意上理解，也跟集群差不多，但是它的组织比较松散，不像集群，有一个组织性，一台服务器垮了，其它的服务器可以顶上来。 分布式的每一个节点，都完成不同的业务，一个节点垮了，那这个业务就不可访问了。 简单说，分布式是以缩短单个任务的执行时间来提升效率的，而集群则是通过提高单位时间内执行的任务数来提升效率。 例如：如果一个任务由 10 个子任务组成，每个子任务单独执行需 1 小时，则在一台服务器上执行该任务需 10 小时。 采用分布式方案，提供 10 台服务器，每台服务器只负责处理一个子任务，不考虑子任务间的依赖关系，执行完这个任务只需一个小时。(这种工作模式的一个典型代表就是 Hadoop 的 Map/Reduce 分布式计算模型） 而采用集群方案，同样提供 10 台服务器

CDH 大数据平台搭建

由 我与影子孤独终老i 提交于 2021-02-18 12:31:12
一、概述 Cloudera版本（Cloudera’s Distribution Including Apache Hadoop，简称“CDH”），基于Web的用户界面,支持大多数Hadoop组件，包括HDFS、MapReduce、Hive、Pig、 Hbase、Zookeeper、Sqoop,简化了大数据平台的安装、使用难度。 二、安装部署 | 序号 | IP地址 | 主机名 |系统版本| | -------- | -------- | -------- | | 1 | 172.20.2.222 | cm-server |centos7.3 | 2 | 172.20.2.203 | hadoop-1 |centos7.3 | 3 | 172.20.2.204 | hadoop-2 |centos7.3 | 4 | 172.20.2.205 | hadoop-3 |centos7.3 2.2.1 基础环境部署 a.修改主机名配置hosts systemctl stop firewalld hostnamectl set-hostname cm-server #更改个主机名 sed -i 's/SELINUX=enforcing/SELINUX=disable/g' /etc/selinux/config setenforce 0 cat >>/etc/hosts<<EOF
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