Cloudera Certified Administrator for Apache Hadoop(CCAH认证)
Exam Sections and Blueprint
1. HDFS (17%)
- Describe the function of HDFS daemons
- Describe the normal operation of an Apache Hadoop cluster, both in data storage and in data processing
- Identify current features of computing systems that motivate a system like Apache Hadoop
- Classify major goals of HDFS Design
- Given a scenario, identify appropriate use case for HDFS Federation
- Identify components and daemon of an HDFS HA-Quorum cluster
- Analyze the role of HDFS security (Kerberos)
- Determine the best data serialization choice for a given scenario
- Describe file read and write paths
- Identify the commands to manipulate files in the Hadoop File System Shell
2. YARN and MapReduce version 2 (MRv2) (17%)
- Understand how upgrading a cluster from Hadoop 1 to Hadoop 2 affects cluster settings
- Understand how to deploy MapReduce v2 (MRv2 / YARN), including all YARN daemons
- Understand basic design strategy for MapReduce v2 (MRv2)
- Determine how YARN handles resource allocations
- Identify the workflow of MapReduce job running on YARN
- Determine which files you must change and how in order to migrate a cluster from MapReduce version 1 (MRv1) to MapReduce version 2 (MRv2) running on YARN
3. Hadoop Cluster Planning (16%)
- Principal points to consider in choosing the hardware and operating systems to host an Apache Hadoop cluster
- Analyze the choices in selecting an OS
- Understand kernel tuning and disk swapping
- Given a scenario and workload pattern, identify a hardware configuration appropriate to the scenario
- Given a scenario, determine the ecosystem components your cluster needs to run in order to fulfill the SLA
- Cluster sizing: given a scenario and frequency of execution, identify the specifics for the workload, including CPU, memory, storage, disk I/O
- Disk Sizing and Configuration, including JBOD versus RAID, SANs, virtualization, and disk sizing requirements in a cluster
- Network Topologies: understand network usage in Hadoop (for both HDFS and MapReduce) and propose or identify key network design components for a given scenario
4. Hadoop Cluster Installation and Administration (25%)
- Given a scenario, identify how the cluster will handle disk and machine failures
- Analyze a logging configuration and logging configuration file format
- Understand the basics of Hadoop metrics and cluster health monitoring
- Identify the function and purpose of available tools for cluster monitoring
- Be able to install all the ecoystme components in CDH 5, including (but not limited to): Impala, Flume, Oozie, Hue, Cloudera Manager, Sqoop, Hive, and Pig
- Identify the function and purpose of available tools for managing the Apache Hadoop file system
5. Resource Management (10%)
- Understand the overall design goals of each of Hadoop schedulers
- Given a scenario, determine how the FIFO Scheduler allocates cluster resources
- Given a scenario, determine how the Fair Scheduler allocates cluster resources under YARN
- Given a scenario, determine how the Capacity Scheduler allocates cluster resources
6. Monitoring and Logging (15%)
- Understand the functions and features of Hadoop’s metric collection abilities
- Analyze the NameNode and JobTracker Web UIs
- Understand how to monitor cluster daemons
- Identify and monitor CPU usage on master nodes
- Describe how to monitor swap and memory allocation on all nodes
- Identify how to view and manage Hadoop’s log files
- Interpret a log file
低调大师中文资讯倾力打造互联网数据资讯、行业资源、电子商务、移动互联网、网络营销平台。
持续更新报道IT业界、互联网、市场资讯、驱动更新,是最及时权威的产业资讯及硬件资讯报道平台。
转载内容版权归作者及来源网站所有,本站原创内容转载请注明来源。
- 上一篇
SparkContext的初始化(叔篇)——TaskScheduler的启动
《深入理解Spark:核心思想与源码分析》一书前言的内容请看链接《深入理解SPARK:核心思想与源码分析》一书正式出版上市 《深入理解Spark:核心思想与源码分析》一书第一章的内容请看链接《第1章 环境准备》 《深入理解Spark:核心思想与源码分析》一书第二章的内容请看链接《第2章 SPARK设计理念与基本架构》 由于本书的第3章内容较多,所以打算分别开辟四篇随笔分别展现。 《深入理解Spark:核心思想与源码分析》一书第三章第一部分的内容请看链接《深入理解Spark:核心思想与源码分析》——SparkContext的初始化(伯篇)》《深入理解Spark:核心思想与源码分析》一书第三章第一部分的内容请看链接《深入理解Spark:核心思想与源码分析》——SparkContext的初始化(仲篇)》 本文展现第3章第三部分的内容: 3.8 TaskScheduler的启动 3.7节介绍了任务调度器TaskScheduler的创建,要想TaskScheduler发挥作用,必须要启动它,代码如下。 taskScheduler.start() TaskScheduler在启动的时候,实际调用了...
- 下一篇
Hadoop 2.0 NameNode HA和Federation实践
一、背景 天云趋势在2012年下半年开始为某大型国有银行的历史交易数据备份及查询提供基于Hadoop的技术解决方案,由于行业的特殊性,客户对服务的可用性有着非常高的要求,而HDFS长久以来都被单点故障的问题所困扰,直到Apache Hadoop在2012年5月发布了2.0的alpha版本,其中MRv2还很不成熟,可HDFS的新功能已经基本可用,尤其是其中的的High Availability(以下简称HA)和Federation。Cloudera也于7月制作了CDH4.0.1,包含了Hadoop 2.0的诸多新功能和组件,于是我们就基于CDH4.0.1进行了HA和Federation的测试。 此工作由我和同事张军、钱兴会共同完成。 二、为什么需要HA和Federation 单点故障 在Hadoop 2.0之前,也有若干技术试图解决单点故障的问题,我们在这里做个简短的总结 Secondary NameNode。它不是HA,它只是阶段性的合并edits和fsimage,以缩短集群启动的时间。当NameNode(以下简称NN)失效的时候,Secondary NN并无法立刻提供服务,Secon...
相关文章
文章评论
共有0条评论来说两句吧...
文章二维码
点击排行
推荐阅读
最新文章
- Windows10,CentOS7,CentOS8安装MongoDB4.0.16
- CentOS8编译安装MySQL8.0.19
- MySQL8.0.19开启GTID主从同步CentOS8
- CentOS8安装Docker,最新的服务器搭配容器使用
- CentOS8,CentOS7,CentOS6编译安装Redis5.0.7
- SpringBoot2整合Redis,开启缓存,提高访问速度
- CentOS7,8上快速安装Gitea,搭建Git服务器
- Docker使用Oracle官方镜像安装(12C,18C,19C)
- CentOS关闭SELinux安全模块
- SpringBoot2初体验,简单认识spring boot2并且搭建基础工程