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A Cloudera Certified Administrator for Apache Hadoop (CCAH) certification proves that you have demonstrated your technical knowledge, skills, and ability to configure, deploy, maintain, and secure an Apache Hadoop cluster. Cloudera Certified Administrator for Apache Hadoop (CCA-500) Number of Questions: 60 questions Time Limit: 90 minutesPassing Score: 70% Language: English, Japanese Price: NOT AVAILABLE 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 DesignGiven 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 SystemShell Understand how to deploy core ecosystem components, including Spark, Impala, and Hive Understand how to deploy MapReduce v2 (MRv2 / YARN), including all YARN daemons Understand basic design strategy for YARN and Hadoop Determine how YARN handles resource allocations Identify the workflow of job running on YARN Determinewhich 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 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 swappingGiven 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/ODisk 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 Given a scenario, identify how the cluster will handle diskand 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 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 allocatescluster resources under YARN Given a scenario, determine how the Capacity Scheduler allocates cluster resources 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 howto monitor swap and memory allocation on all nodes Identify how to view and manage Hadoop’s log files Interpret a log file Disclaimer: These exam preparation pages are intended to provide information about the objectives covered by each exam, related resources, and recommended reading and courses. The material contained within these pages isnot intended to guarantee a passing score on any exam. Cloudera recommends that a candidate thoroughly understand the objectives for each exam and utilize the resources and training courses recommended on these pages to gain a thorough understand of the domain of knowledge related to the role the exam evaluates. Chapter 1: Thank You andLet's Get Started Course Structure 09:56 Tools & Setup 06:24 Tools & Setup (Linux) 05:21 Chapter 2: Introduction To Big Data What is Big Data? 17:47 Understanding Big Data Problem 14:24 History of Hadoop 03:46 Quiz 1 Test your understanding of Big Data Chapter 3: HDFS HDFS - Why Another Filesystem? 13:20 Blocks 07:50 WorkingWith HDFS 16:09 HDFS - Read & Write 09:23 Quiz 2 Test your understanding of HDFS HDFS Assignment Article Chapter 4: MapReduce Introduction to MapReduce 08:51 Dissecting MapReduce Components 18:03 Dissecting MapReduce Program (Part 1) 12:00 Dissecting MapReduce Program (Part 2) 16:06 Quiz 3 Test your understandingof MapReduce Chapter 5: Architechture HDFS Architechture 12:46 Secondary Namenode 11:24 Highly Available Hadoop 08:48 MRv1 Architechture 10:40 YARN 11:22 Quiz 4 Test your understanding of Hadoop Architechture Chapter 6: Cluster Planning Hadoop Versions 11:21 Software Requirements 06:46 Hardware Requirements 15:48Cluster Sizing 06:48 JBOD vs. RAID 17:07 Network Topology 16:00 Kernel Level Tuning 11:35 Quiz 5 Test your understanding of Cluster Planning Chapter 7: Cluster Setup Vendors & Hosting 06:36 Virtual Image Setup (Part-1) 08:53 Virtual Image Setup (Part-2) 25:42 Cluster Setup (Part 1) 23:43 Cluster Setup (Part 2) 25:35 Cluster Setup(Part 3) 18:01 Amazon EMR 15:46 Quiz 6 Test your understanding of Cluster Setup Chapter 8: Day to Day Essentials Getting To Know Your Cluster 11:49 Disk Usage 08:45 Quotas 10:29 Recovering from Accidental Data Loss 11:35 Stop Start Restart 15:35 Adding and Removing Nodes 16:30 Network Topology 09:42 Quiz 7 Test yourunderstanding of Day to Day Essentials Chapter 9: Troubleshooting Exploring Logs 10:18 Namenode Stuck In Safe Mode 15:23 Namenode - Failure and Recovery 14:13 Memory Issues 16:03 Chapter 10: Kerberos Authentication Introduction To Kerberos Authentication 08;19 Installing & Configuring Kerberos 20:16 Create Needed Principals& Keytabs For Kerberos Authentication 14:50 Configure & Enable Kerberos Authentication in Hadoop 19:54 Quiz 8 Test your understanding of Kerberos Authentication Chapter 11: High Availability Introduction To High Availability & Installation 08:53 Configuring High Availability with Quorum Journal Manager 10:13 Convert Cluster to HighAvailable Cluster & Verification 15:15 Quiz 9 Test your understanding of High Availability Chapter 12: Resource Management FIFO Scheduler 14:19 Introduction To Capacity Scheduler 10:23 Configuring & Experiments with Capacity Scheduler 09:30 Introduction To Fair Scheduler 12:50 Granular Resource Management with Fair Scheduler 11:25 Lesson 55 Dominant Resource Fairness & Protecting Queues in Fair Scheduler 15:10 Quiz 10 Test your understanding of Resource Management Chapter 13: Cloudera Manager Introduction To Cloudera Manager 13:09 Installing Cloudera Manager 24:07 Working with Cloudera Manager 16:23 Monitoring with Cloudera Manager 21:04Troubleshooting with Cloudera Manager 10:13 Quiz 11 Test your understanding of Cloudera Manager Chapter 14: Apache Ambari Cluster Installation with Apache Ambari 23:35 Apache Ambari Walk through 11:10 Resource Manager High Availability with Apache Ambari 16:53 Cluster Upgrade with Apache Ambari 17:48 Chapter 15: Tools InHadoop Ecosystem Introduction To Apache Pig 11:16 Installing Apache Pig 05:47 Introduction To Apache Hive 09:17 Dissect a Hive Table 10:13 Installing Apache Hive 21:55 Introduction To Apache Sqoop 13:50 Installing Apache Sqoop 05:57 Introduction To Apache Flume 13:52 Installing Apache Flume 03:27 Quiz 12 Test yourunderstanding of Tools In Hadoop Ecosystem Chapter 16: Puppet Puppet - The Why & The What 12:41 Puppet Installation 9:43 Puppet Concepts with Tomcat Installation 24:12 Installing Hadoop with Puppet 27:33 The Hadoop Distributed File System (HDFS) namenode maintains states of all datanodes. There are two types of states. The fist typedescribes the liveness of a datanode indicating if the node is live, dead or stale. The second type describes the admin state indicating if the node is in service, decommissioned or under maintenance. When an administrator decommission a datanode, the datanode will first be transitioned into DECOMMISSION INPROGRESS state. After all blocksbelonging to that datanode have been fully replicated elsewhere based on each block’s replication factor. the datanode will be transitioned to DECOMMISSIONED state. After that, the administrator can shutdown the node to perform long-term repair and maintenance that could take days or weeks. After the machine has been repaired, the machinecan be recommissioned back to the cluster. Sometimes administrators only need to take datanodes down for minutes/hours to perform short-term repair/maintenance. In such scenario, the HDFS block replication overhead incurred by decommission might not be necessary and a light-weight process is desirable. And that is what maintenance state isused for. When an administrator put a datanode in maintenance state, the datanode will first be transitioned to ENTERING MAINTENANCE state. As long as all blocks belonging to that datanode is minimally replicated elsewhere, the datanode will immediately be transitioned to IN MAINTENANCE state. After the maintenance has completed, theadministrator can take the datanode out of the maintenance state. In addition, maintenance state supports timeout that allows administrators to config the maximum duration in which a datanode is allowed to stay in maintenance state. After the timeout, the datanode will be transitioned out of maintenance state automatically by HDFS without humanintervention. In summary, datanode admin operations include the followings: Decommission Recommission Putting nodes in maintenance state Taking nodes out of maintenance state And datanode admin states include the followings: NORMAL The node is in service. DECOMMISSIONED The node has been decommissioned.DECOMMISSION INPROGRESS The node is being transitioned to DECOMMISSIONED state. IN MAINTENANCE The node in in maintenance state. ENTERING MAINTENANCE The node is being transitioned to maintenance state. To perform any of datanode admin operations, there are two steps. Update host-level configuration files to indicate thedesired admin states of targeted datanodes. There are two supported formats for configuration files. Hostname-only configuration. Each line includes the hostname/ip address for a datanode. That is the default format. JSON-based configuration. The configuration is in JSON format. Each element maps to one datanode and each datanode can havemultiple properties. This format is required to put datanodes to maintenance states. Run the following command to have namenode reload the host-level configuration files. hdfs dfsadmin [-refreshNodes] This is the default configuration used by the namenode. It only supports node decommission and recommission; it doesn’t support admin operationsrelated to maintenance state. Use dfs.hosts and dfs.hosts.exclude as explained in hdfs-default.xml. In the following example, host1 and host2 need to be in service. host3 and host4 need to be in decommissioned state. dfs.hosts file dfs.hosts.exclude file JSON-based format is the new configuration format that supports generic properties on datanodes.Set the following configurations to enable JSON-based format as explained in hdfs-default.xml. Setting Value dfs.namenode.hosts.provider.classname inedHostFileManager dfs.hosts the path of the json hosts file Here is the list of currently supported properties by HDFS. Property DescriptionhostName Required. The host name of the datanode. upgradeDomain Optional. The upgrade domain id of the datanode. adminState Optional. The expected admin state. The default value is NORMAL; DECOMMISSIONED for decommission; IN MAINTENANCE for maintenance state. port Optional. the port number of the datanodemaintenanceExpireTimeInMS Optional. The epoch time in milliseconds until which the datanode will remain in maintenance state. The default value is forever. In the following example, host1 and host2 need to in service. host3 need to be in decommissioned state. host4 need to be in in maintenance state. dfs.hosts file [ { "hostName": "host1" }, {"hostName": "host2", "upgradeDomain": "ud0" }, { "hostName": "host3", "adminState": "DECOMMISSIONED" }, { "hostName": "host4", "upgradeDomain": "ud2", "adminState": "IN MAINTENANCE" } ] There are several cluster-level settings related to datanode administration. For common use cases, you should rely on the default values. Please referto hdfs-default.xml for descriptions and default values. dfs.namenode.maintenance.replication.min dfs.namenode.decommission.interval dfs.namenode.decommission.blocks.per.interval odes The original decommissioning algorithm has issues when DataNodes having lots of blocks aredecommissioned such as Write lock in the NameNode could be held for a long time for queueing re-replication. Re-replication work progresses node by node if there are multiple decommissioning DataNodes. HDFS-14854 introduced new decommission monitor in order to mitigate those issues. This feature is currently marked as experimental anddisabled by default. You can enable this by setting the value of dfs.namenode.decommission.monitor.class to nodeAdminBackoffMonitor in hdfs-site.xml. The relevant configuration properties are listed in the table below. Please refer to hdfs-default.xml for descriptions and default values. Propertydfs.namenode.decommission.monitor.class limit blocks.per.lock Admin states are part of the namenode’s webUI and JMX. As explained in HDFSCommands.html, you can also verify admin states using the following commands. Use dfsadmin tocheck admin states at the cluster level. hdfs dfsadmin -report Use fsck to check admin states of datanodes storing data at a specific path. For backward compatibility, a special flag is required to return maintenance states. hdfs fsck // only show decommission state hdfs fsck -maintenance // include maintenance state

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Beginning apache hadoop administration pdf. Apache hadoop vs hive. . 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 .