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        Administrator Training for Apache Hadoop培訓(xùn)

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 ecosystem components in CDH 5, including (but not limited to): Impala, Flume, Oozie, Hue, 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