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NEW QUESTION: 1
The advantage of core tiles is that they allow you an opportunity to examine the cause of problems, so that they can be resolved.
However, core files must be managed because they_____.
A. steal resources from the processor, slowing down system performance
B. fill up swap space; this will begin to slow the system due to swaps
C. fill up swap space; this will begin to slow the system due to paging
D. take up large amounts of disk space
E. make numerous entries into the /var/adm/wtmpx file
Answer: D
Explanation:
Part of the job of cleaning up heavily loaded file systems involves locating and removing files that have not been used recently. You can locate unused files by using the ls or find commands.
Other ways to conserve disk space include emptying temporary directories such as the directories located in /var/tmp or /var/spool, and deleting core and crash dump files.
Note: Core files are generated when a process or application terminates abnormally. Core files are managed with the coreadm command.
For example, you can use the coreadm command to configure a system so that all process core files are placed in a single system directory. This means it is easier to track problems byexamining the core files in a specific directory whenever a process or daemon terminates abnormally.
Reference: Oracle Solaris Administration: Common Tasks, Finding and Removing Old or Inactive Files
NEW QUESTION: 2
The Hadoop framework provides a mechanism for coping with machine issues such as faulty configuration or impending hardware failure. MapReduce detects that one or a number of machines are performing poorly and starts more copies of a map or reduce task. All the tasks run simultaneously and the task finish first are used. This is called:
A. Combine
B. Default Partitioner
C. IdentityReducer
D. Speculative Execution
E. IdentityMapper
Answer: D
Explanation:
Speculative execution: One problem with the Hadoop system is that by dividing the tasks across many nodes, it is possible for a few slow nodes to rate-limit the rest of the program. For example if one node has a slow disk controller, then it may be reading its input at only 10% the speed of all the other nodes. So when 99 map tasks are already complete, the system is still waiting for the final map task to check in, which takes much longer than all the other nodes. By forcing tasks to run in isolation from one another, individual tasks do not know where their inputs come from. Tasks trust the Hadoop platform to just deliver the appropriate input. Therefore, the same input can be processed multiple times in parallel, to exploit differences in machine capabilities. As most of the tasks in a job are coming to a close, the Hadoop platform will schedule redundant copies of the remaining tasks across several nodes which do not have other work to perform. This process is known as speculative execution. When tasks complete, they announce this fact to the JobTracker. Whichever copy of a task finishes first becomes the definitive copy. If other copies were executing speculatively, Hadoop tells the TaskTrackers to abandon the tasks and discard their outputs. The Reducers then receive their inputs from whichever Mapper completed successfully, first.
Reference: Apache Hadoop, Module 4: MapReduce
Note:
*Hadoop uses "speculative execution." The same task may be started on multiple boxes. The first one to finish wins, and the other copies are killed.
*There are a few reasons Hadoop can kill tasks by his own decisions:
Failed tasks are tasks that error out.
a) Task does not report progress during timeout (default is 10 minutes)
b) FairScheduler or CapacityScheduler needs the slot for some other pool (FairScheduler) or queue (CapacityScheduler).
c) Speculative execution causes results of task not to be needed since it has completed on other place.
Reference: Difference failed tasks vs killed tasks
NEW QUESTION: 3
3月から5月の時間有効期間間隔を使用してこの時間階層をクエリすると、次の結果のうちどれが生成されますか？
A. C
B. B
C. D
D. A
Answer: C
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