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NEW QUESTION: 1
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. IdentityReducer
B. Speculative Execution
C. Combine
D. Default Partitioner
E. IdentityMapper
Answer: B
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.
Failed tasks are tasks that error out.
*There are a few reasons Hadoop can kill tasks by his own decisions:
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: 2
Warum ist / etc / shadow nicht weltweit lesbar, wenn die Passwörter verschlüsselt gespeichert sind?
A. Dies ist nur aus historischen Gründen.
B. Die Datei enthält weitere Informationen, die geheim gehalten werden müssen.
C. Die Passwörter können von jedem mit Root-Zugriff entschlüsselt werden.
D. Die verschlüsselten Passwörter sind weiterhin Brute-Force-Angriffen ausgesetzt.
Answer: D
NEW QUESTION: 3
RESTインターフェイスを使用してHTTPSでAzure Storageに接続するWebアプリを開発しています。このアプリは、世界中のどこからでもアクセスできるビデオコンテンツをアップロードおよびストリーミングします。
アプリの各部分に必要なストレージ容量は異なります。階層ネームスペースを作成する必要があります。
どのストレージサービスを実装する必要がありますか？答えるために、正しい行動に適切なサービスを選択してください。各サービスは、1回、複数回、またはまったく使用しないことができます。コンテンツを表示するには、ペイン間の分割バーをドラッグするか、スクロールする必要があります。
注：それぞれ正しい選択は1ポイントの価値があります。
Answer:
Explanation:
説明
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