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NEW QUESTION: 1
A. Option B
B. Option C
C. Option A
D. Option D
Answer: A
NEW QUESTION: 2
列ストアテーブルの利点は何ですか？
この質問には3つの正解があります。
応答：
A. データのフットプリントは圧縮によって自動的に削減されます
B. 必要な列のみが実際にメモリにロードされます
C. 列を分割できます
D. 大量の読み取り/書き込みトランザクション処理用に最適化されています
Answer: A,B,C
NEW QUESTION: 3
In the standard word count MapReduce algorithm, why might using a combiner reduce the overall Job running time?
A. Because combiners perform local aggregation of word counts, thereby allowing the mappers to process input data faster.
B. Because combiners perform local aggregation of word counts, thereby reducing the number of key-value pairs that need to be snuff let across the network to the reducers.
C. Because combiners perform local aggregation of word counts, and then transfer that data to reducers without writing the intermediate data to disk.
D. Because combiners perform local aggregation of word counts, thereby reducing the number of mappers that need to run.
Answer: A
Explanation:
*Simply speaking a combiner can be considered as a"mini reducer"that will be applied potentially several times still during the map phase before to send the new (hopefully reduced) set of key/value pairs to the reducer(s). This is why a combiner must implement the Reducer interface (or extend the Reducer class as of hadoop 0.20).
*Combiners are used to increase the efficiency of a MapReduce program. They are used to aggregate intermediate map output locally on individual mapper outputs. Combiners can help you reduce the amount of data that needs to be transferred across to the reducers. You can use your reducer code as a combiner if the operation performed is commutative and associative. The execution of combiner is not guaranteed, Hadoop may or may not execute a combiner. Also, if required it may execute it more then 1 times. Therefore your MapReduce jobs should not depend on the combiners execution.
Reference:24 Interview Questions & Answers for Hadoop MapReduce developers,What are combiners? When should I use a combiner in my MapReduce Job?