And that's just the beginning of what you can
do with MapReduce. Recommendation systems,
fraud detection, item classification are
all great problems for MapReduce. These all have some
basic characteristics in common. They have a lot of data,
and the work can be parallelized rather than slogging
through in serial. Perhaps one of the more difficult things
to learn when you're new to Hadoop. Is how to
think about solving problems in terms of MapReduce. It can
be very different than how you're used to working. And
frankly, it takes a lot of practice. So in the next
lesson, we're going to write the code for the sales by
store problem, and we'll also start talking about other MapReduce problems.
ここまでがMapReduceの基本です
レコメンドシステムや
不正の検知や 項目の分類などは
MapReduce処理に適しています
共通する特徴は データが膨大であること
処理が並列で行えることです
Hadoopを初めて学ぶ人にとって
おそらく一番難しいことは
MapReduceでの問題解決を考えることです
いままでのプログラムと大きく違います
実際 かなりの練習が必要です
次のレッスンでは実際にコードを書き
他のMapReduceの処理例にも触れます