November 2017
Intermediate to advanced
328 pages
6h 51m
Japanese
Content preview from 入門 PySpark ―PythonとJupyterで活用するSpark 2エコシステム
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1.3
Spark 2.0
のアーキテクチャ
13
1.3.4
Structured Streaming
2016 年の Spark Summit EastでReynold Xinは次のように引用しました。
「ストリーミング分析を行う最もシンプルな方法は、ストリーミングにこだわらないことだ」
これは、Structured Streamingを構築する上での基盤になっています。ストリーミングは強力で
すが、重要な問題の一つとして構築とメンテナンスが難しいということがあります。Uber、Netflix、
Pinterestといった企業は Spark Streamingをプロダクション環境で動作させていますが、同時にその
システムの可用性を高く保つために専門のチームを持っています。
Spark Streaming
の高レベルでの概要については、
Spark Streaming: What Is It and Who's
Using It?
（
http://bit.ly/1Qb10f6
）を参照してください。
すでに述べたとおり、Spark Streaming の運用にあたってはイベントの遅延、最終的なデータソース
への出力の欠落、障害時の状態の回復、読み書きの分散処理など、問題になり得ることがたくさんあり
ます（これがすべてというわけでもありません）。
Spark 1.3
静的な
DataFrame
Spark 2.0
無限の
DataFrame
単一の
API
A Deep Dive into Structured Streaming ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.