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Sampling has long been a prominent tool in statistics and analytics, first and foremost when very large amounts of data are involved. In the realm of very large file systems (and hierarchical data stores in general), however, sampling has mostly been ignored and for several good reasons. Mainly, running sampling in such an environment introduces technical challenges that make the entire sampling process non-beneficial. In this work we demonstrate that there are cases for which sampling is very worthwhile in very large file systems. We address this topic in two aspect: (a) the technical side where we design and implement solutions to efficient weighted sampling that is also distributed, one-pass and addresses multiple efficiency aspects; and (b) the usability aspect in which we demonstrate several use-cases in which weighted sampling over large file systems is extremely beneficial. In particular, we show use-cases regarding estimation of compression ratios, testing and auditing and offline collection of statistics on very large data stores.

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