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There's a slide deck[1] that explains it rather well.

Basically, Tachyon acts as a distributed, reliable, in memory file system.

To generalise enormously, programs have problems sharing data in RAM. Tachyon lets you share data between (say) your Spark jobs and your Hadoop Map/Reduce jobs at RAM speed, even across machines (it understands data-locality, so will attempt to keep data close to where it is being used).

[1] http://www.cs.berkeley.edu/~haoyuan/talks/Tachyon_2014-10-16...




Neat, thanks for the link, the code examples towards the end make it clear that this is pretty simple to use.


Yeah, most things coming from the Spark team are excellent in that respect.

I've never used Tachyon, but based on the wonderful "getting started" experience Spark gives I'd be confident it would be similarly well thought out.




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