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Skream is a high-performance time-series library with memory-footprint guarantees.

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The example below computes quantiles for randomly shuffled numbers in the range 0..1000. The 10% and 90% quantiles don't look good. By contrast the this Java library https://github.com/mayconbordin/streaminer gives reasonably...

Find a dataset with an authoritative answer.

Check whether memoizing the get-new-_math_ functions in the Skreams themselves (metadata again?) helps performance.

Implement a persistent store for session information, so Skreams survive a bounce of the REST server. Maybe into Redis?