Sadly, in Python, we do not have a ggplot2.
https://github.com/sirrice/pygg provides the ggplot2 syntax in Python as a wrapper around Wickham's R implementation. It is useful if you 1) want the R syntax, 2) program in Python, 3) just want static plots.
Recreating and keeping up with Hadley's hard work is challenging, particularly because ggplot2's layout and extensions are really nice and continue to evolve.
As an alternative that preserves the full power of Wickham's implementation, pygg[1] is a Python wrapper that provides R's ggplot2 syntax in Python and runs everything in R.
There's some related research out of UIUC that's pretty neat. http://dataspread.github.io/
Relevant paper: Queues are databases, Jim Gray
It would be nice to see longer running experimentsto see how a) long term usage (compaction in LSM trees) and b) disk interactions (benefits of compression) impact performance.
For example, in the large values experiment, the experiment executes 1000 writes. However if the results report LevelDB as 1060 ops/sec. Assuming op == write, then the experiment ran for all of 1 second. This shows great instantaneous performance, but what if it kept running?
Additionally, it appears that no-compression is the way to go, which makes sense for small values and an in-memory experiment, but is that the case when disk comes into play?