|
|
|
|
|
|
by jerf
3644 days ago
|
|
|
No, I'm fairly sure this saying is used in the context of the implementations actually being slow. "You're always in IO" is a different defense used for the slow languages, one which I have also found is overstated, for what it's worth. I have found in practice it isn't that hard to build even a simple-looking REST interface that has non-trivial amounts of time spent in non-IO code in Perl or Python. I think a lot of people would be shocked if they sat down and really worked out just how little Python/Perl/etc. code they can run before they webserver noticeably starts slowing down at even small scales. |
|
|
I spent a little time working with Guido on cache design for the App Engine data access api (ndb). In Java-land, it's a big win to cache datastore entities in memcache because fetch latency is about an order of magnitude faster. In Python-land, caching is a mixed bag - real-world profiling found marginal benefit even with 100% hit rate, and a significant performance disadvantage if the hit rate drops off. This is primarily attributed to pickling overhead.
If you're curious, here are the benchmarks (from 2011): https://github.com/googlecloudplatform/datastore-ndb-python/...