perf: perform a garbage collection before each iteration Currently, no explicit garbage collection is performed when running the microbenchmarks in `hg perf`. I think this is wrong because garbage collection can have a significant impact on execution times. And, if gc is triggered via the default heuristics, it will fire effectively randomly during subsequent benchmark iterations due to variable amount of garbage left over from previous runs. Running a gc before invoking the measured function will help ensure state is more consistent across all iterations.

formatter: support json formatting of long type By luck, we appear to not pass any long instances into the JSON formatter. I suspect this will change with all the Python 3 porting work. Plus I have another series that will convert some ints to longs that triggers this.

rebase: don't use mutable default argument value

mq: don't use mutable default argument value

util: don't use mutable default argument value I don't think this is any tight loops and we'd need to worry about PyObject creation overhead. Also, I'm pretty sure strptime() will be much slower than PyObject creation (date parsing is surprisingly slow).

match: don't use mutable default argument value There shouldn't be a big perf hit creating a new object because this function is complicated and does things that dwarf the cost of creating a new PyObject.

hgweb: don't use mutable default argument value

hgweb: don't use mutable default argument value

filemerge: don't use mutable default argument value

context: don't use mutable default argument value Mutable default argument values are a Python gotcha and can represent subtle, hard-to-find bugs. Lets rid our code base of them.