Pierre-Yves David <pierre-yves.david@fb.com> [Wed, 07 Jan 2015 10:32:17 -0800] rev 23814
setdiscovery: drop the 'always' argument to '_updatesample'
This argument exists because of the complex code flow in '_takequicksample'. It
first gets the list of heads and then calls '_updatesample' on an empty initial
sample and a size limit matching the differences between the number of heads and
the target sample size. Finally the heads and the sample from '_updatesample'
were added. To ensure this addition result had the exact target length, the code
had to ensure no elements from the heads were added to the '_updatesample'
content and therefore was passing this "always included set of heads".
Instead we can just update the initial heads sample directly and use the final
target size as target size for the update.
This removes the need for this 'always' parameter to the '_updatesample' function
The test are affected because different set building order results in different
random sampling.
Pierre-Yves David <pierre-yves.david@fb.com> [Wed, 07 Jan 2015 17:28:51 -0800] rev 23813
setdiscovery: always add exponential sample to the heads
As explained in a previous changeset, prioritizing heads too much behaves
pathologically when there are more heads than the sample size. To counter this,
we always inject exponential samples before reducing to the sample size limit.
This already show some benefit in the test themselves, but on a real-world example
this moves my discovery for push to pathologically headed repo from 45 rounds to
17 of them.
We should maybe ensure that at least 25% of the result sample is heads, but I
think the random sampling will be fine in practice.
Pierre-Yves David <pierre-yves.david@fb.com> [Wed, 07 Jan 2015 17:23:21 -0800] rev 23812
setdiscovery: directly run '_updatesample'
The heads and exponential sample are going to end up in the same set
before any extra processing happens. We simplify the code by directly
updating a set with heads.
Changes in the order the set is built lead to small changes in the random
sampling output. But after double checking, I can confirm the input data to
the random sampling is consistent.