Taking Advantage of GitHubIterator

Let’s say that for some reason you’re stalking all of GitHub’s users and you just so happen to be using github3.py to do this. You might write code that looks like this:

import github3

g = github3.login(USERNAME, PASSWORD)

for u in g.iter_all_users():

The problem is that you will then have to reiterate over all of the users each time you want to get the new users. You have two approaches you can take to avoid this with GitHubIterator.

You can not call the method directly in the for-loop and keep the iterator as a separate reference like so:

i = g.iter_all_users():

for u in i:

The First Approach

Then after your first pass through your GitHubIterator object will have an attribute named etag. After you’ve added all the currently existing users you could do the following to retrieve the new users in a timely fashion:

import time

while True:
    for u in i:

    time.sleep(120)  # Sleep for 2 minutes

The Second Approach

etag = i.etag
# Store this somewhere

# Later when you start a new process or go to check for new users you can
# then do

i = g.iter_all_users(etag=etag)

for u in i:

If there are no new users, these approaches won’t impact your rate limit at all. This mimics the ability to conditionally refresh data on almost all other objects in github3.py.