Time streaming exec scheduling

[omatthew98]

Why are these changes needed?

Currently we are not timing how much time is spent during scheduling in the streaming executor. This times the total process_time for the scheduling steps / calls to _scheduling_loop_step. This stat is included in DatasetStats and a later PR will include this and other StreamingExecutor stats into the DatasetStatsSummary.

Related issue number

Closes #42797

Checks

• I've signed off every commit(by using the -s flag, i.e., git commit -s) in this PR.
• I've run scripts/format.sh to lint the changes in this PR.
• I've included any doc changes needed for https://docs.ray.io/en/master/.
  • I've added any new APIs to the API Reference. For example, if I added a
    method in Tune, I've added it in doc/source/tune/api/ under the
    corresponding .rst file.
• I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/
• Testing Strategy
  • Manual testing
  • Unit tests
  • Release tests
  • This PR is not tested :(

[omatthew98]

Conducted some manual testing with the following snippet (and adding a line to log the time from within StreamingExecutor.run):

def sleep(x):
    time.sleep(0.5)
    return x

num_rows = sys.argv[1] if len(sys.argv) > 1 else 10

ds = ray.data.range(num_rows).map(sleep)

for _ in ds.iter_batches(batch_size=1):
    continue

For num_rows = 100 the total scheduling time was 0.23742400000000297 and for num_rows = 1000 the total scheduling time was 1.063609000000004. More comprehensive testing will be added in followup pr which will update the metrics being reported out through DatasetStatsSummary.

[can-anyscale]

Seem to broke linux://python/ray/data:test_streaming_integration

[can-anyscale]

I'm trying to revert to unblock test failures