from math import log10

from data.model.repo_mirror import (get_eligible_mirrors, get_max_id_for_repo_mirror_config,
                                    get_min_id_for_repo_mirror_config)
from data.database import RepoMirrorConfig
from util.migrate.allocator import yield_random_entries

from workers.repomirrorworker.models_interface import (RepoMirrorToken, RepoMirrorWorkerDataInterface)


class RepoMirrorModel(RepoMirrorWorkerDataInterface):
  def repositories_to_mirror(self, start_token=None):
    def batch_query():
      return get_eligible_mirrors()

    # Find the minimum ID.
    if start_token is not None:
      min_id = start_token.min_id
    else:
      min_id = get_min_id_for_repo_mirror_config()

    # Get the ID of the last repository mirror config. Will be None if there are none in the database.
    max_id = get_max_id_for_repo_mirror_config()
    if max_id is None:
      return (None, None)

    if min_id is None or min_id > max_id:
      return (None, None)

    # 4^log10(total) gives us a scalable batch size into the billions.
    batch_size = int(4**log10(max(10, max_id - min_id)))

    iterator = yield_random_entries(
      batch_query,
      RepoMirrorConfig.id,
      batch_size,
      max_id,
      min_id)

    return (iterator, RepoMirrorToken(max_id + 1))

repo_mirror_model = RepoMirrorModel()