@@ -14,9 +14,12 @@
 
 """Scanner for the Big Query rules engine."""
 
+import collections
+
 from google.cloud.forseti.common.gcp_type.bigquery_access_controls import (
     BigqueryAccessControls)
 
+from google.cloud.forseti.common.gcp_type import project
 from google.cloud.forseti.common.util import logger
 from google.cloud.forseti.scanner.audit import bigquery_rules_engine
 from google.cloud.forseti.scanner.scanners import base_scanner
# Copyright 2017 The Forseti Security Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Scanner for the Big Query rules engine."""

from google.cloud.forseti.common.gcp_type.bigquery_access_controls import (
    BigqueryAccessControls)

from google.cloud.forseti.common.util import logger
from google.cloud.forseti.scanner.audit import bigquery_rules_engine
from google.cloud.forseti.scanner.scanners import base_scanner


LOGGER = logger.get_logger(__name__)


class BigqueryScanner(base_scanner.BaseScanner):
    """Scanner for BigQuery acls."""

    def __init__(self, global_configs, scanner_configs, service_config,
                 model_name, snapshot_timestamp,
                 rules):
        """Initialization.

        Args:
            global_configs (dict): Global configurations.
            scanner_configs (dict): Scanner configurations.
            service_config (ServiceConfig): Forseti 2.0 service configs
            model_name (str): name of the data model
            snapshot_timestamp (str): Timestamp, formatted as YYYYMMDDTHHMMSSZ.
            rules (str): Fully-qualified path and filename of the rules file.
        """
        super(BigqueryScanner, self).__init__(
            global_configs,
            scanner_configs,
            service_config,
            model_name,
            snapshot_timestamp,
            rules)
        self.rules_engine = bigquery_rules_engine.BigqueryRulesEngine(
            rules_file_path=self.rules,
            snapshot_timestamp=self.snapshot_timestamp)
        self.rules_engine.build_rule_book(self.global_configs)

    @staticmethod
    def _flatten_violations(violations):
        """Flatten RuleViolations into a dict for each RuleViolation member.

        Args:
            violations (list): The RuleViolations to flatten.

        Yields:
            dict: Iterator of RuleViolations as a dict per member.
        """
        for violation in violations:
            violation_data = {'dataset_id': violation.dataset_id,
                              'full_name': violation.full_name,
                              'access_domain': violation.domain,
                              'access_user_by_email': violation.user_email,
                              'access_special_group': violation.special_group,
                              'access_group_by_email': violation.group_email,
                              'role': violation.role, 'view': violation.view}
            yield {
                'resource_id': violation.resource_id,
                'resource_type': violation.resource_type,
                'full_name': violation.full_name,
                'rule_index': violation.rule_index,
                'rule_name': violation.rule_name,
                'violation_type': violation.violation_type,
                'violation_data': violation_data,
                'resource_data': violation.resource_data
            }

    def _output_results(self, all_violations):
        """Output results.

        Args:
            all_violations (list): A list of BigQuery violations.
        """
        all_violations = self._flatten_violations(all_violations)
        self._output_results_to_db(all_violations)

    def _find_violations(self, bigquery_data):
        """Find violations in the policies.

        Args:
            bigquery_data (list): Big Query data to find violations in

        Returns:
            list: A list of BigQuery violations
        """
        all_violations = []
        LOGGER.info('Finding BigQuery acl violations...')

        for bigquery_acl in bigquery_data:
            violations = self.rules_engine.find_policy_violations(
                bigquery_acl)
            LOGGER.debug(violations)
            all_violations.extend(violations)
        return all_violations

    def _retrieve(self):
        """Retrieves the data for scanner.

        Returns:
            list: BigQuery ACL data
        """
        model_manager = self.service_config.model_manager
        scoped_session, data_access = model_manager.get(self.model_name)
        with scoped_session as session:
            bq_acls = []

            for policy in data_access.scanner_iter(session, 'dataset_policy'):
                dataset_id = policy.parent.name
                # There is no functional use for project_id in this scanner,
                # other than to identify where the dataset comes from,
                # which can now be done with full_name.
                # In case you are tempted to get the project_id anyways,
                # do not use project_id = policy.parent.parent.name
                # which will cause db session conflict.
                # Instead, parse the project_id from the full_name.
                bq_acls.extend(
                    BigqueryAccessControls.from_json(
                        project_id=None,
                        dataset_id=dataset_id,
                        full_name=policy.full_name,
                        acls=policy.data))

        return bq_acls

    def run(self):
        """Runs the data collection."""
        bigquery_acls_data = self._retrieve()
        all_violations = self._find_violations(bigquery_acls_data)
        self._output_results(all_violations)

(here and in function parameters of find_policy_violations). To avoid ambiguity, I think we should either rename resource to something like parent_project, or change resource to storing the dataset resource. A possible advantage of using the dataset as the resource is it allows the bigquery scanner to examine other properties of the dataset (e.g. location) in the future.