50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
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from math import log10
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from app import app
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from data.model.image import (get_images_eligible_for_scan, get_image_pk_field,
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get_max_id_for_sec_scan, get_min_id_for_sec_scan)
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from util.migrate.allocator import yield_random_entries
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from workers.securityworker.models_interface import (ScanToken, SecurityWorkerDataInterface)
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class PreOCIModel(SecurityWorkerDataInterface):
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def candidates_to_scan(self, target_version, start_token=None):
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def batch_query():
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return get_images_eligible_for_scan(target_version)
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# Find the minimum ID.
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min_id = None
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if start_token is not None:
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min_id = start_token.min_id
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else:
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min_id = app.config.get('SECURITY_SCANNER_INDEXING_MIN_ID')
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if min_id is None:
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min_id = get_min_id_for_sec_scan(target_version)
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# Get the ID of the last image we can analyze. Will be None if there are no images in the
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# database.
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max_id = get_max_id_for_sec_scan()
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if max_id is None:
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return (None, None)
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if min_id is None or min_id > max_id:
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return (None, None)
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# 4^log10(total) gives us a scalable batch size into the billions.
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batch_size = int(4**log10(max(10, max_id - min_id)))
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# TODO: Once we have a clean shared NamedTuple for Images, send that to the secscan analyzer
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# rather than the database Image itself.
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iterator = yield_random_entries(
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batch_query,
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get_image_pk_field(),
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batch_size,
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max_id,
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min_id,)
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return (iterator, ScanToken(max_id + 1))
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pre_oci_model = PreOCIModel()
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