зеркало из https://github.com/mozilla/treeherder.git
297 строки
8.8 KiB
Python
297 строки
8.8 KiB
Python
import json
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from tests.sampledata import SampleData
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from treeherder.client import TreeherderClient
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from treeherder.model.derived.refdata import RefDataManager
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def post_collection(project, th_collection, auth=None):
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client = TreeherderClient(protocol='http', host='localhost', auth=None)
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return client.post_collection(project, th_collection)
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def do_job_ingestion(jm, refdata, job_data, sample_resultset, verify_data=True):
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"""
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Ingest ``job_data`` which will be JSON job blobs.
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``verify_data`` - whether or not to run the ingested jobs
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through the verifier.
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"""
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jm.store_result_set_data(sample_resultset)
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max_index = len(sample_resultset) - 1
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resultset_index = 0
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# Structures to test if we stored everything
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build_platforms_ref = set()
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machine_platforms_ref = set()
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machines_ref = set()
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options_ref = set()
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job_types_ref = set()
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products_ref = set()
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result_sets_ref = set()
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log_urls_ref = set()
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coalesced_job_guids = {}
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coalesced_replacements = []
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artifacts_ref = {}
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blobs = []
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for index, blob in enumerate(job_data):
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if resultset_index > max_index:
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resultset_index = 0
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# Modify job structure to sync with the resultset sample data
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if 'sources' in blob:
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del blob['sources']
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blob['revision_hash'] = sample_resultset[resultset_index]['revision_hash']
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blobs.append(blob)
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resultset_index += 1
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# Build data structures to confirm everything is stored
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# as expected
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if verify_data:
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job_guid = blob['job']['job_guid']
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job = blob['job']
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build_platforms_ref.add(
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RefDataManager.get_platform_key(
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job.get('build_platform', {}).get('os_name', 'unkown'),
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job.get('build_platform', {}).get('platform', 'unkown'),
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job.get('build_platform', {}).get('architecture', 'unknown')
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))
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machine_platforms_ref.add(
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RefDataManager.get_platform_key(
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job.get('machine_platform', {}).get('os_name', 'unkown'),
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job.get('machine_platform', {}).get('platform', 'unkown'),
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job.get('machine_platform', {}).get('architecture', 'unknown')
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))
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machines_ref.add(job.get('machine', 'unknown'))
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options_ref = options_ref.union(job.get('option_collection', []).keys())
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job_types_ref.add(job.get('name', 'unknown'))
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products_ref.add(job.get('product_name', 'unknown'))
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result_sets_ref.add(blob['revision_hash'])
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log_url_list = job.get('log_references', [])
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for log_data in log_url_list:
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log_urls_ref.add(log_data['url'])
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artifact_name = job.get('artifact', {}).get('name')
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if artifact_name:
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artifacts_ref[artifact_name] = job.get('artifact')
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coalesced = blob.get('coalesced', [])
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if coalesced:
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coalesced_job_guids[job_guid] = coalesced
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coalesced_replacements.append('%s')
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# Store the modified json blobs
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jm.store_job_data(blobs)
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if verify_data:
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# Confirms stored data matches whats in the reference data structs
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verify_build_platforms(refdata, build_platforms_ref)
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verify_machine_platforms(refdata, machine_platforms_ref)
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verify_machines(refdata, machines_ref)
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verify_options(refdata, options_ref)
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verify_job_types(refdata, job_types_ref)
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verify_products(refdata, products_ref)
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verify_result_sets(jm, result_sets_ref)
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verify_log_urls(jm, log_urls_ref)
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verify_artifacts(jm, artifacts_ref)
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verify_coalesced(jm, coalesced_job_guids, coalesced_replacements)
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def verify_build_platforms(refdata, build_platforms_ref):
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build_platforms = refdata.dhub.execute(
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proc='refdata_test.selects.test_all_build_platforms',
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)
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build_platforms_set = set()
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for build_platform in build_platforms:
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build_platforms_set.add(
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RefDataManager.get_platform_key(
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build_platform.get('os_name'),
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build_platform.get('platform'),
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build_platform.get('architecture')
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))
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assert build_platforms_ref.issubset(build_platforms_set)
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def verify_machine_platforms(refdata, machine_platforms_ref):
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machine_platforms = refdata.dhub.execute(
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proc='refdata_test.selects.test_all_machine_platforms',
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)
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machine_platforms_set = set()
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for machine_platform in machine_platforms:
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machine_platforms_set.add(
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RefDataManager.get_platform_key(
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machine_platform.get('os_name'),
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machine_platform.get('platform'),
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machine_platform.get('architecture')
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))
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assert machine_platforms_ref.issubset(machine_platforms_set)
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def verify_machines(refdata, machines_ref):
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machines = refdata.dhub.execute(
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proc='refdata_test.selects.test_all_machines',
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key_column='name',
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return_type='set'
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)
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assert machines_ref.issubset(machines)
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def verify_options(refdata, options_ref):
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options = refdata.dhub.execute(
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proc='refdata_test.selects.test_all_options',
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key_column='name',
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return_type='set'
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)
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assert options_ref.issubset(options)
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def verify_job_types(refdata, job_types_ref):
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job_types = refdata.dhub.execute(
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proc='refdata_test.selects.test_all_job_types',
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key_column='name',
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return_type='set'
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)
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assert job_types_ref.issubset(job_types)
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def verify_products(refdata, products_ref):
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products = refdata.dhub.execute(
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proc='refdata_test.selects.test_all_products',
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key_column='name',
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return_type='set'
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)
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assert products_ref.issubset(products)
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def verify_result_sets(jm, result_sets_ref):
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revision_hashes = jm.get_dhub().execute(
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proc='jobs.selects.get_all_result_set_revision_hashes',
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key_column='revision_hash',
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return_type='set'
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)
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assert result_sets_ref.issubset(revision_hashes)
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def verify_log_urls(jm, log_urls_ref):
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log_urls = jm.get_dhub().execute(
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proc='jobs.selects.get_all_log_urls',
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key_column='url',
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return_type='set'
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)
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assert log_urls_ref.issubset(log_urls)
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def verify_artifacts(jm, artifacts_ref):
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artifacts = jm.get_dhub().execute(
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proc='jobs.selects.get_all_artifacts',
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key_column='name',
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return_type='dict'
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)
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for key in artifacts.keys():
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assert artifacts[key]['name'] == artifacts_ref[key]['name']
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assert artifacts[key]['type'] == artifacts_ref[key]['type']
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assert json.loads(artifacts[key]['blob']) == artifacts_ref[key]['blob']
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def verify_coalesced(jm, coalesced_job_guids, coalesced_replacements):
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coalesced_job_guid_list = coalesced_job_guids.keys()
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if coalesced_job_guid_list:
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rep_str = ','.join(coalesced_replacements)
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data = jm.get_dhub().execute(
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proc='jobs.selects.get_jobs_by_coalesced_guids',
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replace=[rep_str],
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placeholders=coalesced_job_guid_list
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)
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coalesced_job_guids_stored = {}
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for datum in data:
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if datum['job_coalesced_to_guid'] not in coalesced_job_guids_stored:
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coalesced_job_guids_stored[datum['job_coalesced_to_guid']] = []
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coalesced_job_guids_stored[datum['job_coalesced_to_guid']].append(
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datum['job_guid']
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)
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assert coalesced_job_guids_stored == coalesced_job_guids
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def load_exp(filename):
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"""
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Load in an expected result json and return as an obj.
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If the file doesn't exist, it will be created, but the test will
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fail, due to no content. This is to make it easier during test
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development.
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"""
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path = SampleData().get_log_path(filename)
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with open(path, "a+") as f:
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try:
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return json.loads(f.read())
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except ValueError:
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# if it's not parse-able, return an empty dict
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return {}
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def unicode_keys(d):
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return dict([(unicode(k), v) for k, v in d.items()])
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def clean_source_blob_dict(src):
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"""Fix a few fields so they're easier to compare"""
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src["push_timestamp"] = long(src["push_timestamp"])
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return src
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def clean_job_blob_dict(job):
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"""Fix a few fields so they're easier to compare"""
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job["start_timestamp"] = long(job["start_timestamp"])
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job["submit_timestamp"] = long(job["submit_timestamp"])
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job["end_timestamp"] = long(job["end_timestamp"])
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job["result"] = unicode(job["result"])
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# move artifact logs to log_references area for comparison
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try:
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artlog = job["artifact"]["log_urls"]
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job["log_references"].extend(artlog)
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del(job["artifact"]["log_urls"])
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except KeyError:
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pass # no problem
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return job
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