def jobsfs_to_mongo(guid, buid, name):
"""Composed method for resopnding to a guid update."""
assert re.match(r'^[0-9A-F]{8}-[0-9A-F]{4}-[0-9A-F]{4}-[0-9A-F]{4}-[0-9A-F]{12}$', guid.upper()), \
"%s is not a valid guid" % guid
assert re.match(r'^\d+$', str(buid)), "%s is not a valid buid" % buid
logger.info("Updating Job Source %s", guid)
# Make the BusinessUnit and Company
create_businessunit(buid)
bu = BusinessUnit.objects.get(id=buid)
bu.title = name
bu.save()
add_company(bu)
# Lookup the jobs, filter then, transform them, and then load the jobs
zf = get_jobsfs_zipfile(guid)
jobs = get_jobs_from_zipfile(zf, guid)
jobs = filter_current_jobs(jobs, bu)
jobs = (hr_xml_to_json(job, bu) for job in jobs)
jobs = list(jobs)
for job in jobs:
job['guid'] = job['guid'].lower()
if len(jobs) > 0:
collection = connect_db().db.jobs
bulk = collection.initialize_unordered_bulk_op()
for job in jobs:
bulk.find({'guid': job['guid']}).upsert().replace_one(job)
bulk.execute()
def jobsfs_to_mongo(guid, buid, name):
"""Composed method for resopnding to a guid update."""
assert re.match(r'^[0-9A-F]{8}-[0-9A-F]{4}-[0-9A-F]{4}-[0-9A-F]{4}-[0-9A-F]{12}$', guid.upper()), \
"%s is not a valid guid" % guid
assert re.match(r'^\d+$', str(buid)), "%s is not a valid buid" % buid
logger.info("Updating Job Source %s", guid)
# Make the BusinessUnit and Company
create_businessunit(buid)
bu = BusinessUnit.objects.get(id=buid)
bu.title = name
bu.save()
add_company(bu)
# Lookup the jobs, filter then, transform them, and then load the jobs
zf = get_jobsfs_zipfile(guid)
jobs = get_jobs_from_zipfile(zf, guid)
jobs = filter_current_jobs(jobs, bu)
jobs = (hr_xml_to_json(job, bu) for job in jobs)
jobs = list(jobs)
for job in jobs:
job['guid'] = job['guid'].lower()
if len(jobs) > 0:
collection = connect_db().db.jobs
bulk = collection.initialize_unordered_bulk_op()
for job in jobs:
bulk.find({'guid': job['guid']}).upsert().replace_one(job)
bulk.execute()
def get_mongo_db():
"""
Retrieve the current mongo database (defined in settings.MONGO_DBNAME).
:return: a mongo database
"""
return connect_db().db
def populate_mongo_data(self):
job_views = connect_db().db.job_views
mongo_data = [
{
"time_first_viewed": dateparser.parse("10/17/2012 01:00:00"),
"country": "GER",
"state": "Gutentag",
"city": "Braunshweiger",
"found_on": "www.google.de",
"view_count": 3,
},
{
"time_first_viewed": dateparser.parse("10/18/2012 01:00:00"),
"country": "GER",
"state": "Gutentag",
"city": "Braunshweiger",
"found_on": "www.google.de",
"view_count": 3,
},
{
"time_first_viewed": dateparser.parse("10/18/2012 01:00:00"),
"country": "USA",
"state": "IN",
"city": "Peru",
"found_on": "www.google.com",
"view_count": 2,
},
{
"time_first_viewed": dateparser.parse("10/18/2012 01:00:00"),
"country": "USA",
"state": "IN",
"city": "Indianapolis",
"found_on": "www.google.com",
"view_count": 7,
},
{
"time_first_viewed": dateparser.parse("10/18/2012 01:00:00"),
"country": "USA",
"state": "MI",
"city": "Muskegon",
"found_on": "www.google.com",
"view_count": 1,
},
]
job_views.insert_many(mongo_data)
def seoxml_to_mongo(buid, data_dir=DATA_DIR):
filepath = download_feed_file(buid, data_dir=data_dir)
jobfeed = DEv2JobFeed(filepath, jsid=buid, markdown=False, company=None)
# If the feed file did not pass validation, return. The return value is
# '(0, 0)' to match what's returned on a successful parse.
if jobfeed.errors:
error = jobfeed.error_messages
logging.error("BUID:%s - Feed file has failed validation on line %s. "
"Exception: %s" %
(buid, error['line'], error['exception']))
raise FeedImportError(error)
# A dictionary of uids
jobfeed.jobparse()
jobs = jobfeed.solr_jobs()
collection = connect_db().db.jobs
bulk = collection.initialize_unordered_bulk_op()
for job in jobs:
bulk.find({'guid': job['guid']}).upsert().replace_one(job)
bulk.execute()
def seoxml_to_mongo(buid, data_dir=DATA_DIR):
filepath = download_feed_file(buid, data_dir=data_dir)
jobfeed = DEv2JobFeed(filepath, jsid=buid, markdown=False,
company=None)
# If the feed file did not pass validation, return. The return value is
# '(0, 0)' to match what's returned on a successful parse.
if jobfeed.errors:
error = jobfeed.error_messages
logging.error("BUID:%s - Feed file has failed validation on line %s. "
"Exception: %s" % (buid, error['line'],
error['exception']))
raise FeedImportError(error)
# A dictionary of uids
jobfeed.jobparse()
jobs = jobfeed.solr_jobs()
collection = connect_db().db.jobs
bulk = collection.initialize_unordered_bulk_op()
for job in jobs:
bulk.find({'guid': job['guid']}).upsert().replace_one(job)
bulk.execute()
def to_mongo(file_name):
"""
Inserts the constituent lines of the file pointed to by "file_name" into
a Mongo database.
"""
analytics = connect_db().db
base_name = os.path.basename(file_name)
# If you're comparing this with store.py from MyJobs-Analytics, you'll
# notice that we're using file_name here instead of base_name. Analytics
# log files are stored on the analytics servers themselves, while redirect
# stores its logs in s3. This is one way to differentiate the two.
count = analytics.files.find_one({"file": file_name}) or 0
if count:
# This file was already processed.
# TODO: Add ability to upsert based on this?
return
lines = get_log_lines(file_name)
# All redirect logs are named using the same format, which differs from
# analytics logs - "ex%y%m%d%H%M%S.log"; strip "ex" and the extension to
# get our timestamp.
timestamp = os.path.splitext(base_name)[0][2:]
file_created = datetime.strptime(timestamp, "%y%m%d%H%M%S")
file_id = analytics.files.insert_one({
"file": file_name,
"line_count": len(lines),
"file_created": file_created
}).inserted_id
json_lines = []
# Denotes if the file was inserted successfully in its entirety. Toggled if
# we can't parse a line as JSON.
success = True
for line in lines:
# Try to parse each line as JSON. There may or may not be invalid data
# in the file; Don't crash and burn if so.
try:
json_line = json.loads(line)
except ValueError:
success = False
else:
# '"-"' is valid JSON but insert_many requires a list of
# dictionaries. If "json_line" is a string, it's not a
# document we wish to keep.
if not isinstance(json_line, basestring):
for key, value in json_line.items():
if key in ['to', 'sv', 'nv', 'fv'] and value:
# parser.parse('') results in today's date; we probably
# don't want that. Ensure the parameter has a value.
try:
json_line[key] = parser.parse(value)
except (ValueError, TypeError):
pass
elif isinstance(value, basestring) and value.isdigit():
json_line[key] = int(value)
if key == 'time':
json_line[key] = datetime.fromtimestamp(
json_line[key])
json_line['file_id'] = file_id
json_lines.append(json_line)
if json_lines:
# It's possible the file is blank or we failed parsing all lines.
# TODO: Auditing procedure that compares "line_count" from the "files"
# collection with the number of items related to that file in the
# "analytics" collection.
analytics.analytics.insert_many(json_lines)
analytics.files.update({'_id': file_id}, {'$set': {'success': success}})
def get_mongo_db():
"""
Retrieve the current mongo database (defined in settings.MONGO_DBNAME).
:return: a mongo database
"""
return connect_db().db
def to_mongo(file_name):
"""
Inserts the constituent lines of the file pointed to by "file_name" into
a Mongo database.
"""
analytics = connect_db().db
base_name = os.path.basename(file_name)
# If you're comparing this with store.py from MyJobs-Analytics, you'll
# notice that we're using file_name here instead of base_name. Analytics
# log files are stored on the analytics servers themselves, while redirect
# stores its logs in s3. This is one way to differentiate the two.
count = analytics.files.find_one({"file": file_name}) or 0
if count:
# This file was already processed.
# TODO: Add ability to upsert based on this?
return
lines = get_log_lines(file_name)
# All redirect logs are named using the same format, which differs from
# analytics logs - "ex%y%m%d%H%M%S.log"; strip "ex" and the extension to
# get our timestamp.
timestamp = os.path.splitext(base_name)[0][2:]
file_created = datetime.strptime(timestamp, "%y%m%d%H%M%S")
file_id = analytics.files.insert_one({
"file": file_name, "line_count": len(lines),
"file_created": file_created}).inserted_id
json_lines = []
# Denotes if the file was inserted successfully in its entirety. Toggled if
# we can't parse a line as JSON.
success = True
for line in lines:
# Try to parse each line as JSON. There may or may not be invalid data
# in the file; Don't crash and burn if so.
try:
json_line = json.loads(line)
except ValueError:
success = False
else:
# '"-"' is valid JSON but insert_many requires a list of
# dictionaries. If "json_line" is a string, it's not a
# document we wish to keep.
if not isinstance(json_line, basestring):
for key, value in json_line.items():
if key in ['to', 'sv', 'nv', 'fv'] and value:
# parser.parse('') results in today's date; we probably
# don't want that. Ensure the parameter has a value.
try:
json_line[key] = parser.parse(value)
except (ValueError, TypeError):
pass
elif isinstance(value, basestring) and value.isdigit():
json_line[key] = int(value)
if key == 'time':
json_line[key] = datetime.fromtimestamp(
json_line[key])
json_line['file_id'] = file_id
json_lines.append(json_line)
if json_lines:
# It's possible the file is blank or we failed parsing all lines.
# TODO: Auditing procedure that compares "line_count" from the "files"
# collection with the number of items related to that file in the
# "analytics" collection.
analytics.analytics.insert_many(json_lines)
analytics.files.update({'_id': file_id},
{'$set': {'success': success}})