def run_cross_validation(self, job):
""" Cross file validation job. Test all rules with matching rule_timing.
Run each cross-file rule and create error report.
Args:
job: Current job
"""
sess = GlobalDB.db().session
job_id = job.job_id
# Create File Status object
create_file_if_needed(job_id)
# Create list of errors
error_list = ErrorInterface()
submission_id = job.submission_id
job_start = datetime.now()
logger.info({
'message':
'Beginning cross-file validations on submission_id: ' +
str(submission_id),
'message_type':
'ValidatorInfo',
'submission_id':
submission_id,
'job_id':
job.job_id,
'action':
'run_cross_validations',
'start':
job_start,
'status':
'start'
})
# Delete existing cross file errors for this submission
sess.query(ErrorMetadata).filter(
ErrorMetadata.job_id == job_id).delete()
sess.commit()
# get all cross file rules from db
cross_file_rules = sess.query(RuleSql).filter_by(
rule_cross_file_flag=True)
# for each cross-file combo, run associated rules and create error report
for c in get_cross_file_pairs():
first_file = c[0]
second_file = c[1]
combo_rules = cross_file_rules.filter(
or_(
and_(RuleSql.file_id == first_file.id,
RuleSql.target_file_id == second_file.id),
and_(RuleSql.file_id == second_file.id,
RuleSql.target_file_id == first_file.id)))
# get error file name/path
error_file_name = report_file_name(submission_id, False,
first_file.name,
second_file.name)
error_file_path = "".join(
[CONFIG_SERVICES['error_report_path'], error_file_name])
warning_file_name = report_file_name(submission_id, True,
first_file.name,
second_file.name)
warning_file_path = "".join(
[CONFIG_SERVICES['error_report_path'], warning_file_name])
# open error report and gather failed rules within it
with open(error_file_path, 'w', newline='') as error_file,\
open(warning_file_path, 'w', newline='') as warning_file:
error_csv = csv.writer(error_file,
delimiter=',',
quoting=csv.QUOTE_MINIMAL,
lineterminator='\n')
warning_csv = csv.writer(warning_file,
delimiter=',',
quoting=csv.QUOTE_MINIMAL,
lineterminator='\n')
# write headers to file
error_csv.writerow(self.crossFileReportHeaders)
warning_csv.writerow(self.crossFileReportHeaders)
# send comboRules to validator.crossValidate sql
current_cols_short_to_long = self.short_to_long_dict[
first_file.id].copy()
current_cols_short_to_long.update(
self.short_to_long_dict[second_file.id].copy())
cross_validate_sql(combo_rules.all(), submission_id,
current_cols_short_to_long, first_file.id,
second_file.id, job, error_csv, warning_csv,
error_list, job_id)
# close files
error_file.close()
warning_file.close()
# stream file to S3 when not local
if not self.is_local:
# stream error file
with open(error_file_path, 'rb') as csv_file:
with smart_open.smart_open(
S3Handler.create_file_path(
self.get_file_name(error_file_name)),
'w') as writer:
while True:
chunk = csv_file.read(CHUNK_SIZE)
if chunk:
writer.write(chunk)
else:
break
csv_file.close()
os.remove(error_file_path)
# stream warning file
with open(warning_file_path, 'rb') as warning_csv_file:
with smart_open.smart_open(
S3Handler.create_file_path(
self.get_file_name(warning_file_name)),
'w') as warning_writer:
while True:
chunk = warning_csv_file.read(CHUNK_SIZE)
if chunk:
warning_writer.write(chunk)
else:
break
warning_csv_file.close()
os.remove(warning_file_path)
# write all recorded errors to database
error_list.write_all_row_errors(job_id)
# Update error info for submission
populate_job_error_info(job)
# mark job status as "finished"
mark_job_status(job_id, "finished")
job_duration = (datetime.now() - job_start).total_seconds()
logger.info({
'message':
'Completed cross-file validations on submission_id: ' +
str(submission_id),
'message_type':
'ValidatorInfo',
'submission_id':
submission_id,
'job_id':
job.job_id,
'action':
'run_cross_validations',
'status':
'finish',
'start':
job_start,
'duration':
job_duration
})
# set number of errors and warnings for submission.
submission = populate_submission_error_info(submission_id)
# TODO: Remove temporary step below
# Temporarily set publishable flag at end of cross file, remove this once users are able to mark their
# submissions as publishable
# Publish only if no errors are present
if submission.number_of_errors == 0:
submission.publishable = True
sess.commit()
# Mark validation complete
mark_file_complete(job_id)
def run_validation(self, job):
""" Run validations for specified job
Args:
job: Job to be validated
Returns:
True if successful
"""
sess = GlobalDB.db().session
error_list = ErrorInterface()
job_id = job.job_id
submission_id = job.submission_id
row_number = 1
file_type = job.file_type.name
validation_start = datetime.now()
log_str = 'on submission_id: {}, job_id: {}, file_type: {}'.format(
str(submission_id), str(job_id), file_type)
logger.info({
'message': 'Beginning run_validation {}'.format(log_str),
'message_type': 'ValidatorInfo',
'submission_id': submission_id,
'job_id': job_id,
'file_type': file_type,
'action': 'run_validations',
'status': 'start',
'start_time': validation_start
})
# Get orm model for this file
model = [ft.model for ft in FILE_TYPE if ft.name == file_type][0]
# Delete existing file level errors for this submission
sess.query(ErrorMetadata).filter(
ErrorMetadata.job_id == job_id).delete()
sess.commit()
# Clear existing records for this submission
sess.query(model).filter_by(submission_id=submission_id).delete()
sess.commit()
# Clear existing flex fields for this job
sess.query(FlexField).filter_by(job_id=job_id).delete()
sess.commit()
# If local, make the error report directory
if self.is_local and not os.path.exists(self.directory):
os.makedirs(self.directory)
# Get bucket name and file name
file_name = job.filename
bucket_name = CONFIG_BROKER['aws_bucket']
region_name = CONFIG_BROKER['aws_region']
error_file_name = report_file_name(job.submission_id, False,
job.file_type.name)
error_file_path = "".join(
[CONFIG_SERVICES['error_report_path'], error_file_name])
warning_file_name = report_file_name(job.submission_id, True,
job.file_type.name)
warning_file_path = "".join(
[CONFIG_SERVICES['error_report_path'], warning_file_name])
# Create File Status object
create_file_if_needed(job_id, file_name)
reader = CsvReader()
# Get file size and write to jobs table
if CONFIG_BROKER["use_aws"]:
file_size = S3Handler.get_file_size(file_name)
else:
file_size = os.path.getsize(file_name)
job.file_size = file_size
sess.commit()
# Get fields for this file
fields = sess.query(FileColumn).filter(
FileColumn.file_id == FILE_TYPE_DICT[file_type]).all()
for field in fields:
sess.expunge(field)
csv_schema = {row.name_short: row for row in fields}
try:
extension = os.path.splitext(file_name)[1]
if not extension or extension.lower() not in ['.csv', '.txt']:
raise ResponseException("", StatusCode.CLIENT_ERROR, None,
ValidationError.fileTypeError)
# Count file rows: throws a File Level Error for non-UTF8 characters
temp_file = open(reader.get_filename(region_name, bucket_name,
file_name),
encoding='utf-8')
file_row_count = len(list(csv.reader(temp_file)))
try:
temp_file.close()
except AttributeError:
# File does not exist, and so does not need to be closed
pass
# Pull file and return info on whether it's using short or long col headers
reader.open_file(region_name,
bucket_name,
file_name,
fields,
bucket_name,
self.get_file_name(error_file_name),
self.long_to_short_dict[job.file_type_id],
is_local=self.is_local)
# list to keep track of rows that fail validations
error_rows = []
# While not done, pull one row and put it into staging table if it passes
# the Validator
loading_start = datetime.now()
logger.info({
'message': 'Beginning data loading {}'.format(log_str),
'message_type': 'ValidatorInfo',
'submission_id': submission_id,
'job_id': job_id,
'file_type': file_type,
'action': 'data_loading',
'status': 'start',
'start_time': loading_start
})
with open(error_file_path, 'w', newline='') as error_file,\
open(warning_file_path, 'w', newline='') as warning_file:
error_csv = csv.writer(error_file,
delimiter=',',
quoting=csv.QUOTE_MINIMAL,
lineterminator='\n')
warning_csv = csv.writer(warning_file,
delimiter=',',
quoting=csv.QUOTE_MINIMAL,
lineterminator='\n')
required_list = None
type_list = None
if file_type == "fabs":
# create a list of all required/type labels for FABS
labels = sess.query(ValidationLabel).all()
required_list = {}
type_list = {}
for label in labels:
if label.label_type == "requirement":
required_list[label.column_name] = label.label
else:
type_list[label.column_name] = label.label
# write headers to file
error_csv.writerow(self.reportHeaders)
warning_csv.writerow(self.reportHeaders)
while not reader.is_finished:
row_number += 1
if row_number % 100 == 0:
elapsed_time = (datetime.now() -
loading_start).total_seconds()
logger.info({
'message':
'Loading row: {} {}'.format(
str(row_number), log_str),
'message_type':
'ValidatorInfo',
'submission_id':
submission_id,
'job_id':
job_id,
'file_type':
file_type,
'action':
'data_loading',
'status':
'loading',
'rows_loaded':
row_number,
'start_time':
loading_start,
'elapsed_time':
elapsed_time
})
#
# first phase of validations: read record and record a
# formatting error if there's a problem
#
(record, reduceRow, skip_row, doneReading, rowErrorHere, flex_cols) = \
self.read_record(reader, error_csv, row_number, job, fields, error_list)
if reduceRow:
row_number -= 1
if rowErrorHere:
error_rows.append(row_number)
if doneReading:
# Stop reading from input file
break
elif skip_row:
# Do not write this row to staging, but continue processing future rows
continue
#
# second phase of validations: do basic schema checks
# (e.g., require fields, field length, data type)
#
# D files are obtained from upstream systems (ASP and FPDS) that perform their own basic
# validations, so these validations are not repeated here
if file_type in ["award", "award_procurement"]:
# Skip basic validations for D files, set as valid to trigger write to staging
passed_validations = True
valid = True
else:
if file_type == "fabs":
record['afa_generated_unique'] = (record['award_modification_amendme'] or '-none-') + "_" +\
(record['awarding_sub_tier_agency_c'] or '-none-') + \
"_" + (record['fain'] or '-none-') + "_" + \
(record['uri'] or '-none-')
passed_validations, failures, valid = Validator.validate(
record, csv_schema, file_type == "fabs",
required_list, type_list)
if valid:
# todo: update this logic later when we have actual validations
if file_type == "fabs":
record["is_valid"] = True
model_instance = model(job_id=job_id,
submission_id=submission_id,
valid_record=passed_validations,
**record)
skip_row = not insert_staging_model(
model_instance, job, error_csv, error_list)
if flex_cols:
sess.add_all(flex_cols)
sess.commit()
if skip_row:
error_rows.append(row_number)
continue
if not passed_validations:
fatal = write_errors(
failures, job,
self.short_to_long_dict[job.file_type_id],
error_csv, warning_csv, row_number, error_list,
flex_cols)
if fatal:
error_rows.append(row_number)
loading_duration = (datetime.now() -
loading_start).total_seconds()
logger.info({
'message':
'Completed data loading {}'.format(log_str),
'message_type':
'ValidatorInfo',
'submission_id':
submission_id,
'job_id':
job_id,
'file_type':
file_type,
'action':
'data_loading',
'status':
'finish',
'start_time':
loading_start,
'end_time':
datetime.now(),
'duration':
loading_duration,
'total_rows':
row_number
})
if file_type in ('appropriations', 'program_activity',
'award_financial'):
update_tas_ids(model, submission_id)
#
# third phase of validations: run validation rules as specified
# in the schema guidance. these validations are sql-based.
#
sql_error_rows = self.run_sql_validations(
job, file_type, self.short_to_long_dict[job.file_type_id],
error_csv, warning_csv, row_number, error_list)
error_rows.extend(sql_error_rows)
error_file.close()
warning_file.close()
# stream file to S3 when not local
if not self.is_local:
# stream error file
with open(error_file_path, 'rb') as csv_file:
with smart_open.smart_open(S3Handler.create_file_path(self.get_file_name(error_file_name)), 'w')\
as writer:
while True:
chunk = csv_file.read(CHUNK_SIZE)
if chunk:
writer.write(chunk)
else:
break
csv_file.close()
os.remove(error_file_path)
# stream warning file
with open(warning_file_path, 'rb') as warning_csv_file:
with smart_open.smart_open(S3Handler.create_file_path(self.get_file_name(warning_file_name)), 'w')\
as warning_writer:
while True:
chunk = warning_csv_file.read(CHUNK_SIZE)
if chunk:
warning_writer.write(chunk)
else:
break
warning_csv_file.close()
os.remove(warning_file_path)
# Calculate total number of rows in file
# that passed validations
error_rows_unique = set(error_rows)
total_rows_excluding_header = row_number - 1
valid_rows = total_rows_excluding_header - len(error_rows_unique)
# Update fabs is_valid rows where applicable
# Update submission to include action dates where applicable
if file_type == "fabs":
sess.query(DetachedAwardFinancialAssistance).\
filter(DetachedAwardFinancialAssistance.row_number.in_(error_rows_unique),
DetachedAwardFinancialAssistance.submission_id == submission_id).\
update({"is_valid": False}, synchronize_session=False)
sess.commit()
min_action_date, max_action_date = get_action_dates(
submission_id)
sess.query(Submission).filter(Submission.submission_id == submission_id).\
update({"reporting_start_date": min_action_date, "reporting_end_date": max_action_date},
synchronize_session=False)
# Ensure validated rows match initial row count
if file_row_count != row_number:
raise ResponseException("", StatusCode.CLIENT_ERROR, None,
ValidationError.rowCountError)
# Update job metadata
job.number_of_rows = row_number
job.number_of_rows_valid = valid_rows
sess.commit()
error_list.write_all_row_errors(job_id)
# Update error info for submission
populate_job_error_info(job)
if file_type == "fabs":
# set number of errors and warnings for detached submission
populate_submission_error_info(submission_id)
# Mark validation as finished in job tracker
mark_job_status(job_id, "finished")
mark_file_complete(job_id, file_name)
finally:
# Ensure the files always close
reader.close()
validation_duration = (datetime.now() -
validation_start).total_seconds()
logger.info({
'message':
'Completed run_validation {}'.format(log_str),
'message_type':
'ValidatorInfo',
'submission_id':
submission_id,
'job_id':
job_id,
'file_type':
file_type,
'action':
'run_validation',
'status':
'finish',
'start_time':
validation_start,
'end_time':
datetime.now(),
'duration':
validation_duration
})
return True
def run_validation(self, job):
""" Run validations for specified job
Args:
job: Job to be validated
Returns:
True if successful
"""
sess = GlobalDB.db().session
job_id = job.job_id
error_list = ErrorInterface()
submission_id = job.submission_id
row_number = 1
file_type = job.file_type.name
validation_start = datetime.now()
logger.info(
{
'message': 'Beginning run_validation on submission_id: ' + str(submission_id) +
', job_id: ' + str(job_id) + ', file_type: ' + file_type,
'message_type': 'ValidatorInfo',
'submission_id': submission_id,
'job_id': job_id,
'file_type': file_type,
'action': 'run_validations',
'status': 'start',
'start_time': validation_start})
# Get orm model for this file
model = [ft.model for ft in FILE_TYPE if ft.name == file_type][0]
# Delete existing file level errors for this submission
sess.query(ErrorMetadata).filter(ErrorMetadata.job_id == job_id).delete()
sess.commit()
# Clear existing records for this submission
sess.query(model).filter_by(submission_id=submission_id).delete()
sess.commit()
# Clear existing flex fields for this job
sess.query(FlexField).filter_by(job_id=job_id).delete()
sess.commit()
# If local, make the error report directory
if self.isLocal and not os.path.exists(self.directory):
os.makedirs(self.directory)
# Get bucket name and file name
file_name = job.filename
bucket_name = CONFIG_BROKER['aws_bucket']
region_name = CONFIG_BROKER['aws_region']
error_file_name = self.get_file_name(report_file_name(job.submission_id, False, job.file_type.name))
warning_file_name = self.get_file_name(report_file_name(job.submission_id, True, job.file_type.name))
# Create File Status object
create_file_if_needed(job_id, file_name)
reader = self.get_reader()
# Get file size and write to jobs table
if CONFIG_BROKER["use_aws"]:
file_size = S3Handler.get_file_size(file_name)
else:
file_size = os.path.getsize(file_name)
job.file_size = file_size
sess.commit()
# Get fields for this file
fields = sess.query(FileColumn).filter(FileColumn.file_id == FILE_TYPE_DICT[file_type]).all()
for field in fields:
sess.expunge(field)
csv_schema = {row.name_short: row for row in fields}
try:
extension = os.path.splitext(file_name)[1]
if not extension or extension not in ['.csv', '.txt']:
raise ResponseException("", StatusCode.CLIENT_ERROR, None, ValidationError.fileTypeError)
# Count file rows: throws a File Level Error for non-UTF8 characters
temp_file = open(reader.get_filename(region_name, bucket_name, file_name), encoding='utf-8')
file_row_count = len(list(csv.reader(temp_file)))
try:
temp_file.close()
except AttributeError:
# File does not exist, and so does not need to be closed
pass
# Pull file and return info on whether it's using short or long col headers
reader.open_file(region_name, bucket_name, file_name, fields, bucket_name, error_file_name,
self.long_to_short_dict, is_local=self.isLocal)
# list to keep track of rows that fail validations
error_rows = []
# While not done, pull one row and put it into staging table if it passes
# the Validator
loading_start = datetime.now()
logger.info(
{
'message': 'Beginning data loading on submission_id: ' + str(submission_id) +
', job_id: ' + str(job_id) + ', file_type: ' + file_type,
'message_type': 'ValidatorInfo',
'submission_id': submission_id,
'job_id': job_id,
'file_type': file_type,
'action': 'data_loading',
'status': 'start',
'start_time': loading_start})
with self.get_writer(region_name, bucket_name, error_file_name, self.reportHeaders) as writer, \
self.get_writer(region_name, bucket_name, warning_file_name, self.reportHeaders) as warning_writer:
while not reader.is_finished:
row_number += 1
if row_number % 100 == 0:
elapsed_time = (datetime.now()-loading_start).total_seconds()
logger.info(
{
'message': 'Loading row: ' + str(row_number) + ' on submission_id: ' +
str(submission_id) + ', job_id: ' + str(job_id) + ', file_type: ' + file_type,
'message_type': 'ValidatorInfo',
'submission_id': submission_id,
'job_id': job_id,
'file_type': file_type,
'action': 'data_loading',
'status': 'loading',
'rows_loaded': row_number,
'start_time': loading_start,
'elapsed_time': elapsed_time})
#
# first phase of validations: read record and record a
# formatting error if there's a problem
#
(record, reduceRow, skip_row, doneReading, rowErrorHere, flex_cols) = \
self.read_record(reader, writer, row_number, job, fields, error_list)
if reduceRow:
row_number -= 1
if rowErrorHere:
error_rows.append(row_number)
if doneReading:
# Stop reading from input file
break
elif skip_row:
# Do not write this row to staging, but continue processing future rows
continue
#
# second phase of validations: do basic schema checks
# (e.g., require fields, field length, data type)
#
# D files are obtained from upstream systems (ASP and FPDS) that perform their own basic
# validations, so these validations are not repeated here
if file_type in ["award", "award_procurement"]:
# Skip basic validations for D files, set as valid to trigger write to staging
passed_validations = True
valid = True
else:
if file_type in ["detached_award"]:
record['afa_generated_unique'] = (record['award_modification_amendme'] or '-none-') + \
(record['awarding_sub_tier_agency_c'] or '-none-') + \
(record['fain'] or '-none-') + (record['uri'] or '-none-')
passed_validations, failures, valid = Validator.validate(record, csv_schema,
file_type in ["detached_award"])
if valid:
# todo: update this logic later when we have actual validations
if file_type in ["detached_award"]:
record["is_valid"] = True
model_instance = model(job_id=job_id, submission_id=submission_id,
valid_record=passed_validations, **record)
skip_row = not insert_staging_model(model_instance, job, writer, error_list)
if flex_cols:
sess.add_all(flex_cols)
sess.commit()
if skip_row:
error_rows.append(row_number)
continue
if not passed_validations:
fatal = write_errors(failures, job, self.short_to_long_dict, writer, warning_writer,
row_number, error_list)
if fatal:
error_rows.append(row_number)
loading_duration = (datetime.now()-loading_start).total_seconds()
logger.info(
{
'message': 'Completed data loading on submission_id: ' + str(submission_id) +
', job_id: ' + str(job_id) + ', file_type: ' + file_type,
'message_type': 'ValidatorInfo',
'submission_id': submission_id,
'job_id': job_id,
'file_type': file_type,
'action': 'data_loading',
'status': 'finish',
'start_time': loading_start,
'end_time': datetime.now(),
'duration': loading_duration,
'total_rows': row_number
})
if file_type in ('appropriations', 'program_activity', 'award_financial'):
update_tas_ids(model, submission_id)
#
# third phase of validations: run validation rules as specified
# in the schema guidance. these validations are sql-based.
#
sql_error_rows = self.run_sql_validations(job, file_type, self.short_to_long_dict, writer,
warning_writer, row_number, error_list)
error_rows.extend(sql_error_rows)
# Write unfinished batch
writer.finish_batch()
warning_writer.finish_batch()
# Calculate total number of rows in file
# that passed validations
error_rows_unique = set(error_rows)
total_rows_excluding_header = row_number - 1
valid_rows = total_rows_excluding_header - len(error_rows_unique)
# Update detached_award is_valid rows where applicable
# Update submission to include action dates where applicable
if file_type in ["detached_award"]:
sess.query(DetachedAwardFinancialAssistance).\
filter(DetachedAwardFinancialAssistance.row_number.in_(error_rows_unique),
DetachedAwardFinancialAssistance.submission_id == submission_id).\
update({"is_valid": False}, synchronize_session=False)
sess.commit()
min_action_date, max_action_date = get_action_dates(submission_id)
sess.query(Submission).filter(Submission.submission_id == submission_id).\
update({"reporting_start_date": min_action_date, "reporting_end_date": max_action_date},
synchronize_session=False)
# Ensure validated rows match initial row count
if file_row_count != row_number:
raise ResponseException("", StatusCode.CLIENT_ERROR, None, ValidationError.rowCountError)
# Update job metadata
job.number_of_rows = row_number
job.number_of_rows_valid = valid_rows
sess.commit()
error_list.write_all_row_errors(job_id)
# Update error info for submission
populate_job_error_info(job)
if file_type in ["detached_award"]:
# set number of errors and warnings for detached submission
populate_submission_error_info(submission_id)
# Mark validation as finished in job tracker
mark_job_status(job_id, "finished")
mark_file_complete(job_id, file_name)
finally:
# Ensure the file always closes
reader.close()
validation_duration = (datetime.now()-validation_start).total_seconds()
logger.info(
{
'message': 'Completed run_validation on submission_id: ' + str(submission_id) +
', job_id: ' + str(job_id) + ', file_type: ' + file_type,
'message_type': 'ValidatorInfo',
'submission_id': submission_id,
'job_id': job_id,
'file_type': file_type,
'action': 'run_validation',
'status': 'finish',
'start_time': validation_start,
'end_time': datetime.now(),
'duration': validation_duration
})
return True
def run_cross_validation(self, job):
""" Cross file validation job. Test all rules with matching rule_timing.
Run each cross-file rule and create error report.
Args:
job: Current job
"""
sess = GlobalDB.db().session
job_id = job.job_id
# Create File Status object
create_file_if_needed(job_id)
# Create list of errors
error_list = ErrorInterface()
submission_id = job.submission_id
bucket_name = CONFIG_BROKER['aws_bucket']
region_name = CONFIG_BROKER['aws_region']
job_start = datetime.now()
logger.info(
{
'message': 'Beginning cross-file validations on submission_id: ' + str(submission_id),
'message_type': 'ValidatorInfo',
'submission_id': submission_id,
'job_id': job.job_id,
'action': 'run_cross_validations',
'start': job_start,
'status': 'start'})
# Delete existing cross file errors for this submission
sess.query(ErrorMetadata).filter(ErrorMetadata.job_id == job_id).delete()
sess.commit()
# get all cross file rules from db
cross_file_rules = sess.query(RuleSql).filter_by(rule_cross_file_flag=True)
# for each cross-file combo, run associated rules and create error report
for c in get_cross_file_pairs():
first_file = c[0]
second_file = c[1]
combo_rules = cross_file_rules.filter(or_(and_(
RuleSql.file_id == first_file.id,
RuleSql.target_file_id == second_file.id), and_(
RuleSql.file_id == second_file.id,
RuleSql.target_file_id == first_file.id)))
# send comboRules to validator.crossValidate sql
failures = cross_validate_sql(combo_rules.all(), submission_id, self.short_to_long_dict, first_file.id,
second_file.id, job)
# get error file name
report_filename = self.get_file_name(report_file_name(submission_id, False, first_file.name,
second_file.name))
warning_report_filename = self.get_file_name(report_file_name(submission_id, True, first_file.name,
second_file.name))
# loop through failures to create the error report
with self.get_writer(region_name, bucket_name, report_filename, self.crossFileReportHeaders) as writer, \
self.get_writer(region_name, bucket_name, warning_report_filename, self.crossFileReportHeaders) as \
warning_writer:
for failure in failures:
if failure[9] == RULE_SEVERITY_DICT['fatal']:
writer.write(failure[0:7])
if failure[9] == RULE_SEVERITY_DICT['warning']:
warning_writer.write(failure[0:7])
error_list.record_row_error(job_id, "cross_file",
failure[0], failure[3], failure[5], failure[6],
failure[7], failure[8], severity_id=failure[9])
# write the last unfinished batch
writer.finish_batch()
warning_writer.finish_batch()
# write all recorded errors to database
error_list.write_all_row_errors(job_id)
# Update error info for submission
populate_job_error_info(job)
# mark job status as "finished"
mark_job_status(job_id, "finished")
job_duration = (datetime.now()-job_start).total_seconds()
logger.info(
{
'message': 'Completed cross-file validations on submission_id: ' + str(submission_id),
'message_type': 'ValidatorInfo',
'submission_id': submission_id,
'job_id': job.job_id,
'action': 'run_cross_validations',
'status': 'finish',
'start': job_start,
'duration': job_duration})
# set number of errors and warnings for submission.
submission = populate_submission_error_info(submission_id)
# TODO: Remove temporary step below
# Temporarily set publishable flag at end of cross file, remove this once users are able to mark their
# submissions as publishable
# Publish only if no errors are present
if submission.number_of_errors == 0:
submission.publishable = True
sess.commit()
# Mark validation complete
mark_file_complete(job_id)
def load_file_data(self, sess, bucket_name, region_name):
""" Loads in the file data and performs initial validations
Args:
sess: the database connection
bucket_name: the bucket to pull the file
region_name: the region to pull the file
"""
loading_start = datetime.now()
logger.info({
'message': 'Beginning data loading {}'.format(self.log_str),
'message_type': 'ValidatorInfo',
'submission_id': self.submission_id,
'job_id': self.job.job_id,
'file_type': self.file_type.name,
'action': 'data_loading',
'status': 'start',
'start_time': loading_start
})
# Extension Check
extension = os.path.splitext(self.file_name)[1]
if not extension or extension.lower() not in ['.csv', '.txt']:
raise ResponseException('', StatusCode.CLIENT_ERROR, None, ValidationError.fileTypeError)
# Base file check
file_row_count, self.short_rows, self.long_rows = simple_file_scan(self.reader, bucket_name, region_name,
self.file_name)
# total_rows = header + long_rows (and will be added on per chunk)
# Note: we're adding long_rows here because pandas will exclude long_rows when we're loading the data
self.total_rows = 1 + len(self.long_rows)
# Making base error/warning files
self.error_file_name = report_file_name(self.submission_id, False, self.file_type.name)
self.error_file_path = ''.join([CONFIG_SERVICES['error_report_path'], self.error_file_name])
self.warning_file_name = report_file_name(self.submission_id, True, self.file_type.name)
self.warning_file_path = ''.join([CONFIG_SERVICES['error_report_path'], self.warning_file_name])
with open(self.error_file_path, 'w', newline='') as error_file, \
open(self.warning_file_path, 'w', newline='') as warning_file:
error_csv = csv.writer(error_file, delimiter=',', quoting=csv.QUOTE_MINIMAL, lineterminator='\n')
warning_csv = csv.writer(warning_file, delimiter=',', quoting=csv.QUOTE_MINIMAL, lineterminator='\n')
error_csv.writerow(self.report_headers)
warning_csv.writerow(self.report_headers)
# Adding formatting errors to error file
format_error_df = process_formatting_errors(self.short_rows, self.long_rows, self.report_headers)
format_error_df.to_csv(self.error_file_path, columns=self.report_headers, index=False, quoting=csv.QUOTE_ALL,
mode='a', header=False)
# Finally open the file for loading into the database with baseline validations
self.reader.open_file(region_name, bucket_name, self.file_name, self.fields, bucket_name,
self.get_file_name(self.error_file_name),
self.daims_to_short_dict[self.file_type.file_type_id],
self.short_to_daims_dict[self.file_type.file_type_id],
is_local=self.is_local)
# Going back to reprocess the header row
self.reader.file.seek(0)
reader_obj = pd.read_csv(self.reader.file, dtype=str, delimiter=self.reader.delimiter, error_bad_lines=False,
keep_default_na=False, chunksize=CHUNK_SIZE, warn_bad_lines=False)
for chunk_df in reader_obj:
self.process_data_chunk(sess, chunk_df)
# Ensure validated rows match initial row count
if file_row_count != self.total_rows:
raise ResponseException('', StatusCode.CLIENT_ERROR, None, ValidationError.rowCountError)
loading_duration = (datetime.now() - loading_start).total_seconds()
logger.info({
'message': 'Completed data loading {}'.format(self.log_str),
'message_type': 'ValidatorInfo',
'submission_id': self.submission_id,
'job_id': self.job.job_id,
'file_type': self.file_type.name,
'action': 'data_loading',
'status': 'finish',
'start_time': loading_start,
'end_time': datetime.now(),
'duration': loading_duration,
'total_rows': self.total_rows
})
return file_row_count