def _process_upload(context, data):
"""
When provided with a filename this function will process each row
within the file and then return a tuple. The tuple will contain
- a list of error messages (if any)
- a list of dicts where each dict contains ...
{
'package': 'a_package_id',
'action': 'Added' or 'Updated'
}
"""
log = inventory_upload.get_logger()
errors = []
results = []
filename = data['file']
publisher_name = data['publisher']
import urlparse
client = CkanClient(
base_location=urlparse.urljoin(context['site_url'], 'api'),
api_key=context['apikey'])
tableset = None
try:
_, ext = os.path.splitext(filename)
tableset = messytables.any_tableset(
open(filename, 'r'), extension=ext[1:])
except Exception, e:
if str(e) == "Unrecognized MIME type: text/plain":
tableset = messytables.any_tableset(f, mimetype="text/csv")
else:
errors.append("Unable to load file: {0}".format(e))
def _process_upload(context, data):
"""
When provided with a filename this function will process each row
within the file and then return a tuple. The tuple will contain
- a list of error messages (if any)
- a list of dicts where each dict contains ...
{
'package': 'a_package_id',
'action': 'Added' or 'Updated'
}
"""
log = inventory_upload.get_logger()
errors = []
results = []
filename = data['file']
publisher_name = data['publisher']
import urlparse
client = CkanClient(base_location=urlparse.urljoin(context['site_url'],
'api'),
api_key=context['apikey'])
tableset = None
try:
_, ext = os.path.splitext(filename)
tableset = messytables.any_tableset(open(filename, 'r'),
extension=ext[1:])
except Exception, e:
if str(e) == "Unrecognized MIME type: text/plain":
tableset = messytables.any_tableset(f, mimetype="text/csv")
else:
errors.append("Unable to load file: {0}".format(e))
def create_new_model(self, modelname, app_label):
""" Use messytables to guess field types and build a new model """
nocols = False
cols = self.csvfile[0]
for col in cols:
if not col:
nocols = True
if nocols:
cols = ["col_%s" % num for num in range(1, len(cols))]
print("No column names for %s columns" % len(cols))
else:
# strip quotes at ends and replace internal spaces with underscores
cols = [col.strip("\r") for col in cols]
cols = [col.strip('"') for col in cols]
cols = [col.strip("'") for col in cols]
cols = [cleancol.sub("_", col).lower() for col in cols]
try:
from messytables import any_tableset, type_guess
except:
self.errors.append(
"If you want to inspect CSV files to generate model code, you must install https://messytables.readthedocs.org"
)
self.modelname = ""
return
try:
table_set = any_tableset(self.filehandle)
row_set = table_set.tables[0]
types = type_guess(row_set.sample)
types = [str(typeobj) for typeobj in types]
# If the header has more cols than the data has cols - ignore the end ones
if len(cols) > len(types):
cols = cols[:len(types)]
except Exception as err:
self.errors.append("messytables could not run due to error")
self.errors.append(str(err))
self.modelname = ""
return
fieldset = []
maximums = self.get_maxlengths(cols)
for i, col in enumerate(cols):
length = maximums[i]
if types[i] == "String" and length > 255:
types[i] = "Text"
integer = length
decimal = int(length / 2)
if decimal > 10:
decimal = 10
blank = True
default = True
column = (col, types[i], length, length, integer, decimal, blank,
default)
fieldset.append(column)
# Import here so that messytables is not a dependency for just using csvimport cmd
from csvimport.make_model import MakeModel
maker = MakeModel()
return maker.model_from_table("%s_%s" % (app_label, modelname),
fieldset)
def main(argv=None):
args = parse_args(argv)
if args.file is None:
# slurp the whole input since there seems to be a bug in messytables
# which should be able to handle streams but doesn't
args.file = cStringIO.StringIO(sys.stdin.read())
relation_key = args_to_relation_key(args)
table_set = any_tableset(args.file)
if len(table_set.tables) != 1:
raise ValueError("Can only handle files with a single table, not %s" % len(table_set.tables))
row_set = table_set.tables[0]
# guess header names and the offset of the header:
offset, headers = headers_guess(row_set.sample)
row_set.register_processor(strip_processor())
row_set.register_processor(headers_processor(headers))
# Temporarily, mark the offset of the header
row_set.register_processor(offset_processor(offset + 1))
# guess types and register them
types = type_guess(replace_empty_string(row_set.sample), strict=True, types=[StringType, DecimalType, IntegerType])
row_set.register_processor(types_processor(types))
# Messytables seems to not handle the case where there are no headers.
# Work around this as follows:
# 1) offset must be 0
# 2) if the types of the data match the headers, assume there are
# actually no headers
if offset == 0:
try:
[t.cast(v) for (t, v) in zip(types, headers)]
except:
pass
else:
# We don't need the headers_processor or the offset_processor
row_set._processors = []
row_set.register_processor(strip_processor())
row_set.register_processor(types_processor(types))
headers = None
# Construct the Myria schema
schema = messy_to_schema(types, headers)
logging.info("Myria schema: {}".format(json.dumps(schema)))
# Prepare data for writing to Myria
data, kwargs = write_data(row_set, schema)
if not args.dry:
# Connect to Myria and send the data
connection = myria.MyriaConnection(hostname=args.hostname, port=args.port, ssl=args.ssl)
ret = connection.upload_file(relation_key, schema, data, args.overwrite, **kwargs)
sys.stdout.write(pretty_json(ret))
else:
sys.stdout.write(data)
def __init__(self, filename):
"""
When provided with a filename (to a CSV, XLS, or XLSX) the constructor
will attempt to load the file and ensure that messytables knows how to
process it.
"""
self.tableset = None
try:
_, ext = os.path.splitext(filename)
self.tableset = messytables.any_tableset(open(filename, "r"), extension=ext[1:])
except Exception, e:
if str(e) == "Unrecognized MIME type: text/plain":
# Attempt to force the load as a CSV file to work around messytables
# not recognising text/plain
self.tableset = messytables.any_tableset(f, mimetype="text/csv")
else:
log.exception(e)
raise Exception(u"Failed to load the file at {0}".format(filename))
def transform(self):
handle = self.open_data(self.url)
if not handle:
raise ResourceError("Remote resource missing",
"Unable to load the remote resource")
try:
if self.is_csv():
table_set = any_tableset(fileobj=handle,
extension=self.type)
else:
table_set = any_tableset(fileobj=handle,
extension=self.type,
mimetype=self.mimetype)
except Exception, e:
# e.g. ValueError('Unrecognized MIME type: application/vnd.oasis.opendocument.spreadsheet')
log.warn('Messytables parse error %s %s: %s', self.resource_identifier, self.url, e)
log.warn('Some data: ext: %s, mime: %s', self.type, self.mimetype)
raise ResourceError("Resource loading error",
"Unable to load the resource")
def transform(self):
handle = self.open_data(self.url)
if not handle:
raise ResourceError("Informacije", "Udaljeni resurs nedostupan")
try:
table_set = any_tableset(fileobj=handle,
extension=self.type,
mimetype=self.mimetype)
except Exception, e:
raise ResourceError("Informacija", "Resurs nedostupan")
def ingest(self, meta, local_path):
with open(local_path, 'rb') as fh:
table_set = any_tableset(fh,
extension=meta.extension,
mimetype=meta.mime_type,
window=20000)
tables = []
for sheet, row_set in enumerate(table_set.tables):
tables.append(self.generate_table(meta, sheet, row_set))
meta.tables = tables
document = self.create_document(meta)
self.emit(document)
def __init__(self, filename):
"""
When provided with a filename (to a CSV, XLS, or XLSX) the constructor
will attempt to load the file and ensure that messytables knows how to
process it.
"""
self.tableset = None
try:
_, ext = os.path.splitext(filename)
self.tableset = messytables.any_tableset(open(filename, 'r'),
extension=ext[1:])
except Exception, e:
if str(e) == "Unrecognized MIME type: text/plain":
# Attempt to force the load as a CSV file to work around messytables
# not recognising text/plain
self.tableset = messytables.any_tableset(filename,
mimetype="text/csv")
else:
log.exception(e)
raise Exception(
u"Failed to load the file at {0}".format(filename))
def proc(f, database_name, table_name):
table_set = messytables.any_tableset(f)
row_set = table_set.tables[0]
# guess header names and the offset of the header:
offset, headers = messytables.headers_guess(row_set.sample)
row_set.register_processor(messytables.headers_processor(headers))
row_set.register_processor(messytables.offset_processor(offset + 1))
types = messytables.type_guess(row_set.sample, types=[
messytables.types.StringType,
messytables.types.DateType,
], strict=True)
hive_data_file = tempfile.NamedTemporaryFile(mode='w')
fields_ddl = ','.join([
' {0} {1}\n'.format(
canonicalize_column_name(colName),
hive_column_type(colType)
)
for colName, colType in zip(headers, types)
])
hive_sql = '''
DROP TABLE IF EXISTS {0};
CREATE TABLE {0} (
{1}
)
STORED AS TEXTFILE
TBLPROPERTIES ("comment"="add_messytable on {3}");
LOAD DATA LOCAL INPATH '{2}' OVERWRITE INTO TABLE {0};
'''.format(table_name, fields_ddl, hive_data_file.name,
datetime.datetime.now().isoformat())
hive_cmd_file = tempfile.NamedTemporaryFile(mode='w')
print(hive_sql, file=hive_cmd_file)
hive_cmd_file.flush()
row_set.register_processor(messytables.types_processor(types))
for row in row_set:
print('\001'.join(map(str, [ c.value for c in row])),
file=hive_data_file)
hive_data_file.flush()
subprocess.call([
'hive',
'--database', database_name,
'-f', hive_cmd_file.name,
])
def create_new_model(self, modelname, app_label):
""" Use messytables to guess field types and build a new model """
nocols = False
cols = self.csvfile[0]
for col in cols:
if not col:
nocols = True
if nocols:
cols = ['col_%s' % num for num in range(1, len(cols))]
print('No column names for %s columns' % len(cols))
else:
cols = [cleancol.sub('_', col).lower() for col in cols]
try:
from messytables import any_tableset, type_guess
except:
self.errors.append(
'If you want to inspect CSV files to generate model code, you must install https://messytables.readthedocs.org'
)
self.modelname = ''
return
try:
table_set = any_tableset(self.filehandle)
row_set = table_set.tables[0]
types = type_guess(row_set.sample)
types = [str(typeobj) for typeobj in types]
except:
self.errors.append('messytables could not guess your column types')
self.modelname = ''
return
fieldset = []
maximums = self.get_maxlengths(cols)
for i, col in enumerate(cols):
length = maximums[i]
if types[i] == 'String' and length > 255:
types[i] = 'Text'
integer = length
decimal = int(length / 2)
if decimal > 10:
decimal = 10
blank = True
default = True
column = (col, types[i], length, length, integer, decimal, blank,
default)
fieldset.append(column)
# Import here so that messytables is not a dependency for just using csvimport cmd
from csvimport.make_model import MakeModel
maker = MakeModel()
return maker.model_from_table('%s_%s' % (app_label, modelname),
fieldset)
def transform(self):
handle = self.open_data(self.url)
if not handle:
raise ResourceError("Remote resource missing",
"Unable to load the remote resource")
try:
table_set = any_tableset(fileobj=handle,
extension=self.type,
mimetype=self.mimetype)
except Exception, e:
raise ResourceError("Resource loading error",
"Unable to load the resource")
def transform(self):
handle = self.open_data(self.url)
if not handle:
raise ResourceError("Informacije",
"Udaljeni resurs nedostupan")
try:
table_set = any_tableset(fileobj=handle,
extension=self.type,
mimetype=self.mimetype)
except Exception, e:
raise ResourceError("Informacija",
"Resurs nedostupan")
def validate_file(file_tmp, file_name, tmp_filepath):
log.info("upload: checking file * %s * ", file_name)
MAX_HEADER_LENGTH = 64
# not allowed characters ( - ' " ’ ‘) regex
inappropriate_chars = re.compile(r"[\-|\'|\"|\u2018|\u2019]");
datastore_ext = config.get('ckan.mimetype_guess', "csv xls xlsx tsv")
tmp_file_name, tmp_file_ext = os.path.splitext(file_name)
#check if datastore file (csv xls xlsx tsv)
if tmp_file_ext[1:].lower() in datastore_ext:
table_set = any_tableset(file_tmp)
#check if only one data sheet in the file
if len(table_set.tables)>1:
rollback_tmp(file_tmp, tmp_filepath)
log.error("upload: the file * %s * was not uploaded - There is more then one data sheet in the file", file_name)
raise logic.ValidationError(
{'upload': ['There is more then one data sheet in the file']}
)
else:
row_set = table_set.tables[0]
# guess header names and the offset of the header:
offset, headers = headers_guess(row_set.sample)
row_set.register_processor(headers_processor(headers))
for header in headers:
# too long header
if len(header) > MAX_HEADER_LENGTH:
rollback_tmp(file_tmp, tmp_filepath)
log.error("upload: the file * %s * was not uploaded - too long header - * %s *",
file_name, header)
raise logic.ValidationError(
{'upload': ['too long header (64 max)']}
)
# not allowed characters in header ( - ' " ’ ‘)
if inappropriate_chars.search(header):
rollback_tmp(file_tmp, tmp_filepath)
log.error("upload: the file * %s * was not uploaded - there are inappropriate characters in headers * %s *",
file_name, header)
raise logic.ValidationError(
{'upload': ['there are inappropriate characters in headers (apostrophe/apostrophes/dash)']}
)
# Check for duplicate fields
unique_fields = set(headers)
if not len(unique_fields) == len(headers):
rollback_tmp(file_tmp, tmp_filepath)
log.error("upload: the file * %s * was not uploaded - Duplicate column names are not supported", file_name)
raise logic.ValidationError({'upload': ['Duplicate column names are not supported']})
log.info("passed validation succesfully - the file * %s * was uploaded to CKAN (filestore)", file_name)
else:
pass
def create_new_model(self, modelname, app_label):
""" Use messytables to guess field types and build a new model """
nocols = False
cols = self.csvfile[0]
for col in cols:
if not col:
nocols = True
if nocols:
cols = ['col_%s' % num for num in range(1, len(cols))]
print ('No column names for %s columns' % len(cols))
else:
cols = [cleancol.sub('_', col).lower() for col in cols]
try:
from messytables import any_tableset, type_guess
except:
self.errors.append(
'If you want to inspect CSV files to generate model code, you must install https://messytables.readthedocs.org')
self.modelname = ''
return
try:
table_set = any_tableset(self.filehandle)
row_set = table_set.tables[0]
types = type_guess(row_set.sample)
types = [str(typeobj) for typeobj in types]
except Exception as err:
self.errors.append('messytables could not run due to error')
self.errors.append(str(err))
self.modelname = ''
return
fieldset = []
maximums = self.get_maxlengths(cols)
for i, col in enumerate(cols):
length = maximums[i]
if types[i] == 'String' and length > 255:
types[i] = 'Text'
integer = length
decimal = int(length / 2)
if decimal > 10:
decimal = 10
blank = True
default = True
column = (col, types[i], length, length, integer, decimal, blank, default)
fieldset.append(column)
# Import here so that messytables is not a dependency for just using csvimport cmd
from csvimport.make_model import MakeModel
maker = MakeModel()
return maker.model_from_table('%s_%s' % (app_label, modelname), fieldset)
def transform(self):
handle = self.open_data(self.url)
if not handle:
raise ResourceError("Remote resource missing",
"Unable to load the remote resource")
try:
table_set = any_tableset(fileobj=handle,
extension=self.type,
mimetype=self.mimetype)
except Exception, e:
raise ResourceError("Resource loading error",
"Unable to load the resource")
def ingest(self, meta, local_path):
with open(local_path, 'rb') as fh:
table_set = any_tableset(fh,
extension=meta.extension,
mimetype=meta.mime_type,
window=20000)
tables = []
document = self.create_document(meta)
for sheet, row_set in enumerate(table_set.tables):
tables.append(
self.generate_table(document, meta, sheet, row_set))
meta.tables = tables
document.meta = meta
self.emit(document)
def read_file(self, filename):
"""
Guess the filetype and read the file into row sets
"""
#print("Reading file", filename)
try:
fh = open(filename, 'rb')
table_set = any_tableset(fh) # guess the type...
except:
#traceback.print_exc()
# Cannot find the schema.
table_set = None
return table_set
def read_file(self, filename):
"""
Guess the filetype and read the file into row sets
"""
#print("Reading file", filename)
try:
fh = open(filename, 'rb')
table_set = any_tableset(fh) # guess the type...
except:
#traceback.print_exc()
# Cannot find the schema.
table_set = None
return table_set
def resource_row_set(package, resource):
""" Generate an iterator over all the rows in this resource's
source data. """
# Try to gather information about the source file type.
if not resource.meta.get('extension'):
resource.meta['extension'] = guess_extension(resource.meta.get('name'))
# This is a work-around because messytables hangs on boto file
# handles, so we're doing it via plain old HTTP.
table_set = any_tableset(resource.fh(),
extension=resource.meta.get('extension'),
mimetype=resource.meta.get('mime_type'))
tables = list(table_set.tables)
if not len(tables):
log.error("No tables were found in the source file.")
return
return tables[0]
def transform(self):
handle = self.open_data(self.url)
if not handle:
raise ResourceError("Remote resource missing",
"Unable to load the remote resource")
try:
table_set = any_tableset(fileobj=handle,
extension=self.type,
mimetype=self.mimetype)
except Exception, e:
# e.g. ValueError('Unrecognized MIME type: application/vnd.oasis.opendocument.spreadsheet')
log.warn('Messytables parse error %s %s: %s',
self.resource_identifier, self.url, e)
raise ResourceError("Resource loading error",
"Unable to load the resource")
def parse_table(source):
# This is a work-around because messytables hangs on boto file
# handles, so we're doing it via plain old HTTP.
# We're also passing in an extended window size to give more
# reliable type detection.
# Because Python's CSV dialect sniffer isn't the best, this also
# constrains the field quoting character to a double quote.
table_set = mt.any_tableset(source.fh(),
extension=source.meta.get('extension'),
mimetype=source.meta.get('mime_type'),
quotechar='"',
window=20000)
tables = list(table_set.tables)
if not len(tables):
log.error("No tables were found in the source file.")
return
row_set = tables[0]
headers = [c.value for c in next(row_set.sample)]
row_set.register_processor(mt.headers_processor(headers))
row_set.register_processor(mt.offset_processor(1))
types = mt.type_guess(row_set.sample, strict=True)
row_set.register_processor(mt.types_processor(types, strict=True))
fields, i = {}, 0
row_iter = iter(row_set)
while True:
i += 1
try:
row = row_iter.next()
if not len(fields):
fields = generate_field_spec(row)
data = convert_row(row, fields, i)
check_empty = set(data.values())
if None in check_empty and len(check_empty) == 1:
continue
yield None, fields, data
except StopIteration:
return
except Exception, e:
# log.exception(e)
yield e, fields, None
def parse_data(input):
fh = open(input, 'rb')
try:
table_set = messytables.any_tableset(fh)
except messytables.ReadError as e:
print(e)
get_row_set = lambda table_set: table_set.tables.pop()
row_set = get_row_set(table_set)
offset, headers = messytables.headers_guess(row_set.sample)
# Some headers might have been converted from strings to floats and such.
headers = [str(header) for header in headers]
row_set.register_processor(messytables.headers_processor(headers))
row_set.register_processor(messytables.offset_processor(offset + 1))
types = messytables.type_guess(row_set.sample, types=TYPES, strict=True)
row_set.register_processor(messytables.types_processor(types))
headers = [header.strip() for header in headers if header.strip()]
headers_set = set(headers)
def row_iterator():
for row in row_set:
data_row = {}
for index, cell in enumerate(row):
column_name = cell.column.strip()
if column_name not in headers_set:
continue
data_row[column_name] = cell.value
yield data_row
result = row_iterator()
headers_dicts = [
dict(id=field[0], type=TYPE_MAPPING[str(field[1])])
for field in zip(headers, types)
]
print('Determined headers and types: {headers}'.format(
headers=headers_dicts))
return headers_dicts, result
def parse_table(source):
# This is a work-around because messytables hangs on boto file
# handles, so we're doing it via plain old HTTP.
# We're also passing in an extended window size to give more
# reliable type detection.
# Because Python's CSV dialect sniffer isn't the best, this also
# constrains the field quoting character to a double quote.
table_set = mt.any_tableset(source.fh(),
extension=source.meta.get('extension'),
mimetype=source.meta.get('mime_type'),
quotechar='"', window=20000)
tables = list(table_set.tables)
if not len(tables):
log.error("No tables were found in the source file.")
return
row_set = tables[0]
headers = [c.value for c in next(row_set.sample)]
row_set.register_processor(mt.headers_processor(headers))
row_set.register_processor(mt.offset_processor(1))
types = mt.type_guess(row_set.sample, strict=True)
row_set.register_processor(mt.types_processor(types, strict=True))
fields, i = {}, 0
row_iter = iter(row_set)
while True:
i += 1
try:
row = row_iter.next()
if not len(fields):
fields = generate_field_spec(row)
data = convert_row(row, fields, i)
check_empty = set(data.values())
if None in check_empty and len(check_empty) == 1:
continue
yield None, fields, data
except StopIteration:
return
except Exception, e:
# log.exception(e)
yield e, fields, None
def resource_row_set(package, resource):
""" Generate an iterator over all the rows in this resource's
source data. """
# This is a work-around because messytables hangs on boto file
# handles, so we're doing it via plain old HTTP.
table_set = any_tableset(resource.fh(),
extension=resource.meta.get('extension'),
mimetype=resource.meta.get('mime_type'))
tables = list(table_set.tables)
if not len(tables):
log.error("No tables were found in the source file.")
return
row_set = tables[0]
offset, headers = headers_guess(row_set.sample)
row_set.register_processor(headers_processor(headers))
row_set.register_processor(offset_processor(offset + 1))
types = type_guess(row_set.sample, strict=True)
row_set.register_processor(types_processor(types))
return row_set
def test_simple_zip(self):
fh = horror_fobj('simple.zip')
table_set = any_tableset(fh, extension='zip')
assert isinstance(table_set, ZIPTableSet)
def push_to_datastore(self, context, resource):
# Get the resource's content hash, which is used to check whether the
# resource file has changed since last time.
hash_dict = resource.get('hash')
if hash_dict:
original_content_hash = json.loads(hash_dict)['content']
check_hash = not self.options.force
else:
# This resource has no hash yet, it must be a new resource.
original_content_hash = ''
check_hash = False
try:
result = fetch_resource.download(context,
resource,
self.max_content_length,
DATA_FORMATS,
check_modified=check_hash)
except fetch_resource.ResourceNotModified as e:
logger.info(
u'Skipping unmodified resource: {0}'.format(resource['url'])
)
return {'success': True,
'resource': resource['id'],
'error': None}
except Exception as e:
logger.exception(e)
return {'success': False,
'resource': resource['id'],
'error': 'Could not download resource'}
if check_hash and (result['hash'] == original_content_hash):
logger.info(
u'Skipping unmodified resource: {0}'.format(resource['url'])
)
os.remove(result['saved_file'])
return {'success': True,
'resource': resource['id'],
'error': None}
content_type = result['headers'].get('content-type', '')\
.split(';', 1)[0] # remove parameters
f = open(result['saved_file'], 'rb')
try:
table_sets = any_tableset(
f,
mimetype=content_type,
extension=resource['format'].lower()
)
# only first sheet in xls for time being
row_set = table_sets.tables[0]
offset, headers = headers_guess(row_set.sample)
except Exception as e:
logger.exception(e)
os.remove(result['saved_file'])
return {'success': False,
'resource': resource['id'],
'error': 'Error parsing the resource'}
row_set.register_processor(headers_processor(headers))
row_set.register_processor(offset_processor(offset + 1))
row_set.register_processor(datetime_procesor())
logger.info('Header offset: {0}.'.format(offset))
guessed_types = type_guess(
row_set.sample,
[
messytables.types.StringType,
messytables.types.IntegerType,
messytables.types.FloatType,
messytables.types.DecimalType,
messytables.types.DateUtilType
],
strict=True
)
logger.info('Guessed types: {0}'.format(guessed_types))
row_set.register_processor(types_processor(guessed_types, strict=True))
row_set.register_processor(stringify_processor())
guessed_type_names = [TYPE_MAPPING[type(gt)] for gt in
guessed_types]
def send_request(data):
data_dict = {
'resource_id': resource['id'],
'fields': [dict(id=name, type=typename) for name, typename
in zip(headers, guessed_type_names)],
'records': data,
'force': True,
}
response = toolkit.get_action('datastore_create')(
context,
data_dict
)
return response
# Delete any existing data before proceeding. Otherwise
# 'datastore_create' will append to the existing datastore. And if the
# fields have significantly changed, it may also fail.
logger.info('Trying to delete existing datastore for resource {0} '
'(may not exist).'.format(resource['id']))
try:
toolkit.get_action('datastore_delete')(
context,
{'resource_id': resource['id'], 'force': True}
)
except toolkit.ObjectNotFound:
logger.info('Datastore not found for resource {0}.'.format(
resource['id']))
except Exception as e:
logger.exception(e)
logger.info('Creating: {0}.'.format(resource['id']))
# generates chunks of data that can be loaded into ckan
# n is the maximum size of a chunk
def chunky(iterable, n):
it = iter(iterable)
while True:
chunk = list(
itertools.imap(
dict, itertools.islice(it, n)))
if not chunk:
return
yield chunk
count = 0
try:
for data in chunky(row_set.dicts(), 100):
count += len(data)
send_request(data)
except Exception as e:
logger.exception(e)
os.remove(result['saved_file'])
return {'success': False,
'resource': resource['id'],
'error': 'Error pushing data to datastore'}
logger.info("There should be {n} entries in {res_id}.".format(
n=count,
res_id=resource['id']
))
resource.update({
'webstore_url': 'active',
'webstore_last_updated': datetime.now().isoformat()
})
toolkit.get_action('resource_update')(context, resource)
os.remove(result['saved_file'])
return {'success': True,
'resource': resource['id'],
'error': None}
def parse_csv(filename, cfg_in):
"""
Guess csv structure
:param filename:
:param cfg_in:
:param known_structure: list of strings formats in order of columns, from start
but may be not all (next is auto treeted)
:return: lst_types, offset, headers
* quotechar - specifies a one-character string to use as the
quoting character. It defaults to '"'.
* delimiter - specifies a one-character string to use as the
field separator. It defaults to ','.
* skipinitialspace - specifies how to interpret whitespace which
immediately follows a delimiter. It defaults to False, which
means that whitespace immediately following a delimiter is part
of the following field.
* lineterminator - specifies the character sequence which should
terminate rows.
* quoting - controls when quotes should be generated by the writer.
It can take on any of the following module constants:
csv.QUOTE_MINIMAL means only when required, for example, when a
field contains either the quotechar or the delimiter
csv.QUOTE_ALL means that quotes are always placed around fields.
csv.QUOTE_NONNUMERIC means that quotes are always placed around
fields which do not parse as integers or floating point
numbers.
csv.QUOTE_NONE means that quotes are never placed around fields.
* escapechar - specifies a one-character string used to escape
the delimiter when quoting is set to QUOTE_NONE.
* doublequote - controls the handling of quotes inside fields. When
True, two consecutive quotes are interpreted as one during read,
and when writing, each quote character embedded in the data is
written as two quotes
Example:
parse_csv(filename, ['%H:%M:%S'])
"""
set_field_if_no(cfg_in, 'types', [])
set_field_if_no(cfg_in, 'delimiter')
with open(filename, 'rb') as fh:
ext = os_path.splitext(filename)[1]
# Load a file object:
try:
# If you are sure that file is csv use CSVTableSet(fh)
from magic import MagicException # because any_tableset uses libmagic
table_set = any_tableset(fh,
mimetype=None,
extension=ext,
delimiter=cfg_in['delimiter'])
except (ImportError, MagicException) as e:
print('There are error ', standard_error_info(e),
'\n=> Loading file as csv without trying other formats')
table_set = CSVTableSet(fh, delimiter=cfg_in['delimiter'])
# A table set is a collection of tables:
row_set = table_set.tables[0]
# A row set is an iterator over the table, but it can only
# be run once. To peek, a sample is provided:
# guess header names and the offset of the header:
offset, headers = headers_guess(row_set.sample) # tolerance=1
row_set.register_processor(headers_processor(headers))
# add one to begin with content, not the header:
row_set.register_processor(offset_processor(offset + 1))
# guess column types:
lst_types = type_guess(row_set.sample, strict=True)
row_sample = next(row_set.sample)
# check not detected types
def formats2types(formats_str):
for f in formats_str:
if f:
if is_date_format(f):
yield (types.DateType(f))
else:
yield (TimeType())
else:
yield (None)
known_types = formats2types(cfg_in['types'])
for n, (t, s, kt) in enumerate(zip(lst_types, row_sample,
known_types)):
if t.result_type == types.StringType.result_type:
# not auto detected? -> check known_types
if kt.test(s.value):
lst_types[n] = kt # t= kt
else: # known_types fits element
print(
"col'"
's#{:d} value "{}" type not match provided type of {}'.
format(n, s.value, type(kt)))
# kt = types.DateType('mm/dd/yyyy')
# kt.test('0'+s.value)
# detect?
else:
pass
# not works for time type:
# print(jts.headers_and_typed_as_jts(headers,
# list(map(jts.celltype_as_string, lst_types))).as_json())
return lst_types, offset, headers
def test_libreoffice_xlsx(self):
fh = horror_fobj('libreoffice.xlsx')
table_set = any_tableset(fh)
row_set = table_set.tables[0]
data = list(row_set)
assert_equal(0, len(data))
def _datastorer_upload(context, resource, logger):
result = download(context, resource, data_formats=DATA_FORMATS)
content_type = result['headers'].get('content-type', '')\
.split(';', 1)[0] # remove parameters
f = open(result['saved_file'], 'rb')
table_sets = any_tableset(f,
mimetype=content_type,
extension=resource['format'].lower())
##only first sheet in xls for time being
row_set = table_sets.tables[0]
offset, headers = headers_guess(row_set.sample)
row_set.register_processor(headers_processor(headers))
row_set.register_processor(offset_processor(offset + 1))
row_set.register_processor(datetime_procesor())
logger.info('Header offset: {0}.'.format(offset))
guessed_types = type_guess(row_set.sample, [
messytables.types.StringType, messytables.types.IntegerType,
messytables.types.FloatType, messytables.types.DecimalType,
messytables.types.DateUtilType
],
strict=True)
logger.info('Guessed types: {0}'.format(guessed_types))
row_set.register_processor(types_processor(guessed_types, strict=True))
row_set.register_processor(stringify_processor())
ckan_url = context['site_url'].rstrip('/')
datastore_create_request_url = '%s/api/action/datastore_create' % (
ckan_url)
guessed_type_names = [TYPE_MAPPING[type(gt)] for gt in guessed_types]
def send_request(data):
request = {
'resource_id':
resource['id'],
'fields': [
dict(id=name, type=typename)
for name, typename in zip(headers, guessed_type_names)
],
'force':
True,
'records':
data
}
response = requests.post(
datastore_create_request_url,
data=json.dumps(request),
headers={
'Content-Type': 'application/json',
'Authorization': context['apikey']
},
)
check_response_and_retry(response, datastore_create_request_url,
logger)
# Delete any existing data before proceeding. Otherwise 'datastore_create' will
# append to the existing datastore. And if the fields have significantly changed,
# it may also fail.
try:
logger.info(
'Deleting existing datastore (it may not exist): {0}.'.format(
resource['id']))
response = requests.post('%s/api/action/datastore_delete' % (ckan_url),
data=json.dumps({
'resource_id': resource['id'],
'force': True
}),
headers={
'Content-Type': 'application/json',
'Authorization': context['apikey']
})
if not response.status_code or response.status_code not in (200, 404):
# skips 200 (OK) or 404 (datastore does not exist, no need to delete it)
logger.error('Deleting existing datastore failed: {0}'.format(
get_response_error(response)))
raise DatastorerException("Deleting existing datastore failed.")
except requests.exceptions.RequestException as e:
logger.error('Deleting existing datastore failed: {0}'.format(str(e)))
raise DatastorerException("Deleting existing datastore failed.")
logger.info('Creating: {0}.'.format(resource['id']))
# generates chunks of data that can be loaded into ckan
# n is the maximum size of a chunk
def chunky(iterable, n):
it = iter(iterable)
while True:
chunk = list(itertools.imap(dict, itertools.islice(it, n)))
if not chunk:
return
yield chunk
count = 0
for data in chunky(row_set.dicts(), 100):
count += len(data)
send_request(data)
logger.info("There should be {n} entries in {res_id}.".format(
n=count, res_id=resource['id']))
ckan_request_url = ckan_url + '/api/action/resource_update'
resource.update({
'webstore_url':
'active',
'webstore_last_updated':
datetime.datetime.now().isoformat()
})
response = requests.post(ckan_request_url,
data=json.dumps(resource),
headers={
'Content-Type': 'application/json',
'Authorization': context['apikey']
})
if response.status_code not in (201, 200):
raise DatastorerException(
'Ckan bad response code (%s). Response was %s' %
(response.status_code, response.content))
def check_filename(d):
if not d['tableset']:
raise SkipTest("Optional library not installed. Skipping")
fh = horror_fobj(d['filename'])
table_set = any_tableset(fh, extension=d['filename'], auto_detect=False)
assert isinstance(table_set, d['tableset']), type(table_set)
def check_no_filename(d):
fh = horror_fobj(d['filename'])
table_set = any_tableset(fh)
assert isinstance(table_set, d['tableset']), type(table_set)
def test_scraperwiki_xlsx(self):
fh = horror_fobj('sw_gen.xlsx')
table_set = any_tableset(fh)
row_set = table_set.tables[0]
data = list(row_set)
assert_equal(16, len(data))
def push_to_datastore(task_id, input, dry_run=False):
'''Download and parse a resource push its data into CKAN's DataStore.
An asynchronous job that gets a resource from CKAN, downloads the
resource's data file and, if the data file has changed since last time,
parses the data and posts it into CKAN's DataStore.
:param dry_run: Fetch and parse the data file but don't actually post the
data to the DataStore, instead return the data headers and rows that
would have been posted.
:type dry_run: boolean
'''
handler = util.StoringHandler(task_id, input)
logger = logging.getLogger(task_id)
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
validate_input(input)
data = input['metadata']
ckan_url = data['ckan_url']
resource_id = data['resource_id']
api_key = input.get('api_key')
try:
resource = get_resource(resource_id, ckan_url, api_key)
except util.JobError as e:
# try again in 5 seconds just incase CKAN is slow at adding resource
time.sleep(5)
resource = get_resource(resource_id, ckan_url, api_key)
# check if the resource url_type is a datastore
if resource.get('url_type') == 'datastore':
logger.info('Dump files are managed with the Datastore API')
return
# check scheme
url = resource.get('url')
scheme = urlsplit(url).scheme
if scheme not in ('http', 'https', 'ftp'):
raise util.JobError(
'Only http, https, and ftp resources may be fetched.'
)
# fetch the resource data
logger.info('Fetching from: {0}'.format(url))
headers = {}
if resource.get('url_type') == 'upload':
# If this is an uploaded file to CKAN, authenticate the request,
# otherwise we won't get file from private resources
headers['Authorization'] = api_key
try:
response = requests.get(
url,
headers=headers,
timeout=DOWNLOAD_TIMEOUT,
verify=SSL_VERIFY,
stream=True, # just gets the headers for now
)
response.raise_for_status()
cl = response.headers.get('content-length')
try:
if cl and int(cl) > MAX_CONTENT_LENGTH:
raise util.JobError(
'Resource too large to download: {cl} > max ({max_cl}).'
.format(cl=cl, max_cl=MAX_CONTENT_LENGTH))
except ValueError:
pass
tmp = tempfile.TemporaryFile()
length = 0
m = hashlib.md5()
for chunk in response.iter_content(CHUNK_SIZE):
length += len(chunk)
if length > MAX_CONTENT_LENGTH:
raise util.JobError(
'Resource too large to process: {cl} > max ({max_cl}).'
.format(cl=length, max_cl=MAX_CONTENT_LENGTH))
tmp.write(chunk)
m.update(chunk)
ct = response.headers.get('content-type', '').split(';', 1)[0]
except requests.HTTPError as e:
raise HTTPError(
"DataPusher received a bad HTTP response when trying to download "
"the data file", status_code=e.response.status_code,
request_url=url, response=e.response.content)
except requests.RequestException as e:
raise HTTPError(
message=str(e), status_code=None,
request_url=url, response=None)
file_hash = m.hexdigest()
tmp.seek(0)
if (resource.get('hash') == file_hash
and not data.get('ignore_hash')):
logger.info("The file hash hasn't changed: {hash}.".format(
hash=file_hash))
return
resource['hash'] = file_hash
try:
table_set = messytables.any_tableset(tmp, mimetype=ct, extension=ct)
except messytables.ReadError as e:
# try again with format
tmp.seek(0)
try:
format = resource.get('format')
table_set = messytables.any_tableset(tmp, mimetype=format, extension=format)
except:
raise util.JobError(e)
get_row_set = web.app.config.get('GET_ROW_SET',
lambda table_set: table_set.tables.pop())
row_set = get_row_set(table_set)
offset, headers = messytables.headers_guess(row_set.sample)
existing = datastore_resource_exists(resource_id, api_key, ckan_url)
existing_info = None
if existing:
existing_info = dict((f['id'], f['info'])
for f in existing.get('fields', []) if 'info' in f)
# Some headers might have been converted from strings to floats and such.
headers = [str(header) for header in headers]
row_set.register_processor(messytables.headers_processor(headers))
row_set.register_processor(messytables.offset_processor(offset + 1))
types = messytables.type_guess(row_set.sample, types=TYPES, strict=True)
# override with types user requested
if existing_info:
types = [{
'text': messytables.StringType(),
'numeric': messytables.DecimalType(),
'timestamp': messytables.DateUtilType(),
}.get(existing_info.get(h, {}).get('type_override'), t)
for t, h in zip(types, headers)]
row_set.register_processor(messytables.types_processor(types))
headers = [header.strip() for header in headers if header.strip()]
headers_set = set(headers)
def row_iterator():
for row in row_set:
data_row = {}
for index, cell in enumerate(row):
column_name = cell.column.strip()
if column_name not in headers_set:
continue
if isinstance(cell.value, str):
try:
data_row[column_name] = cell.value.encode('latin-1').decode('utf-8')
except (UnicodeDecodeError, UnicodeEncodeError):
data_row[column_name] = cell.value
else:
data_row[column_name] = cell.value
yield data_row
result = row_iterator()
'''
Delete existing datstore resource before proceeding. Otherwise
'datastore_create' will append to the existing datastore. And if
the fields have significantly changed, it may also fail.
'''
if existing:
logger.info('Deleting "{res_id}" from datastore.'.format(
res_id=resource_id))
delete_datastore_resource(resource_id, api_key, ckan_url)
headers_dicts = [dict(id=field[0], type=TYPE_MAPPING[str(field[1])])
for field in zip(headers, types)]
# Maintain data dictionaries from matching column names
if existing_info:
for h in headers_dicts:
if h['id'] in existing_info:
h['info'] = existing_info[h['id']]
# create columns with types user requested
type_override = existing_info[h['id']].get('type_override')
if type_override in list(_TYPE_MAPPING.values()):
h['type'] = type_override
logger.info('Determined headers and types: {headers}'.format(
headers=headers_dicts))
if dry_run:
return headers_dicts, result
count = 0
for i, chunk in enumerate(chunky(result, 250)):
records, is_it_the_last_chunk = chunk
count += len(records)
logger.info('Saving chunk {number} {is_last}'.format(
number=i, is_last='(last)' if is_it_the_last_chunk else ''))
send_resource_to_datastore(resource, headers_dicts, records,
is_it_the_last_chunk, api_key, ckan_url)
logger.info('Successfully pushed {n} entries to "{res_id}".'.format(
n=count, res_id=resource_id))
if data.get('set_url_type', False):
update_resource(resource, api_key, ckan_url)
def test_simple_csv(self):
fh = horror_fobj('simple.csv')
table_set = any_tableset(fh, extension='csv')
assert isinstance(table_set, CSVTableSet)
def test_libreoffice_xlsx(self):
fh = horror_fobj('libreoffice.xlsx')
table_set = any_tableset(fh)
row_set = table_set.tables[0]
data = list(row_set)
assert_equal(0, len(data))
def test_scraperwiki_xlsx(self):
fh = horror_fobj('sw_gen.xlsx')
table_set = any_tableset(fh)
row_set = table_set.tables[0]
data = list(row_set)
assert_equal(16, len(data))
def check_no_filename(d):
if not d['tableset']:
raise SkipTest("Optional library not installed. Skipping")
fh = horror_fobj(d['filename'])
table_set = any_tableset(fh)
assert isinstance(table_set, d['tableset']), type(table_set)
f = cStringIO.StringIO(file_content)
##
#f = cStringIO.StringIO(response.read())
file_hash = hashlib.md5(f.read()).hexdigest()
f.seek(0)
if (resource.get('hash') == file_hash and not data.get('ignore_hash')):
logger.info(
"The file hash hasn't changed: {hash}.".format(hash=file_hash))
return
resource['hash'] = file_hash
try:
table_set = messytables.any_tableset(f, mimetype=ct, extension=ct)
except messytables.ReadError as e:
## try again with format
f.seek(0)
try:
format = resource.get('format')
table_set = messytables.any_tableset(f,
mimetype=format,
extension=format)
except:
raise util.JobError(e)
row_set = table_set.tables.pop()
offset, headers = messytables.headers_guess(row_set.sample)
existing = datastore_resource_exists(resource_id, api_key, ckan_url)
def validate_file(file_tmp, file_name, tmp_filepath):
log.info("upload: checking file * %s * ", file_name)
MAX_HEADER_LENGTH = 64
# not allowed characters ( - ' " ’ ‘) regex
inappropriate_chars = re.compile(r"[\-|\'|\"|\u2018|\u2019]")
datastore_ext = config.get('ckan.mimetype_guess', "csv xls xlsx tsv")
tmp_file_name, tmp_file_ext = os.path.splitext(file_name)
tmp_file_ext_str = tmp_file_ext[1:].lower()
#check if datastore file (csv xls xlsx tsv)
if tmp_file_ext_str in datastore_ext:
try:
table_set = any_tableset(file_tmp)
except:
log.info("file is not valid * %s * ", file_name)
raise logic.ValidationError({'upload': ['The file is not valid']})
#check if only one data sheet in the file
if len(table_set.tables) > 1:
rollback_tmp(file_tmp, tmp_filepath)
log.error(
"upload: the file * %s * was not uploaded - There is more then one data sheet in the file",
file_name)
raise logic.ValidationError({
'upload': [_('There is more then one data sheet in the file')]
})
#check if table_set is not empty
elif len(table_set.tables) > 0:
row_set = table_set.tables[0]
# guess header names and the offset of the header:
offset, headers = headers_guess(row_set.sample)
row_set.register_processor(headers_processor(headers))
for header in headers:
# too long header
if len(header) > MAX_HEADER_LENGTH:
rollback_tmp(file_tmp, tmp_filepath)
log.error(
"upload: the file * %s * was not uploaded - too long header - * %s *",
file_name, header)
raise logic.ValidationError(
{'upload': [_('too long header (64 max)')]})
# not allowed characters in header ( - ' " ’ ‘)
#if inappropriate_chars.search(header):
# rollback_tmp(file_tmp, tmp_filepath)
# log.error("upload: the file * %s * was not uploaded - there are inappropriate characters in headers * %s *",
# file_name, header)
# raise logic.ValidationError(
# {'upload': [_('there are inappropriate characters in headers (apostrophe/apostrophes/dash)')]}
# )
# Check for duplicate fields
unique_fields = set(headers)
if not len(unique_fields) == len(headers):
rollback_tmp(file_tmp, tmp_filepath)
log.error(
"upload: the file * %s * was not uploaded - Duplicate column names are not supported",
file_name)
raise logic.ValidationError({
'upload': [_('Duplicate column names are not supported')]
})
log.info(
"passed validation succesfully - the file * %s * was uploaded to CKAN (filestore)",
file_name)
else:
log.info(
"no table_set was created by messytables - skip headers validations in the file * %s * ",
file_name)
else:
pass
def test_unknown(self):
fh = horror_fobj('simple.unknown')
self.assertRaises(ValueError, lambda: any_tableset(fh, extension='unknown'))
file_hash = m.hexdigest()
tmp.seek(0)
if (resource.get('hash') == file_hash and not data.get('ignore_hash')):
logger.info(
"The file hash hasn't changed: {hash}.".format(hash=file_hash))
return
resource['hash'] = file_hash
# Decoded data if needed
decoded_tmp = force_decode(tmp)
try:
table_set = messytables.any_tableset(decoded_tmp,
mimetype=ct,
extension=ct)
except messytables.ReadError as e:
## try again with format
decoded_tmp.seek(0)
try:
format = resource.get('format')
table_set = messytables.any_tableset(decoded_tmp,
mimetype=format,
extension=format)
except:
raise util.JobError(e)
row_set = table_set.tables.pop()
offset, headers = messytables.headers_guess(row_set.sample)
def check_filename(d):
fh = horror_fobj(d['filename'])
table_set = any_tableset(fh, extension=d['filename'], auto_detect=False)
assert isinstance(table_set, d['tableset']), type(table_set)
def test_simple_xlsx(self):
fh = horror_fobj('simple.xlsx')
table_set = any_tableset(fh, extension='xlsx')
assert isinstance(table_set, XLSXTableSet)
def load_table(table_filepath, resource_id, mimetype='text/csv', logger=None):
'''Loads an Excel file (or other tabular data recognized by messytables)
into Datastore and creates indexes.
Largely copied from datapusher - see below. Is slower than load_csv.
'''
# use messytables to determine the header row
logger.info('Determining column names and types')
ct = mimetype
format = os.path.splitext(table_filepath)[1] # filename extension
with open(table_filepath, 'rb') as tmp:
#
# Copied from datapusher/jobs.py:push_to_datastore
#
try:
table_set = messytables.any_tableset(tmp,
mimetype=ct,
extension=ct)
except messytables.ReadError as e:
# try again with format
tmp.seek(0)
try:
table_set = messytables.any_tableset(tmp,
mimetype=format,
extension=format)
except Exception as e:
raise LoaderError(e)
if not table_set.tables:
raise LoaderError('Could not parse file as tabular data')
row_set = table_set.tables.pop()
offset, headers = messytables.headers_guess(row_set.sample)
existing = datastore_resource_exists(resource_id)
existing_info = None
if existing:
existing_info = dict((f['id'], f['info'])
for f in existing.get('fields', [])
if 'info' in f)
# Some headers might have been converted from strings to floats and such.
headers = encode_headers(headers)
row_set.register_processor(messytables.headers_processor(headers))
row_set.register_processor(messytables.offset_processor(offset + 1))
TYPES, TYPE_MAPPING = get_types()
types = messytables.type_guess(row_set.sample,
types=TYPES,
strict=True)
# override with types user requested
if existing_info:
types = [{
'text': messytables.StringType(),
'numeric': messytables.DecimalType(),
'timestamp': messytables.DateUtilType(),
}.get(existing_info.get(h, {}).get('type_override'), t)
for t, h in zip(types, headers)]
row_set.register_processor(messytables.types_processor(types))
headers = [
header.strip()[:MAX_COLUMN_LENGTH] for header in headers
if header.strip()
]
headers_set = set(headers)
def row_iterator():
for row in row_set:
data_row = {}
for index, cell in enumerate(row):
column_name = cell.column.strip()
if column_name not in headers_set:
continue
data_row[column_name] = cell.value
yield data_row
result = row_iterator()
'''
Delete existing datstore resource before proceeding. Otherwise
'datastore_create' will append to the existing datastore. And if
the fields have significantly changed, it may also fail.
'''
if existing:
logger.info('Deleting "{res_id}" from datastore.'.format(
res_id=resource_id))
delete_datastore_resource(resource_id)
headers_dicts = [
dict(id=field[0], type=TYPE_MAPPING[str(field[1])])
for field in zip(headers, types)
]
# Maintain data dictionaries from matching column names
if existing_info:
for h in headers_dicts:
if h['id'] in existing_info:
h['info'] = existing_info[h['id']]
# create columns with types user requested
type_override = existing_info[h['id']].get('type_override')
if type_override in _TYPE_MAPPING.values():
h['type'] = type_override
logger.info('Determined headers and types: {headers}'.format(
headers=headers_dicts))
### Commented - this is only for tests
# if dry_run:
# return headers_dicts, result
logger.info('Copying to database...')
count = 0
for i, records in enumerate(chunky(result, 250)):
count += len(records)
logger.info('Saving chunk {number}'.format(number=i))
send_resource_to_datastore(resource_id, headers_dicts, records)
logger.info('...copying done')
if count:
logger.info(
'Successfully pushed {n} entries to "{res_id}".'.format(
n=count, res_id=resource_id))
else:
# no datastore table is created
raise LoaderError('No entries found - nothing to load')
def test_xlsm(self):
fh = horror_fobj('bian-anal-mca-2005-dols-eng-1011-0312-tab3.xlsm')
table_set = any_tableset(fh, extension='xls')
row_set = table_set.tables[0]
data = list(row_set)
assert_equal(62, len(data))
def load_csv(csv_filepath, resource_id, mimetype='text/csv', logger=None):
'''Loads a CSV into DataStore. Does not create the indexes.'''
# use messytables to determine the header row
extension = os.path.splitext(csv_filepath)[1]
tempdir = tempfile.mkdtemp(suffix=resource_id)
if extension.lower() == '.zip':
with zipfile.ZipFile(csv_filepath, "r") as zip_ref:
csvfiles = [
file for file in zip_ref.filelist
if file.filename.lower().endswith('.csv')
]
if len(csvfiles) == 0:
logger.info("no csvfiles found in %s" % csv_filepath)
if len(csvfiles) > 0:
if len(csvfiles) > 1:
logger.info(
"multiple csv files found in %s, only one will be ingested: %s"
% (csv_filepath, csvfiles[0].filename))
else:
logger.info("unzipping %s and ingesting %s" %
(csv_filepath, csvfiles[0].filename))
zip_ref.extract(csvfiles[0], tempdir)
new_loc = os.path.join(tempdir, csvfiles[0].filename)
csv_filepath = new_loc
extension = os.path.splitext(csv_filepath)[1]
logger.info("unzipped %s" % csvfiles[0].filename)
# close.
zip_ref.close()
with open(csv_filepath, 'rb') as f:
try:
table_set = messytables.any_tableset(f,
mimetype=mimetype,
extension=extension)
except messytables.ReadError as e:
# # try again with format
# f.seek(0)
# try:
# format = resource.get('format')
# table_set = messytables.any_tableset(f, mimetype=format,
# extension=format)
# except Exception:
raise LoaderError('Messytables error: {}'.format(e))
if not table_set.tables:
raise LoaderError('Could not detect tabular data in this file')
row_set = table_set.tables.pop()
header_offset, headers = messytables.headers_guess(row_set.sample)
# Some headers might have been converted from strings to floats and such.
headers = encode_headers(headers)
# Guess the delimiter used in the file
with open(csv_filepath, 'r') as f:
header_line = f.readline()
try:
sniffer = csv.Sniffer()
delimiter = sniffer.sniff(header_line).delimiter
except csv.Error:
logger.warning(
'Could not determine delimiter from file, use default ","')
delimiter = ','
# Setup the converters that run when you iterate over the row_set.
# With pgloader only the headers will be iterated over.
row_set.register_processor(messytables.headers_processor(headers))
row_set.register_processor(messytables.offset_processor(header_offset + 1))
# types = messytables.type_guess(row_set.sample, types=TYPES, strict=True)
headers = [
header.strip()[:MAX_COLUMN_LENGTH] for header in headers
if header.strip()
]
# headers_dicts = [dict(id=field[0], type=TYPE_MAPPING[str(field[1])])
# for field in zip(headers, types)]
# TODO worry about csv header name problems
# e.g. duplicate names
# encoding (and line ending?)- use chardet
# It is easier to reencode it as UTF8 than convert the name of the encoding
# to one that pgloader will understand.
logger.info('Ensuring character coding is UTF8')
f_write = tempfile.NamedTemporaryFile(suffix=extension, delete=False)
try:
with open(csv_filepath, 'rb') as f_read:
csv_decoder = messytables.commas.UTF8Recoder(f_read, encoding=None)
for line in csv_decoder:
f_write.write(line)
f_write.close() # ensures the last line is written
csv_filepath = f_write.name
logger.info('Ensuring character coding is UTF8 complete')
# check tables exists
# datastore db connection
engine = get_write_engine()
# get column info from existing table
existing = datastore_resource_exists(resource_id)
existing_info = {}
if existing:
existing_info = dict((f['id'], f['info'])
for f in existing.get('fields', [])
if 'info' in f)
'''
Delete existing datastore table before proceeding. Otherwise
the COPY will append to the existing table. And if
the fields have significantly changed, it may also fail.
'''
logger.info('Deleting "{res_id}" from DataStore.'.format(
res_id=resource_id))
delete_datastore_resource(resource_id)
# Columns types are either set (overridden) in the Data Dictionary page
# or default to text type (which is robust)
fields = [
{'id': header_name,
'type': existing_info.get(header_name, {})\
.get('type_override') or 'text',
}
for header_name in headers]
# Maintain data dictionaries from matching column names
if existing_info:
for f in fields:
if f['id'] in existing_info:
f['info'] = existing_info[f['id']]
logger.info('Fields: {}'.format(fields))
# Create table
from ckan import model
context = {'model': model, 'ignore_auth': True}
data_dict = dict(
resource_id=resource_id,
fields=fields,
)
data_dict['records'] = None # just create an empty table
data_dict['force'] = True # TODO check this - I don't fully
# understand read-only/datastore resources
try:
p.toolkit.get_action('datastore_create')(context, data_dict)
except p.toolkit.ValidationError as e:
if 'fields' in e.error_dict:
# e.g. {'message': None, 'error_dict': {'fields': [u'"***" is not a valid field name']}, '_error_summary': None}
error_message = e.error_dict['fields'][0]
raise LoaderError(
'Error with field definition: {}'.format(error_message))
else:
raise LoaderError(
'Validation error when creating the database table: {}'.
format(str(e)))
except Exception as e:
raise LoaderError(
'Could not create the database table: {}'.format(e))
connection = context['connection'] = engine.connect()
if not fulltext_trigger_exists(connection, resource_id):
logger.info('Trigger created')
_create_fulltext_trigger(connection, resource_id)
# datstore_active is switched on by datastore_create - TODO temporarily
# disable it until the load is complete
# logger.info('Disabling row index trigger')
_disable_fulltext_trigger(connection, resource_id)
# logger.info('Dropping indexes')
_drop_indexes(context, data_dict, False)
logger.info('Copying to database...')
# Options for loading into postgres:
# 1. \copy - can't use as that is a psql meta-command and not accessible
# via psycopg2
# 2. COPY - requires the db user to have superuser privileges. This is
# dangerous. It is also not available on AWS, for example.
# 3. pgloader method? - as described in its docs:
# Note that while the COPY command is restricted to read either from its standard input or from a local file on the server's file system, the command line tool psql implements a \copy command that knows how to stream a file local to the client over the network and into the PostgreSQL server, using the same protocol as pgloader uses.
# 4. COPY FROM STDIN - not quite as fast as COPY from a file, but avoids
# the superuser issue. <-- picked
# with psycopg2.connect(DSN) as conn:
# with conn.cursor() as curs:
# curs.execute(SQL)
raw_connection = engine.raw_connection()
try:
cur = raw_connection.cursor()
try:
with open(csv_filepath, 'rb') as f:
# can't use :param for table name because params are only
# for filter values that are single quoted.
try:
cur.copy_expert(
"COPY \"{resource_id}\" ({column_names}) "
"FROM STDIN "
"WITH (DELIMITER '{delimiter}', FORMAT csv, HEADER 1, "
" ENCODING '{encoding}');".format(
resource_id=resource_id,
column_names=', '.join(
['"{}"'.format(h) for h in headers]),
delimiter=delimiter,
encoding='UTF8',
), f)
except psycopg2.DataError as e:
# e is a str but with foreign chars e.g.
# 'extra data: "paul,pa\xc3\xbcl"\n'
# but logging and exceptions need a normal (7 bit) str
error_str = str(e).decode('ascii', 'replace').encode(
'ascii', 'replace')
logger.warning(error_str)
raise LoaderError(
'Error during the load into PostgreSQL:'
' {}'.format(error_str))
finally:
cur.close()
finally:
raw_connection.commit()
finally:
os.remove(csv_filepath) # i.e. the tempfile
shutil.rmtree(tempdir)
logger.info('...copying done')
logger.info('Creating search index...')
_populate_fulltext(connection, resource_id, fields=fields)
logger.info('...search index created')
return fields
def check_no_filename(d):
if not d['tableset']:
raise SkipTest("Optional library not installed. Skipping")
fh = horror_fobj(d['filename'])
table_set = any_tableset(fh)
assert isinstance(table_set, d['tableset']), type(table_set)
def _datastorer_upload(context, resource, logger):
result = download(context, resource, data_formats=DATA_FORMATS)
logger.info('Downloaded resource %r' %(resource))
content_type = result['headers'].get('content-type', '')\
.split(';', 1)[0] # remove parameters
extension = resource['format'].lower()
fp = open(result['saved_file'], 'rb')
if zipfile.is_zipfile(result['saved_file']):
fp, zf = open_zipped_tableset(fp, extension=extension)
logger.info('Opened entry %s from ZIP archive %s', zf, result['saved_file'])
else:
logger.info('Opened file %s' %(result['saved_file']))
table_sets = any_tableset(fp, extension=extension)
if 'sample_size' in context:
table_sets.window = max(1000, int(context['sample_size']))
logger.info('Using a sample window of %d', table_sets.window)
##only first sheet in xls for time being
row_set = table_sets.tables[0]
offset, headers = headers_guess(row_set.sample)
row_set.register_processor(headers_processor(headers))
row_set.register_processor(offset_processor(offset + 1))
row_set.register_processor(datetime_procesor())
logger.info('Header offset: {0}.'.format(offset))
guessed_types = type_guess(
row_set.sample,
[
messytables.types.StringType,
messytables.types.IntegerType,
messytables.types.FloatType,
messytables.types.DecimalType,
messytables.types.DateUtilType
],
strict=True
)
logger.info('Guessed types: {0}'.format(guessed_types))
row_set.register_processor(types_processor(guessed_types, strict=True))
row_set.register_processor(stringify_processor())
ckan_url = context['site_url'].rstrip('/')
datastore_create_request_url = '%s/api/action/datastore_create' % (ckan_url)
guessed_type_names = [TYPE_MAPPING[type(gt)] for gt in guessed_types]
def send_request(data):
request = {'resource_id': resource['id'],
'fields': [dict(id=name, type=typename) for name, typename in zip(headers, guessed_type_names)],
'force': True,
'records': data}
response = requests.post(datastore_create_request_url,
data=json.dumps(request),
headers={'Content-Type': 'application/json',
'Authorization': context['apikey']},
)
check_response_and_retry(response, datastore_create_request_url, logger)
# Delete any existing data before proceeding. Otherwise 'datastore_create' will
# append to the existing datastore. And if the fields have significantly changed,
# it may also fail.
try:
logger.info('Deleting existing datastore (it may not exist): {0}.'.format(resource['id']))
response = requests.post('%s/api/action/datastore_delete' % (ckan_url),
data=json.dumps({'resource_id': resource['id'], 'force': True}),
headers={'Content-Type': 'application/json',
'Authorization': context['apikey']}
)
if not response.status_code or response.status_code not in (200, 404):
# skips 200 (OK) or 404 (datastore does not exist, no need to delete it)
logger.error('Deleting existing datastore failed: {0}'.format(get_response_error(response)))
raise DatastorerException("Deleting existing datastore failed.")
except requests.exceptions.RequestException as e:
logger.error('Deleting existing datastore failed: {0}'.format(str(e)))
raise DatastorerException("Deleting existing datastore failed.")
logger.info('Creating: {0}.'.format(resource['id']))
# generates chunks of data that can be loaded into ckan
# n is the maximum size of a chunk
def chunky(iterable, n):
it = iter(iterable)
while True:
chunk = list(
itertools.imap(
dict, itertools.islice(it, n)))
if not chunk:
return
yield chunk
count = 0
for data in chunky(row_set.dicts(), 100):
count += len(data)
send_request(data)
logger.info("There should be {n} entries in {res_id}.".format(n=count, res_id=resource['id']))
ckan_request_url = ckan_url + '/api/action/resource_update'
resource.update({
'webstore_url': 'active',
'webstore_last_updated': datetime.datetime.now().isoformat()
})
response = requests.post(
ckan_request_url,
data=json.dumps(resource),
headers={'Content-Type': 'application/json',
'Authorization': context['apikey']})
if response.status_code not in (201, 200):
raise DatastorerException('Ckan bad response code (%s). Response was %s' %
(response.status_code, response.content))
ct = response.info().getheader('content-type').split(';', 1)[0]
f = cStringIO.StringIO(response.read())
file_hash = hashlib.md5(f.read()).hexdigest()
f.seek(0)
if (resource.get('hash') == file_hash
and not data.get('ignore_hash')):
logger.info("The file hash hasn't changed: {hash}.".format(
hash=file_hash))
return
resource['hash'] = file_hash
try:
table_set = messytables.any_tableset(f, mimetype=ct, extension=ct)
except messytables.ReadError as e:
## try again with format
f.seek(0)
try:
format = resource.get('format')
table_set = messytables.any_tableset(f, mimetype=format, extension=format)
except:
raise util.JobError(e)
row_set = table_set.tables.pop()
offset, headers = messytables.headers_guess(row_set.sample)
row_set.register_processor(messytables.headers_processor(headers))
row_set.register_processor(messytables.offset_processor(offset + 1))
types = messytables.type_guess(row_set.sample, types=TYPES, strict=True)
row_set.register_processor(messytables.types_processor(types))
def check_filename(d):
if not d['tableset']:
raise SkipTest("Optional library not installed. Skipping")
fh = horror_fobj(d['filename'])
table_set = any_tableset(fh, extension=d['filename'], auto_detect=False)
assert isinstance(table_set, d['tableset']), type(table_set)