def send_resource_to_datastore(resource_id, headers, records):
"""
Stores records in CKAN datastore
"""
request = {'resource_id': resource_id,
'fields': headers,
'force': True,
'records': records}
from ckan import model
context = {'model': model, 'ignore_auth': True}
try:
p.toolkit.get_action('datastore_create')(context, request)
except p.toolkit.ValidationError as e:
raise LoaderError('Validation error writing rows to db: {}'
.format(str(e)))
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 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 load_csv(csv_filepath, resource_id, mimetype='text/csv', logger=None):
# use messytables to determine the header row
extension = os.path.splitext(csv_filepath)[1]
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 = [unicode(header) for header in headers]
# 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() 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
# 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}
try:
p.toolkit.get_action('datastore_create')(
context,
dict(
resource_id=resource_id,
fields=fields,
records=None, # just create an empty table
force=True, # TODO check this - I don't fully understand
# read-only/datastore resources
))
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 = engine.connect()
if not fulltext_trigger_exists(connection, resource_id):
_create_fulltext_trigger(connection, resource_id)
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)
connection = engine.raw_connection()
try:
cur = 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 ',', FORMAT csv, HEADER 1, "
" ENCODING '{encoding}');".format(
resource_id=resource_id,
column_names=', '.join(
['"{}"'.format(h) for h in headers]),
encoding='UTF8',
), f)
except psycopg2.DataError as e:
logger.error(e)
raise LoaderError(
'Error during the load into PostgreSQL:'
' {}'.format(e))
finally:
cur.close()
finally:
connection.commit()
finally:
os.remove(csv_filepath) # i.e. the tempfile
logger.info('...copying done')
