import os
import json
from collections import OrderedDict
import singer
from singer import metadata
from tap_google_sheets.streams import STREAMS
LOGGER = singer.get_logger()
# Reference:
# https://github.com/singer-io/getting-started/blob/master/docs/DISCOVERY_MODE.md#Metadata
# Convert column index to column letter
def colnum_string(n):
string = ""
while n > 0:
n, remainder = divmod(n - 1, 26)
string = chr(65 + remainder) + string
return string
# Create sheet_metadata_json with columns from sheet
def get_sheet_schema_columns(sheet, spreadsheet_id, client):
sheet_json_schema = OrderedDict()
data = next(iter(sheet.get('data', [])), {})
row_data = data.get('rowData',[])
# spreadsheet is an OrderedDict, with orderd sheets and rows in the repsonse
headers = row_data[0].get('values', [])
first_values = row_data[1].get('values', [])
# LOGGER.info('first_values = {}'.format(json.dumps(first_values, indent=2, sort_keys=True)))
sheet_json_schema['type'] = 'object'
sheet_json_schema['additionalProperties'] = False
sheet_json_schema = {
'type': 'object',
'additionalProperties': False,
'properties': {
'__sdc_spreadsheet_id': {
'type': ['null', 'string']
},
'__sdc_sheet_id': {
'type': ['null', 'integer']
},
'__sdc_row': {
'type': ['null', 'integer']
}
}
}
header_list = [] # used for checking uniqueness
columns = []
prior_header = None
i = 0
skipped = 0
# Read column headers until end or 2 consecutive skipped headers
for header in headers:
# LOGGER.info('header = {}'.format(json.dumps(header, indent=2, sort_keys=True)))
column_index = i + 1
column_letter = colnum_string(column_index)
header_value = header.get('formattedValue')
if header_value: # NOT skipped
column_is_skipped = False
skipped = 0
column_name = '{}'.format(header_value)
if column_name in header_list:
raise Exception('DUPLICATE HEADER ERROR: {}'.format(column_name))
else:
header_list.append(column_name)
first_value = first_values[i]
# LOGGER.info('first_value[{}] = {}'.format(i, json.dumps(first_value, indent=2, sort_keys=True)))
column_effective_value = first_value.get('effectiveValue', {})
for key in column_effective_value.keys():
if key in ('numberValue', 'stringValue', 'boolValue', 'errorType', 'formulaType'):
column_effective_value_type = key
column_number_format = first_values[i].get('effectiveFormat', {}).get('numberFormat', {})
column_number_format_type = column_number_format.get('type')
# Determine datatype for sheet_json_schema
#
# column_effective_value_type = numberValue, stringValue, boolValue; INVALID: errorType, formulaType
# Reference: https://developers.google.com/sheets/api/reference/rest/v4/spreadsheets/other#ExtendedValue
#
# column_number_format_type = UNEPECIFIED, TEXT, NUMBER, PERCENT, CURRENCY, DATE, TIME, DATE_TIME, SCIENTIFIC
# Reference: https://developers.google.com/sheets/api/reference/rest/v4/spreadsheets/cells#NumberFormatType
#
column_format = None # Default
# column_multiple_of = None # Default
if column_effective_value_type in ('formulaValue', 'errorValue'):
raise Exception('INVALID DATA TYPE ERROR: {}, value: {}'.format(column_name))
elif column_effective_value_type == 'stringValue':
column_type = ['null', 'string']
column_gs_type = 'stringValue'
elif column_effective_value_type == 'boolValue':
column_type = ['null', 'boolean', 'string']
column_gs_type = 'boolValue'
elif column_effective_value_type == 'numberValue':
if column_number_format_type == 'DATE_TIME':
column_type = ['null', 'string']
column_format = 'date-time'
column_gs_type = 'numberType.DATE_TIME'
elif column_number_format_type == 'DATE':
column_type = ['null', 'string']
column_format = 'date'
column_gs_type = 'numberType.DATE'
elif column_number_format_type == 'TIME':
column_type = ['null', 'string']
column_format = 'time'
column_gs_type = 'numberType.TIME'
elif column_number_format_type == 'TEXT':
column_type = ['null', 'string']
column_gs_type = 'stringValue'
else:
column_type = ['null', 'number', 'string']
column_gs_type = 'numberType'
else: # skipped
column_is_skipped = True
skipped = skipped + 1
column_index_str = str(column_index).zfill(2)
column_name = '__sdc_skip_col_{}'.format(column_index_str)
column_type = ['null', 'string']
column_format = None
column_gs_type = 'stringValue'
if skipped >= 2:
# skipped = 2 consecutive skipped headers
# Remove prior_header column_name
sheet_json_schema['properties'].pop(prior_header, None)
column_count = i - 1
break
else:
column = {}
column = {
'columnIndex': column_index,
'columnLetter': column_letter,
'columnName': column_name,
'columnType': column_gs_type,
'columnSkipped': column_is_skipped
}
columns.append(column)
sheet_json_schema['properties'][column_name] = column
sheet_json_schema['properties'][column_name]['type'] = column_type
if column_format:
sheet_json_schema['properties'][column_name]['format'] = column_format
prior_header = column_name
i = i + 1
return sheet_json_schema, columns
def get_sheet_metadata(sheet, spreadsheet_id, client):
sheet_id = sheet.get('properties', {}).get('sheetId')
sheet_title = sheet.get('properties', {}).get('title')
LOGGER.info('sheet_id = {}, sheet_title = {}'.format(sheet_id, sheet_title))
stream_name = 'sheet_metadata'
stream_metadata = STREAMS.get(stream_name)
api = stream_metadata.get('api', 'sheets')
params = stream_metadata.get('params', {})
querystring = '&'.join(['%s=%s' % (key, value) for (key, value) in params.items()]).replace('{sheet_title}', sheet_title)
path = '{}?{}'.format(stream_metadata.get('path').replace('{spreadsheet_id}', spreadsheet_id), querystring)
sheet_md_results = client.get(path=path, api=api, endpoint=stream_name)
sheet_cols = sheet_md_results.get('sheets')[0]
sheet_schema, columns = get_sheet_schema_columns(sheet_cols, spreadsheet_id, client)
return sheet_schema, columns
def get_abs_path(path):
return os.path.join(os.path.dirname(os.path.realpath(__file__)), path)
def get_schemas(client, spreadsheet_id):
schemas = {}
field_metadata = {}
for stream_name, stream_metadata in STREAMS.items():
schema_path = get_abs_path('schemas/{}.json'.format(stream_name))
with open(schema_path) as file:
schema = json.load(file)
schemas[stream_name] = schema
mdata = metadata.new()
# Documentation:
# https://github.com/singer-io/getting-started/blob/master/docs/DISCOVERY_MODE.md#singer-python-helper-functions
# Reference:
# https://github.com/singer-io/singer-python/blob/master/singer/metadata.py#L25-L44
mdata = metadata.get_standard_metadata(
schema=schema,
key_properties=stream_metadata.get('key_properties', None),
valid_replication_keys=stream_metadata.get('replication_keys', None),
replication_method=stream_metadata.get('replication_method', None)
)
field_metadata[stream_name] = mdata
if stream_name == 'spreadsheet_metadata':
api = stream_metadata.get('api', 'sheets')
params = stream_metadata.get('params', {})
querystring = '&'.join(['%s=%s' % (key, value) for (key, value) in params.items()])
path = '{}?{}'.format(stream_metadata.get('path').replace('{spreadsheet_id}', spreadsheet_id), querystring)
spreadsheet_md_results = client.get(path=path, params=querystring, api=api, endpoint=stream_name)
sheets = spreadsheet_md_results.get('sheets')
if sheets:
for sheet in sheets:
sheet_schema, columns = get_sheet_metadata(sheet, spreadsheet_id, client)
# LOGGER.info('sheet_schema = {}'.format(json.dumps(sheet_schema, indent=2, sort_keys=True)))
sheet_title = sheet.get('properties', {}).get('title')
schemas[sheet_title] = sheet_schema
sheet_mdata = metadata.new()
sheet_mdata = metadata.get_standard_metadata(
schema=sheet_schema,
key_properties=['__sdc_row'],
valid_replication_keys=None,
replication_method='FULL_TABLE'
)
field_metadata[sheet_title] = sheet_mdata
return schemas, field_metadata
