[github/fretlink/tap-google-sheets.git] / tap_google_sheets / schema.py

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(num):
    string = ""
    while num > 0:
        num, remainder = divmod(num - 1, 26)
        string = chr(65 + remainder) + string
    return string


# Create sheet_metadata_json with columns from sheet
def get_sheet_schema_columns(sheet):
    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',
        '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))
            header_list.append(column_name)

            first_value = first_values[i]

            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
            #  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
            #  https://developers.google.com/sheets/api/reference/rest/v4/spreadsheets/cells#NumberFormatType
            #
            column_format = None # Default
            if column_effective_value_type == 'stringValue':
                col_properties = {'type': ['null', 'string']}
                column_gs_type = 'stringValue'
            elif column_effective_value_type == 'boolValue':
                col_properties = {'type': ['null', 'boolean', 'string']}
                column_gs_type = 'boolValue'
            elif column_effective_value_type == 'numberValue':
                if column_number_format_type == 'DATE_TIME':
                    col_properties = {
                        'type': ['null', 'string'],
                        'format': 'date-time'
                    }
                    column_gs_type = 'numberType.DATE_TIME'
                elif column_number_format_type == 'DATE':
                    col_properties = {
                        'type': ['null', 'string'],
                        'format': 'date'
                    }
                    column_gs_type = 'numberType.DATE'
                elif column_number_format_type == 'TIME':
                    col_properties = {
                        'type': ['null', 'string'],
                        'format': 'time'
                    }
                    column_gs_type = 'numberType.TIME'
                elif column_number_format_type == 'TEXT':
                    col_properties = {'type': ['null', 'string']}
                    column_gs_type = 'stringValue'
                else:
                    # Interesting - order in the anyOf makes a difference.
                    # Number w/ multipleOf must be listed last, otherwise errors occur.
                    col_properties =  {
                        'anyOf': [
                            {
                                'type': 'string'
                            },
                            {
                                'type': 'null'
                            },
                            {
                                'type': 'number',
                                'multipleOf': 1e-15
                            }
                        ]
                    }
                    column_gs_type = 'numberType'
            # Catch-all to deal with other types and set to string
            # column_effective_value_type: formulaValue, errorValue, or other
            else:
                col_properties = {'type': ['null', 'string']}
                column_gs_type = 'unsupportedValue'
                LOGGER.info('Unsupported data type: {}, value: {}'.format(column_name, \
                    column_effective_value_type))
                LOGGER.info('Converting to string.')
        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)
            col_properties = {'type': ['null', 'string']}
            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)
            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] = col_properties

        prior_header = column_name
        i = i + 1

    return sheet_json_schema, columns


# Get Header Row and 1st data row (Rows 1 & 2) from a Sheet on Spreadsheet w/ sheet_metadata query
#   endpoint: spreadsheets/{spreadsheet_id}
#   params: includeGridData = true, ranges = '{sheet_title}'!1:2
# This endpoint includes detailed metadata about each cell - incl. data type, formatting, etc.
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_metadata: 1st `sheets` node in results
    sheet_metadata = sheet_md_results.get('sheets')[0]

    # Create sheet_json_schema (for discovery/catalog) and columns (for sheet_metadata results)
    sheet_json_schema, columns = get_sheet_schema_columns(sheet_metadata)

    return sheet_json_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)

            # GET spreadsheet_metadata, which incl. sheets (basic metadata for each worksheet)
            spreadsheet_md_results = client.get(path=path, params=querystring, api=api, \
                endpoint=stream_name)

            sheets = spreadsheet_md_results.get('sheets')
            if sheets:
                # Loop thru each worksheet in spreadsheet
                for sheet in sheets:
                    # GET sheet_json_schema for each worksheet (from function above)
                    sheet_json_schema, columns = get_sheet_metadata(sheet, spreadsheet_id, client)
                    LOGGER.info('columns = {}'.format(columns))

                    sheet_title = sheet.get('properties', {}).get('title')
                    schemas[sheet_title] = sheet_json_schema
                    sheet_mdata = metadata.new()
                    sheet_mdata = metadata.get_standard_metadata(
                        schema=sheet_json_schema,
                        key_properties=['__sdc_row'],
                        valid_replication_keys=None,
                        replication_method='FULL_TABLE'
                    )
                    field_metadata[sheet_title] = sheet_mdata

    return schemas, field_metadata
Commit	Line	Data
89643ba6 JH	1	import os
	2	import json
	3	from collections import OrderedDict
	4	import singer
	5	from singer import metadata
	6	from tap_google_sheets.streams import STREAMS
	7
	8	LOGGER = singer.get_logger()
	9
	10	# Reference:
	11	# https://github.com/singer-io/getting-started/blob/master/docs/DISCOVERY_MODE.md#Metadata
	12
	13	# Convert column index to column letter
99424fee	14	def colnum_string(num):
89643ba6	15	string = ""
99424fee JH	16	while num > 0:
99424fee JH	17	num, remainder = divmod(num - 1, 26)
89643ba6 JH	18	string = chr(65 + remainder) + string
	19	return string
	20
	21
	22	# Create sheet_metadata_json with columns from sheet
99424fee	23	def get_sheet_schema_columns(sheet):
89643ba6 JH	24	sheet_json_schema = OrderedDict()
89643ba6 JH	25	data = next(iter(sheet.get('data', [])), {})
99424fee	26	row_data = data.get('rowData', [])
89643ba6 JH	27	# spreadsheet is an OrderedDict, with orderd sheets and rows in the repsonse
	28
	29	headers = row_data[0].get('values', [])
	30	first_values = row_data[1].get('values', [])
	31	# LOGGER.info('first_values = {}'.format(json.dumps(first_values, indent=2, sort_keys=True)))
	32
89643ba6 JH	33	sheet_json_schema = {
	34	'type': 'object',
	35	'additionalProperties': False,
	36	'properties': {
	37	'__sdc_spreadsheet_id': {
	38	'type': ['null', 'string']
	39	},
	40	'__sdc_sheet_id': {
	41	'type': ['null', 'integer']
	42	},
	43	'__sdc_row': {
	44	'type': ['null', 'integer']
	45	}
	46	}
	47	}
	48
	49	header_list = [] # used for checking uniqueness
	50	columns = []
	51	prior_header = None
	52	i = 0
	53	skipped = 0
	54	# Read column headers until end or 2 consecutive skipped headers
	55	for header in headers:
	56	# LOGGER.info('header = {}'.format(json.dumps(header, indent=2, sort_keys=True)))
	57	column_index = i + 1
	58	column_letter = colnum_string(column_index)
	59	header_value = header.get('formattedValue')
	60	if header_value: # NOT skipped
	61	column_is_skipped = False
	62	skipped = 0
	63	column_name = '{}'.format(header_value)
	64	if column_name in header_list:
	65	raise Exception('DUPLICATE HEADER ERROR: {}'.format(column_name))
99424fee	66	header_list.append(column_name)
89643ba6 JH	67
89643ba6 JH	68	first_value = first_values[i]
89643ba6 JH	69
	70	column_effective_value = first_value.get('effectiveValue', {})
	71	for key in column_effective_value.keys():
	72	if key in ('numberValue', 'stringValue', 'boolValue', 'errorType', 'formulaType'):
	73	column_effective_value_type = key
	74
99424fee JH	75	column_number_format = first_values[i].get('effectiveFormat', {}).get(
99424fee JH	76	'numberFormat', {})
89643ba6 JH	77	column_number_format_type = column_number_format.get('type')
	78
	79	# Determine datatype for sheet_json_schema
	80	#
99424fee JH	81	# column_effective_value_type = numberValue, stringValue, boolValue;
	82	# INVALID: errorType, formulaType
	83	# https://developers.google.com/sheets/api/reference/rest/v4/spreadsheets/other#ExtendedValue
89643ba6	84	#
99424fee JH	85	# column_number_format_type = UNEPECIFIED, TEXT, NUMBER, PERCENT, CURRENCY, DATE,
	86	# TIME, DATE_TIME, SCIENTIFIC
	87	# https://developers.google.com/sheets/api/reference/rest/v4/spreadsheets/cells#NumberFormatType
89643ba6 JH	88	#
89643ba6 JH	89	column_format = None # Default
99424fee	90	if column_effective_value_type == 'stringValue':
5890b89c	91	col_properties = {'type': ['null', 'string']}
89643ba6 JH	92	column_gs_type = 'stringValue'
89643ba6 JH	93	elif column_effective_value_type == 'boolValue':
5890b89c	94	col_properties = {'type': ['null', 'boolean', 'string']}
89643ba6 JH	95	column_gs_type = 'boolValue'
	96	elif column_effective_value_type == 'numberValue':
	97	if column_number_format_type == 'DATE_TIME':
5890b89c JH	98	col_properties = {
	99	'type': ['null', 'string'],
	100	'format': 'date-time'
	101	}
89643ba6 JH	102	column_gs_type = 'numberType.DATE_TIME'
89643ba6 JH	103	elif column_number_format_type == 'DATE':
5890b89c JH	104	col_properties = {
	105	'type': ['null', 'string'],
	106	'format': 'date'
	107	}
89643ba6 JH	108	column_gs_type = 'numberType.DATE'
89643ba6 JH	109	elif column_number_format_type == 'TIME':
5890b89c JH	110	col_properties = {
	111	'type': ['null', 'string'],
	112	'format': 'time'
	113	}
89643ba6 JH	114	column_gs_type = 'numberType.TIME'
89643ba6 JH	115	elif column_number_format_type == 'TEXT':
5890b89c	116	col_properties = {'type': ['null', 'string']}
89643ba6 JH	117	column_gs_type = 'stringValue'
89643ba6 JH	118	else:
5890b89c JH	119	# Interesting - order in the anyOf makes a difference.
	120	# Number w/ multipleOf must be listed last, otherwise errors occur.
	121	col_properties = {
	122	'anyOf': [
	123	{
	124	'type': 'string'
	125	},
	126	{
	127	'type': 'null'
	128	},
	129	{
	130	'type': 'number',
	131	'multipleOf': 1e-15
	132	}
	133	]
	134	}
89643ba6	135	column_gs_type = 'numberType'
5890b89c JH	136	# Catch-all to deal with other types and set to string
	137	# column_effective_value_type: formulaValue, errorValue, or other
	138	else:
	139	col_properties = {'type': ['null', 'string']}
	140	column_gs_type = 'unsupportedValue'
	141	LOGGER.info('Unsupported data type: {}, value: {}'.format(column_name, \
99424fee	142	column_effective_value_type))
5890b89c	143	LOGGER.info('Converting to string.')
89643ba6 JH	144	else: # skipped
	145	column_is_skipped = True
	146	skipped = skipped + 1
	147	column_index_str = str(column_index).zfill(2)
	148	column_name = '__sdc_skip_col_{}'.format(column_index_str)
5890b89c	149	col_properties = {'type': ['null', 'string']}
89643ba6 JH	150	column_gs_type = 'stringValue'
	151
	152	if skipped >= 2:
	153	# skipped = 2 consecutive skipped headers
	154	# Remove prior_header column_name
	155	sheet_json_schema['properties'].pop(prior_header, None)
89643ba6 JH	156	break
	157
	158	else:
	159	column = {}
	160	column = {
	161	'columnIndex': column_index,
	162	'columnLetter': column_letter,
	163	'columnName': column_name,
	164	'columnType': column_gs_type,
	165	'columnSkipped': column_is_skipped
	166	}
	167	columns.append(column)
	168
5890b89c	169	sheet_json_schema['properties'][column_name] = col_properties
89643ba6 JH	170
	171	prior_header = column_name
	172	i = i + 1
	173
	174	return sheet_json_schema, columns
	175
	176
5890b89c JH	177	# Get Header Row and 1st data row (Rows 1 & 2) from a Sheet on Spreadsheet w/ sheet_metadata query
	178	# endpoint: spreadsheets/{spreadsheet_id}
	179	# params: includeGridData = true, ranges = '{sheet_title}'!1:2
	180	# This endpoint includes detailed metadata about each cell - incl. data type, formatting, etc.
89643ba6 JH	181	def get_sheet_metadata(sheet, spreadsheet_id, client):
	182	sheet_id = sheet.get('properties', {}).get('sheetId')
	183	sheet_title = sheet.get('properties', {}).get('title')
	184	LOGGER.info('sheet_id = {}, sheet_title = {}'.format(sheet_id, sheet_title))
	185
	186	stream_name = 'sheet_metadata'
	187	stream_metadata = STREAMS.get(stream_name)
	188	api = stream_metadata.get('api', 'sheets')
	189	params = stream_metadata.get('params', {})
99424fee JH	190	querystring = '&'.join(['%s=%s' % (key, value) for (key, value) in \
	191	params.items()]).replace('{sheet_title}', sheet_title)
	192	path = '{}?{}'.format(stream_metadata.get('path').replace('{spreadsheet_id}', \
	193	spreadsheet_id), querystring)
89643ba6 JH	194
89643ba6 JH	195	sheet_md_results = client.get(path=path, api=api, endpoint=stream_name)
5890b89c JH	196	# sheet_metadata: 1st `sheets` node in results
5890b89c JH	197	sheet_metadata = sheet_md_results.get('sheets')[0]
89643ba6	198
5890b89c JH	199	# Create sheet_json_schema (for discovery/catalog) and columns (for sheet_metadata results)
	200	sheet_json_schema, columns = get_sheet_schema_columns(sheet_metadata)
	201
	202	return sheet_json_schema, columns
89643ba6 JH	203
	204
	205	def get_abs_path(path):
	206	return os.path.join(os.path.dirname(os.path.realpath(__file__)), path)
	207
	208	def get_schemas(client, spreadsheet_id):
	209	schemas = {}
	210	field_metadata = {}
	211
	212	for stream_name, stream_metadata in STREAMS.items():
	213	schema_path = get_abs_path('schemas/{}.json'.format(stream_name))
	214	with open(schema_path) as file:
	215	schema = json.load(file)
	216	schemas[stream_name] = schema
	217	mdata = metadata.new()
	218
	219	# Documentation:
	220	# https://github.com/singer-io/getting-started/blob/master/docs/DISCOVERY_MODE.md#singer-python-helper-functions
	221	# Reference:
	222	# https://github.com/singer-io/singer-python/blob/master/singer/metadata.py#L25-L44
	223	mdata = metadata.get_standard_metadata(
	224	schema=schema,
	225	key_properties=stream_metadata.get('key_properties', None),
	226	valid_replication_keys=stream_metadata.get('replication_keys', None),
	227	replication_method=stream_metadata.get('replication_method', None)
	228	)
	229	field_metadata[stream_name] = mdata
99424fee	230
89643ba6 JH	231	if stream_name == 'spreadsheet_metadata':
	232	api = stream_metadata.get('api', 'sheets')
	233	params = stream_metadata.get('params', {})
	234	querystring = '&'.join(['%s=%s' % (key, value) for (key, value) in params.items()])
99424fee JH	235	path = '{}?{}'.format(stream_metadata.get('path').replace('{spreadsheet_id}', \
99424fee JH	236	spreadsheet_id), querystring)
89643ba6	237
5890b89c	238	# GET spreadsheet_metadata, which incl. sheets (basic metadata for each worksheet)
99424fee JH	239	spreadsheet_md_results = client.get(path=path, params=querystring, api=api, \
99424fee JH	240	endpoint=stream_name)
89643ba6 JH	241
	242	sheets = spreadsheet_md_results.get('sheets')
	243	if sheets:
5890b89c	244	# Loop thru each worksheet in spreadsheet
89643ba6	245	for sheet in sheets:
5890b89c JH	246	# GET sheet_json_schema for each worksheet (from function above)
5890b89c JH	247	sheet_json_schema, columns = get_sheet_metadata(sheet, spreadsheet_id, client)
99424fee	248	LOGGER.info('columns = {}'.format(columns))
89643ba6 JH	249
89643ba6 JH	250	sheet_title = sheet.get('properties', {}).get('title')
5890b89c	251	schemas[sheet_title] = sheet_json_schema
89643ba6 JH	252	sheet_mdata = metadata.new()
89643ba6 JH	253	sheet_mdata = metadata.get_standard_metadata(
5890b89c	254	schema=sheet_json_schema,
89643ba6 JH	255	key_properties=['__sdc_row'],
	256	valid_replication_keys=None,
	257	replication_method='FULL_TABLE'
	258	)
	259	field_metadata[sheet_title] = sheet_mdata
99424fee	260
89643ba6	261	return schemas, field_metadata