def schema_for_column(c, pks_for_table):
# Return Schema(None) to avoid calling lower() on a column with no datatype
if c.data_type is None:
LOGGER.info('Skipping column %s since it had no datatype',
c.column_name)
return Schema(None)
data_type = c.data_type.lower()
result = Schema()
# Scale of None indicates default of 6 digits
numeric_scale = c.numeric_scale
if data_type == 'number' and numeric_scale is not None and numeric_scale <= 0:
result.type = nullable_column(c.column_name, 'integer', pks_for_table)
return result
elif data_type == 'number':
# NB: Due to scale and precision variations in Oracle version, and
# among numeric types, we're using a custom `singer.decimal` string
# formatter for this, with no opinion on scale/precision.
result.type = nullable_column(c.column_name, 'string', pks_for_table)
result.format = 'singer.decimal'
return result
elif data_type == 'date' or data_type.startswith("timestamp"):
result.type = nullable_column(c.column_name, 'string', pks_for_table)
result.format = 'date-time'
return result
elif data_type in FLOAT_TYPES:
result.type = nullable_column(c.column_name, 'number', pks_for_table)
return result
elif data_type in STRING_TYPES:
character_used = c.character_used
result.type = nullable_column(c.column_name, 'string', pks_for_table)
if character_used == 'C':
result.maxLength = c.char_length
return result
#these column types are insane. they are NOT actually ieee754 floats
#instead they are represented as decimals, but despite this
#it appears we can say nothing about their max or min
#"float", "double_precision", "real"
elif data_type in ['float', 'double_precision']:
result.type = nullable_column(c.column_name, 'string', pks_for_table)
result.format = 'singer.decimal'
return result
return Schema(None)
def schema_for_column(c):
'''Returns the Schema object for the given Column.'''
data_type = c.data_type.lower()
inclusion = 'available'
result = Schema(inclusion=inclusion)
if data_type == 'boolean':
result.type = ['null', 'boolean']
elif data_type == 'number' or data_type == 'real' or data_type == 'float' or data_type == 'fixed':
result.type = ['null', 'number']
elif data_type == 'text':
result.type = ['null', 'string']
elif data_type == 'timestamp_ntz':
result.type = ['null', 'string']
result.format = 'date-time'
elif data_type == 'variant' or data_type == 'array':
result.type = ['null', 'string']
else:
result = Schema(None,
inclusion='unsupported',
description='Unsupported column type {}'.format(data_type))
return result
def schema_for_column(c):
'''Returns the Schema object for the given Column.'''
data_type = c.data_type.lower()
inclusion = 'available'
result = Schema(inclusion=inclusion)
if data_type == 'boolean':
result.type = ['null', 'boolean']
elif data_type in INTEGER_TYPES:
result.type = ['null', 'number']
elif data_type in FLOAT_TYPES:
result.type = ['null', 'number']
elif data_type in NUMBER_TYPES:
result.type = ['null', 'number']
elif data_type in STRING_TYPES:
result.type = ['null', 'string']
result.maxLength = c.character_maximum_length
elif data_type in DATETIME_TYPES:
result.type = ['null', 'string']
result.format = 'date-time'
elif data_type == 'time':
result.type = ['null', 'string']
result.format = 'time'
elif data_type in BINARY_TYPES:
result.type = ['null', 'string']
result.format = 'binary'
elif data_type in OBJECT_TYPES:
result.type = ['null', 'object', 'array']
else:
result = Schema(
None,
inclusion='unsupported',
description='Unsupported data type {}'.format(data_type))
return result
def schema_for_column(c):
'''Returns the Schema object for the given Column.'''
column_type = c['type'].lower()
column_nullable = c['nullable'].lower()
inclusion = 'available'
result = Schema(inclusion=inclusion)
if column_type == 'bool':
result.type = 'boolean'
elif column_type in BYTES_FOR_INTEGER_TYPE:
result.type = 'integer'
bits = BYTES_FOR_INTEGER_TYPE[column_type] * 8
result.minimum = 0 - 2**(bits - 1)
result.maximum = 2**(bits - 1) - 1
elif column_type in FLOAT_TYPES:
result.type = 'number'
elif column_type == 'numeric':
result.type = 'number'
elif column_type in STRING_TYPES:
result.type = 'string'
elif column_type in DATETIME_TYPES:
result.type = 'string'
result.format = 'date-time'
elif column_type in DATE_TYPES:
result.type = 'string'
result.format = 'date'
else:
result = Schema(
None,
inclusion='unsupported',
description='Unsupported column type {}'.format(column_type))
if column_nullable == 'yes':
result.type = ['null', result.type]
return result
def schema_for_column(c):
"""Returns the Schema object for the given Column."""
data_type = c.data_type.lower()
inclusion = "available"
if c.is_primary_key == 1:
inclusion = "automatic"
result = Schema(inclusion=inclusion)
if data_type == "bit":
result.type = ["null", "boolean"]
#In SQL Server tinyint is unsigned.
elif data_type in BYTES_FOR_UNSIGNED_INTEGER_TYPE:
result.type = ["null", "integer"]
bits = BYTES_FOR_UNSIGNED_INTEGER_TYPE[data_type] * 8
result.minimum = 0
result.maximum = 2**bits - 1
elif data_type in BYTES_FOR_INTEGER_TYPE:
result.type = ["null", "integer"]
bits = BYTES_FOR_INTEGER_TYPE[data_type] * 8
result.minimum = 0 - 2**(bits - 1)
result.maximum = 2**(bits - 1) - 1
elif data_type in FLOAT_TYPES:
result.type = ["null", "number"]
elif data_type in PRECISE_NUMERIC_TYPES:
result.type = ["null", "number"]
result.multipleOf = 10**(0 - c.numeric_scale)
return result
elif data_type in STRING_TYPES:
result.type = ["null", "string"]
if c.character_maximum_length is not None and c.character_maximum_length > 0:
result.maxLength = c.character_maximum_length
elif data_type in DATETIME_TYPES:
result.type = ["null", "string"]
result.format = "date-time"
elif data_type in VARIANT_TYPES:
result.type = ["null", "object"]
else:
result = Schema(
None,
inclusion="unsupported",
description="Unsupported column type",
)
return result
def schema_for_column(c):
'''Returns the Schema object for the given Column.'''
data_type = c.data_type.lower()
column_type = c.column_type.lower()
inclusion = 'available'
# We want to automatically include all primary key columns
if c.column_key.lower() == 'pri':
inclusion = 'automatic'
result = Schema(inclusion=inclusion)
if data_type == 'bit' or column_type.startswith('tinyint(1)'):
result.type = ['null', 'boolean']
elif data_type in BYTES_FOR_INTEGER_TYPE:
result.type = ['null', 'integer']
bits = BYTES_FOR_INTEGER_TYPE[data_type] * 8
if 'unsigned' in c.column_type:
result.minimum = 0
result.maximum = 2**bits - 1
else:
result.minimum = 0 - 2**(bits - 1)
result.maximum = 2**(bits - 1) - 1
elif data_type in FLOAT_TYPES:
result.type = ['null', 'number']
elif data_type == 'decimal':
result.type = ['null', 'number']
result.exclusiveMaximum = True
result.maximum = 10**(c.numeric_precision - c.numeric_scale)
result.multipleOf = 10**(0 - c.numeric_scale)
if 'unsigned' in column_type:
result.minimum = 0
else:
result.exclusiveMinimum = True
result.minimum = -10**(c.numeric_precision - c.numeric_scale)
return result
elif data_type in STRING_TYPES:
result.type = ['null', 'string']
result.maxLength = c.character_maximum_length
elif data_type in DATETIME_TYPES:
result.type = ['null', 'string']
result.format = 'date-time'
else:
result = Schema(
None,
inclusion='unsupported',
description='Unsupported column type {}'.format(column_type))
return result
def schema_for_column(c):
"""Returns the Schema object for the given Column."""
data_type = c.data_type.lower()
column_type = c.column_type.lower()
inclusion = "available"
# We want to automatically include all primary key columns
if c.column_key.lower() == "pri":
inclusion = "automatic"
result = Schema(inclusion=inclusion)
if data_type == "bit" or column_type.startswith("tinyint(1)"):
result.type = ["null", "boolean"]
elif data_type in BYTES_FOR_INTEGER_TYPE:
result.type = ["null", "integer"]
bits = BYTES_FOR_INTEGER_TYPE[data_type] * 8
if "unsigned" in c.column_type:
result.minimum = 0
result.maximum = 2 ** bits - 1
else:
result.minimum = 0 - 2 ** (bits - 1)
result.maximum = 2 ** (bits - 1) - 1
elif data_type in FLOAT_TYPES:
result.type = ["null", "number"]
result.multipleOf = 10 ** (0 - 6)
elif data_type == "decimal":
result.type = ["null", "number"]
result.multipleOf = 10 ** (0 - c.numeric_scale)
return result
elif data_type in STRING_TYPES:
result.type = ["null", "string"]
result.maxLength = c.character_maximum_length
elif data_type in DATETIME_TYPES:
result.type = ["null", "string"]
result.format = "date-time"
elif data_type in VARIANT_TYPES:
result.type = ["null", "object"]
else:
result = Schema(
None,
inclusion="unsupported",
description="Unsupported column type {}".format(column_type),
)
return result
def schema_for_column(c):
data_type = c.sql_data_type.lower()
result = Schema()
if data_type in INTEGER_TYPES:
result.type = nullable_column('integer', c.is_primary_key)
result.minimum = -1 * (2**(c.numeric_precision - 1))
result.maximum = 2**(c.numeric_precision - 1) - 1
return result
elif data_type == 'bit' and c.character_maximum_length == 1:
result.type = nullable_column('boolean', c.is_primary_key)
return result
elif data_type == 'boolean':
result.type = nullable_column('boolean', c.is_primary_key)
return result
elif data_type == 'uuid':
result.type = nullable_column('string', c.is_primary_key)
return result
elif data_type == 'hstore':
result.type = nullable_column('string', c.is_primary_key)
return result
elif data_type == 'citext':
result.type = nullable_column('string', c.is_primary_key)
return result
elif data_type in JSON_TYPES:
result.type = nullable_column('string', c.is_primary_key)
return result
elif data_type == 'numeric':
result.type = nullable_column('number', c.is_primary_key)
if c.numeric_scale is None or c.numeric_scale > MAX_SCALE:
LOGGER.warning(
'capping decimal scale to 38. THIS MAY CAUSE TRUNCATION')
scale = MAX_SCALE
else:
scale = c.numeric_scale
if c.numeric_precision is None or c.numeric_precision > MAX_PRECISION:
LOGGER.warning(
'capping decimal precision to 100. THIS MAY CAUSE TRUNCATION')
precision = MAX_PRECISION
else:
precision = c.numeric_precision
result.exclusiveMaximum = True
result.maximum = 10**(precision - scale)
result.multipleOf = 10**(0 - scale)
result.exclusiveMinimum = True
result.minimum = -10**(precision - scale)
return result
elif data_type in {'time without time zone', 'time with time zone'}:
#times are treated as ordinary strings as they can not possible match RFC3339
result.type = nullable_column('string', c.is_primary_key)
return result
elif data_type in ('date', 'timestamp without time zone',
'timestamp with time zone'):
result.type = nullable_column('string', c.is_primary_key)
result.format = 'date-time'
return result
elif data_type in FLOAT_TYPES:
result.type = nullable_column('number', c.is_primary_key)
return result
elif data_type == 'text':
result.type = nullable_column('string', c.is_primary_key)
return result
elif data_type == 'character varying':
result.type = nullable_column('string', c.is_primary_key)
result.maxLength = c.character_maximum_length
return result
elif data_type == 'character':
result.type = nullable_column('string', c.is_primary_key)
result.maxLength = c.character_maximum_length
return result
return Schema(None)
def schema_for_column(c, pks_for_table):
data_type = c.data_type.lower()
result = Schema()
numeric_scale = c.numeric_scale or DEFAULT_NUMERIC_SCALE
numeric_precision = c.numeric_precision or DEFAULT_NUMERIC_PRECISION
if data_type == 'number' and numeric_scale <= 0:
result.type = nullable_column(c.column_name, 'integer', pks_for_table)
result.minimum = -1 * (10**numeric_precision - 1)
result.maximum = (10**numeric_precision - 1)
if numeric_scale < 0:
result.multipleOf = -10 * numeric_scale
return result
elif data_type == 'number':
result.type = nullable_column(c.column_name, 'number', pks_for_table)
result.exclusiveMaximum = True
result.maximum = 10**(numeric_precision - numeric_scale)
result.multipleOf = 10**(0 - numeric_scale)
result.exclusiveMinimum = True
result.minimum = -10**(numeric_precision - numeric_scale)
return result
elif data_type == 'date' or data_type.startswith("timestamp"):
result.type = nullable_column(c.column_name, 'string', pks_for_table)
result.format = 'date-time'
return result
elif data_type in FLOAT_TYPES:
result.type = nullable_column(c.column_name, 'number', pks_for_table)
return result
elif data_type in STRING_TYPES:
character_used = c.character_used
result.type = nullable_column(c.column_name, 'string', pks_for_table)
if character_used == 'C':
result.maxLength = c.char_length
return result
#these column types are insane. they are NOT actually ieee754 floats
#instead they are represented as decimals, but despite this
#it appears we can say nothing about their max or min
#"real"
elif data_type == 'float' and c.numeric_precision == 63:
result.type = nullable_column(c.column_name, 'number', pks_for_table)
result.multipleOf = 10**-18
return result
#"float", "double_precision",
elif data_type in ['float', 'double_precision']:
result.type = nullable_column(c.column_name, 'number', pks_for_table)
result.multipleOf = 10**-38
return result
return Schema(None)
def discover_catalog(conn):
"""Returns a Catalog describing the table structure of the target database"""
entries = []
for table in conn.get_tables():
# the stream is in format database_name__table_name with all non alphanumeric
# and `_` characters replaced with an `_`.
stream = re.sub(
'[^0-9a-z_]+',
'_',
"{}__{}".format(table.get('database_name').lower(), table.get('name')).lower()
)
# by default we will ALWAYS have 'rid' as an automatically included primary key field.
schema = Schema(
type='object',
additionalProperties=False,
properties={
'rid': Schema(
type=['string'],
inclusion='automatic',
)
}
)
metadata = []
for field in conn.get_fields(table.get('id')):
field_type = ['null']
field_format = None
# https://help.quickbase.com/user-assistance/field_types.html
if field.get('base_type') == 'bool':
field_type.append('boolean')
elif field.get('base_type') == 'float':
field_type.append('number')
elif field.get('base_type') == 'int64':
if field.get('type') in ('timestamp', 'date'):
field_type.append('string')
field_format = 'date-time'
else:
# `timeofday` comes out of the API as an integer for how many milliseconds
# through the day, 900000 would be 12:15am
# `duration` comes out as an integer for how many milliseconds the duration is,
# 1000 would be 1 second
# let's just pass these as an integer
field_type.append('integer')
else:
field_type.append('string')
property_schema = Schema(
type=field_type,
inclusion='available' if field.get('id') != '2' else 'automatic',
)
if field_format is not None:
property_schema.format = field_format
schema.properties[field.get('name')] = property_schema
metadata.append({
'metadata': {
'id': field.get('id')
},
'breadcrumb': [
'properties',
field.get('name')
]
})
entry = CatalogEntry(
database=conn.appid,
table=table.get('id'),
stream_alias=table.get('name'),
stream=stream,
tap_stream_id=stream,
key_properties=['rid'],
schema=schema,
metadata=metadata
)
entries.append(entry)
return Catalog(entries)
