def __init__(self):
# Bounds are (min, max) values, the actual value used will be selected from the
# bounds and each value within the range has an equal probability of being selected.
self._bounds = {
'MAX_NESTED_QUERY_COUNT': (0, 2),
'MAX_NESTED_EXPR_COUNT': (0, 2),
'SELECT_ITEM_COUNT': (1, 5),
'WITH_TABLE_COUNT': (1, 3),
'TABLE_COUNT': (1, 2),
'ANALYTIC_LEAD_LAG_OFFSET': (1, 100),
'ANALYTIC_WINDOW_OFFSET': (1, 100)
}
# Below are interdependent weights used to determine probabilities. The probability
# of any item being selected should be (item weight) / sum(weights). A weight of
# zero means the item will never be selected.
self._weights = {
'SELECT_ITEM_CATEGORY': {
'AGG': 3,
'ANALYTIC': 1,
'BASIC': 10
},
'TYPES': {
Boolean: 1,
Char: 1,
Decimal: 1,
Float: 1,
Int: 10,
Timestamp: 1
},
'ANALYTIC_WINDOW': {
('ROWS', UNBOUNDED_PRECEDING, None): 1,
('ROWS', UNBOUNDED_PRECEDING, PRECEDING): 2,
('ROWS', UNBOUNDED_PRECEDING, CURRENT_ROW): 1,
('ROWS', UNBOUNDED_PRECEDING, FOLLOWING): 2,
('ROWS', UNBOUNDED_PRECEDING, UNBOUNDED_FOLLOWING): 2,
('ROWS', PRECEDING, None): 1,
('ROWS', PRECEDING, PRECEDING): 2,
('ROWS', PRECEDING, CURRENT_ROW): 1,
('ROWS', PRECEDING, FOLLOWING): 2,
('ROWS', PRECEDING, UNBOUNDED_FOLLOWING): 2,
('ROWS', CURRENT_ROW, None): 1,
('ROWS', CURRENT_ROW, CURRENT_ROW): 1,
('ROWS', CURRENT_ROW, FOLLOWING): 2,
('ROWS', CURRENT_ROW, UNBOUNDED_FOLLOWING): 2,
('ROWS', FOLLOWING, FOLLOWING): 2,
('ROWS', FOLLOWING, UNBOUNDED_FOLLOWING): 2,
# Ranges not yet supported
('RANGE', UNBOUNDED_PRECEDING, None): 0,
('RANGE', UNBOUNDED_PRECEDING, PRECEDING): 0,
('RANGE', UNBOUNDED_PRECEDING, CURRENT_ROW): 0,
('RANGE', UNBOUNDED_PRECEDING, FOLLOWING): 0,
('RANGE', UNBOUNDED_PRECEDING, UNBOUNDED_FOLLOWING): 0,
('RANGE', PRECEDING, None): 0,
('RANGE', PRECEDING, PRECEDING): 0,
('RANGE', PRECEDING, CURRENT_ROW): 0,
('RANGE', PRECEDING, FOLLOWING): 0,
('RANGE', PRECEDING, UNBOUNDED_FOLLOWING): 0,
('RANGE', CURRENT_ROW, None): 0,
('RANGE', CURRENT_ROW, CURRENT_ROW): 0,
('RANGE', CURRENT_ROW, FOLLOWING): 0,
('RANGE', CURRENT_ROW, UNBOUNDED_FOLLOWING): 0,
('RANGE', FOLLOWING, FOLLOWING): 0,
('RANGE', FOLLOWING, UNBOUNDED_FOLLOWING): 0
},
'JOIN': {
'INNER': 90,
'LEFT': 30,
'RIGHT': 10,
'FULL_OUTER': 3,
'CROSS': 1
},
'SUBQUERY_PREDICATE': {
('Exists', 'AGG', 'CORRELATED'): 0, # Not supported
('Exists', 'AGG', 'UNCORRELATED'): 1,
('Exists', 'NON_AGG', 'CORRELATED'): 1,
('Exists', 'NON_AGG', 'UNCORRELATED'): 1,
('NotExists', 'AGG', 'CORRELATED'): 0, # Not supported
('NotExists', 'AGG', 'UNCORRELATED'): 0, # Not supported
('NotExists', 'NON_AGG', 'CORRELATED'): 1,
('NotExists', 'NON_AGG', 'UNCORRELATED'): 0, # Not supported
('In', 'AGG', 'CORRELATED'): 0, # Not supported
('In', 'AGG', 'UNCORRELATED'): 0, # Not supported
('In', 'NON_AGG', 'CORRELATED'): 1,
('In', 'NON_AGG', 'UNCORRELATED'): 1,
('NotIn', 'AGG', 'CORRELATED'): 0, # Not supported
('NotIn', 'AGG', 'UNCORRELATED'): 1,
('NotIn', 'NON_AGG', 'CORRELATED'): 1,
('NotIn', 'NON_AGG', 'UNCORRELATED'): 1,
('Scalar', 'AGG', 'CORRELATED'): 0, # Not supported
('Scalar', 'AGG', 'UNCORRELATED'): 1,
('Scalar', 'NON_AGG', 'CORRELATED'): 0, # Not supported
('Scalar', 'NON_AGG', 'UNCORRELATED'): 1
},
'QUERY_EXECUTION': { # Used by the discrepancy searcher
'CREATE_TABLE_AS': 1,
'RAW': 10,
'VIEW': 1
}
}
# On/off switches
self._flags = {
'ANALYTIC_DESIGNS': {
'TOP_LEVEL_QUERY_WITHOUT_LIMIT': True,
'DETERMINISTIC_ORDER_BY': True,
'NO_ORDER_BY': True,
'ONLY_SELECT_ITEM': True,
'UNBOUNDED_WINDOW': True,
'RANK_FUNC': True
}
}
# Independent probabilities where 1 means 100%. These values may be ignored depending
# on the context. For example, GROUP_BY is almost always ignored and instead
# determined by the SELECT item weights above, since mixing aggregate and
# non-aggregate items requires the use of a GROUP BY. The GROUP_BY option below is
# only applied if all of the SELECT items are non-aggregate.
self._probabilities = {
'OPTIONAL_QUERY_CLAUSES': {
'WITH': 0.1, # MAX_NESTED_QUERY_COUNT bounds take precedence
'FROM': 1,
'WHERE': 0.5,
'GROUP_BY':
0.1, # special case, doesn't really do much, see comment above
'HAVING': 0.25,
'UNION': 0.1,
'ORDER_BY': 0.1
},
'OPTIONAL_ANALYTIC_CLAUSES': {
'PARTITION_BY': 0.5,
'ORDER_BY': 0.5,
'WINDOW': 0.5
}, # will only be used if ORDER BY is chosen
'MISC': {
'INLINE_VIEW':
0.1, # MAX_NESTED_QUERY_COUNT bounds take precedence
'SELECT_DISTINCT': 0.1,
'SCALAR_SUBQUERY': 0.1,
'UNION_ALL': 0.5
}
} # Determines use of "ALL" but not "UNION"
self.__type_weights = {}
self.constant_generator = RandomValGenerator()
class DefaultProfile(object):
def __init__(self):
# Bounds are (min, max) values, the actual value used will be selected from the
# bounds and each value within the range has an equal probability of being selected.
self._bounds = {
'MAX_NESTED_QUERY_COUNT': (0, 2),
'MAX_NESTED_EXPR_COUNT': (0, 2),
'SELECT_ITEM_COUNT': (1, 5),
'WITH_TABLE_COUNT': (1, 3),
'TABLE_COUNT': (1, 2),
'ANALYTIC_LEAD_LAG_OFFSET': (1, 100),
'ANALYTIC_WINDOW_OFFSET': (1, 100)
}
# Below are interdependent weights used to determine probabilities. The probability
# of any item being selected should be (item weight) / sum(weights). A weight of
# zero means the item will never be selected.
self._weights = {
'SELECT_ITEM_CATEGORY': {
'AGG': 3,
'ANALYTIC': 1,
'BASIC': 10
},
'TYPES': {
Boolean: 1,
Char: 1,
Decimal: 1,
Float: 1,
Int: 10,
Timestamp: 1
},
'ANALYTIC_WINDOW': {
('ROWS', UNBOUNDED_PRECEDING, None): 1,
('ROWS', UNBOUNDED_PRECEDING, PRECEDING): 2,
('ROWS', UNBOUNDED_PRECEDING, CURRENT_ROW): 1,
('ROWS', UNBOUNDED_PRECEDING, FOLLOWING): 2,
('ROWS', UNBOUNDED_PRECEDING, UNBOUNDED_FOLLOWING): 2,
('ROWS', PRECEDING, None): 1,
('ROWS', PRECEDING, PRECEDING): 2,
('ROWS', PRECEDING, CURRENT_ROW): 1,
('ROWS', PRECEDING, FOLLOWING): 2,
('ROWS', PRECEDING, UNBOUNDED_FOLLOWING): 2,
('ROWS', CURRENT_ROW, None): 1,
('ROWS', CURRENT_ROW, CURRENT_ROW): 1,
('ROWS', CURRENT_ROW, FOLLOWING): 2,
('ROWS', CURRENT_ROW, UNBOUNDED_FOLLOWING): 2,
('ROWS', FOLLOWING, FOLLOWING): 2,
('ROWS', FOLLOWING, UNBOUNDED_FOLLOWING): 2,
# Ranges not yet supported
('RANGE', UNBOUNDED_PRECEDING, None): 0,
('RANGE', UNBOUNDED_PRECEDING, PRECEDING): 0,
('RANGE', UNBOUNDED_PRECEDING, CURRENT_ROW): 0,
('RANGE', UNBOUNDED_PRECEDING, FOLLOWING): 0,
('RANGE', UNBOUNDED_PRECEDING, UNBOUNDED_FOLLOWING): 0,
('RANGE', PRECEDING, None): 0,
('RANGE', PRECEDING, PRECEDING): 0,
('RANGE', PRECEDING, CURRENT_ROW): 0,
('RANGE', PRECEDING, FOLLOWING): 0,
('RANGE', PRECEDING, UNBOUNDED_FOLLOWING): 0,
('RANGE', CURRENT_ROW, None): 0,
('RANGE', CURRENT_ROW, CURRENT_ROW): 0,
('RANGE', CURRENT_ROW, FOLLOWING): 0,
('RANGE', CURRENT_ROW, UNBOUNDED_FOLLOWING): 0,
('RANGE', FOLLOWING, FOLLOWING): 0,
('RANGE', FOLLOWING, UNBOUNDED_FOLLOWING): 0
},
'JOIN': {
'INNER': 90,
'LEFT': 30,
'RIGHT': 10,
'FULL_OUTER': 3,
'CROSS': 1
},
'SUBQUERY_PREDICATE': {
('Exists', 'AGG', 'CORRELATED'): 0, # Not supported
('Exists', 'AGG', 'UNCORRELATED'): 1,
('Exists', 'NON_AGG', 'CORRELATED'): 1,
('Exists', 'NON_AGG', 'UNCORRELATED'): 1,
('NotExists', 'AGG', 'CORRELATED'): 0, # Not supported
('NotExists', 'AGG', 'UNCORRELATED'): 0, # Not supported
('NotExists', 'NON_AGG', 'CORRELATED'): 1,
('NotExists', 'NON_AGG', 'UNCORRELATED'): 0, # Not supported
('In', 'AGG', 'CORRELATED'): 0, # Not supported
('In', 'AGG', 'UNCORRELATED'): 0, # Not supported
('In', 'NON_AGG', 'CORRELATED'): 1,
('In', 'NON_AGG', 'UNCORRELATED'): 1,
('NotIn', 'AGG', 'CORRELATED'): 0, # Not supported
('NotIn', 'AGG', 'UNCORRELATED'): 1,
('NotIn', 'NON_AGG', 'CORRELATED'): 1,
('NotIn', 'NON_AGG', 'UNCORRELATED'): 1,
('Scalar', 'AGG', 'CORRELATED'): 0, # Not supported
('Scalar', 'AGG', 'UNCORRELATED'): 1,
('Scalar', 'NON_AGG', 'CORRELATED'): 0, # Not supported
('Scalar', 'NON_AGG', 'UNCORRELATED'): 1
},
'QUERY_EXECUTION': { # Used by the discrepancy searcher
'CREATE_TABLE_AS': 1,
'RAW': 10,
'VIEW': 1
}
}
# On/off switches
self._flags = {
'ANALYTIC_DESIGNS': {
'TOP_LEVEL_QUERY_WITHOUT_LIMIT': True,
'DETERMINISTIC_ORDER_BY': True,
'NO_ORDER_BY': True,
'ONLY_SELECT_ITEM': True,
'UNBOUNDED_WINDOW': True,
'RANK_FUNC': True
}
}
# Independent probabilities where 1 means 100%. These values may be ignored depending
# on the context. For example, GROUP_BY is almost always ignored and instead
# determined by the SELECT item weights above, since mixing aggregate and
# non-aggregate items requires the use of a GROUP BY. The GROUP_BY option below is
# only applied if all of the SELECT items are non-aggregate.
self._probabilities = {
'OPTIONAL_QUERY_CLAUSES': {
'WITH': 0.1, # MAX_NESTED_QUERY_COUNT bounds take precedence
'FROM': 1,
'WHERE': 0.5,
'GROUP_BY':
0.1, # special case, doesn't really do much, see comment above
'HAVING': 0.25,
'UNION': 0.1,
'ORDER_BY': 0.1
},
'OPTIONAL_ANALYTIC_CLAUSES': {
'PARTITION_BY': 0.5,
'ORDER_BY': 0.5,
'WINDOW': 0.5
}, # will only be used if ORDER BY is chosen
'MISC': {
'INLINE_VIEW':
0.1, # MAX_NESTED_QUERY_COUNT bounds take precedence
'SELECT_DISTINCT': 0.1,
'SCALAR_SUBQUERY': 0.1,
'UNION_ALL': 0.5
}
} # Determines use of "ALL" but not "UNION"
self.__type_weights = {}
self.constant_generator = RandomValGenerator()
def _get_config_value(self, start_config, *keys):
value = start_config
for key in keys:
value = value[key]
return value
def weights(self, *keys):
'''Convenience method for getting the values of named weights'''
return self._get_config_value(self._weights, *keys)
def bounds(self, *keys):
'''Convenience method for getting the values of named bounds'''
return self._get_config_value(self._bounds, *keys)
def probability(self, *keys):
'''Convenience method for getting the value of named probabilities'''
return self._get_config_value(self._probabilities, *keys)
def _choose_from_bounds(self, *bounds):
'''Returns a value that is within the given bounds. Each value has an equal chance
of being chosen.
'''
if isinstance(bounds[0], str):
lower, upper = self.bounds(*bounds)
else:
lower, upper = bounds
return randint(lower, upper)
def _choose_from_weights(self, *weights):
'''Returns a value that is selected from the keys of weights with the probability
determined by the values of weights.
'''
if isinstance(weights[0], str):
weights = self.weights(*weights)
else:
weights = weights[0]
total_weight = sum(weights.itervalues())
numeric_choice = randint(1, total_weight)
for choice_, weight in weights.iteritems():
if weight <= 0:
continue
if numeric_choice <= weight:
return choice_
numeric_choice -= weight
def _choose_from_filtered_weights(self, filter, *weights):
'''Convenience method, apply the given filter before choosing a value.'''
if isinstance(weights[0], str):
weights = self.weights(*weights)
else:
weights = weights[0]
return self._choose_from_weights(
dict((choice_, weight) for choice_, weight in weights.iteritems()
if filter(choice_)))
def _decide_from_probability(self, *keys):
return random() < self.probability(*keys)
def get_max_nested_query_count(self):
'''Return the maximum number of queries the top level query may contain.'''
return self._choose_from_bounds('MAX_NESTED_QUERY_COUNT')
def use_with_clause(self):
return self._decide_from_probability('OPTIONAL_QUERY_CLAUSES', 'WITH')
def get_with_clause_table_ref_count(self):
'''Return the number of table ref entries a WITH clause should contain.'''
return self._choose_from_bounds('WITH_TABLE_COUNT')
def get_select_item_count(self):
return self._choose_from_bounds('SELECT_ITEM_COUNT')
def choose_nested_expr_count(self):
return self._choose_from_bounds('MAX_NESTED_EXPR_COUNT')
def allowed_analytic_designs(self):
return [
design for design, is_enabled in
self._flags['ANALYTIC_DESIGNS'].iteritems() if is_enabled
]
def use_partition_by_clause_in_analytic(self):
return self._decide_from_probability('OPTIONAL_ANALYTIC_CLAUSES',
'PARTITION_BY')
def use_order_by_clause_in_analytic(self):
return self._decide_from_probability('OPTIONAL_ANALYTIC_CLAUSES',
'ORDER_BY')
def use_window_in_analytic(self):
return self._decide_from_probability('OPTIONAL_ANALYTIC_CLAUSES',
'WINDOW')
def choose_window_type(self):
return self._choose_from_weights('ANALYTIC_WINDOW')
def get_window_offset(self):
return self._choose_from_bounds('ANALYTIC_WINDOW_OFFSET')
def get_offset_for_analytic_lead_or_lag(self):
return self._choose_from_bounds('ANALYTIC_LEAD_LAG_OFFSET')
def get_table_count(self):
return self._choose_from_bounds('TABLE_COUNT')
def use_inline_view(self):
return self._decide_from_probability('MISC', 'INLINE_VIEW')
def choose_table(self, table_exprs):
return choice(table_exprs)
def choose_join_type(self, join_types):
return self._choose_from_filtered_weights(
lambda join_type: join_type in join_types, 'JOIN')
def get_join_condition_count(self):
return self._choose_from_bounds('MAX_NESTED_EXPR_COUNT')
def use_where_clause(self):
return self._decide_from_probability('OPTIONAL_QUERY_CLAUSES', 'WHERE')
def use_scalar_subquery(self):
return self._decide_from_probability('MISC', 'SCALAR_SUBQUERY')
def choose_subquery_predicate_category(self, func_name, allow_correlated):
weights = self.weights('SUBQUERY_PREDICATE')
func_names = set(name for name, _, _ in weights.iterkeys())
if func_name not in func_names:
func_name = 'Scalar'
allow_agg = self.weights('SELECT_ITEM_CATEGORY').get('AGG', 0)
if allow_correlated and self.bounds('TABLE_COUNT')[1] == 0:
allow_correlated = False
weights = dict(((name, use_agg, use_correlated), weight)
for (name, use_agg, use_correlated), weight in weights.iteritems()
if name == func_name \
and (allow_agg or use_agg == 'NON_AGG') \
and weight)
if weights:
return self._choose_from_weights(weights)
def use_distinct(self):
return self._decide_from_probability('MISC', 'SELECT_DISTINCT')
def use_distinct_in_func(self):
return self._decide_from_probability('MISC', 'SELECT_DISTINCT')
def use_group_by_clause(self):
return self._decide_from_probability('OPTIONAL_QUERY_CLAUSES',
'GROUP_BY')
def use_having_clause(self):
return self._decide_from_probability('OPTIONAL_QUERY_CLAUSES',
'HAVING')
def use_union_clause(self):
return self._decide_from_probability('OPTIONAL_QUERY_CLAUSES', 'UNION')
def use_union_all(self):
return self._decide_from_probability('MISC', 'UNION_ALL')
def get_query_execution(self):
return self._choose_from_weights('QUERY_EXECUTION')
def use_having_without_groupby(self):
return True
def use_nested_with(self):
return True
# Workaround for Hive null ordering differences, and lack of 'NULL FIRST', 'NULL LAST'
# specifications. The ref db will order nulls as specified for ASC sorting to make it
# identifical to Hive. Valid return values are: 'BEFORE', 'AFTER', or 'DEFAULT',
# the latter means no specification needed.
def nulls_order_asc(self):
return 'DEFAULT'
def choose_val_expr(self, val_exprs, types=TYPES):
if not val_exprs:
raise Exception('At least on value is required')
if not types:
raise Exception('At least one type is required')
available_types = set(types) & set(val_exprs.by_type)
if not available_types:
raise Exception(
'None of the provided values return any of the required types')
val_type = self.choose_type(available_types)
return choice(val_exprs.by_type[val_type])
def choose_constant(self, return_type=None, allow_null=True):
if not return_type:
return_type = self.choose_type()
while True:
val = self.constant_generator.generate_val(return_type)
if val is None and not allow_null:
continue
return return_type(val)
def choose_type(self, types=TYPES):
type_weights = self.weights('TYPES')
weights = dict((type_, type_weights[type_]) for type_ in types)
if not weights:
raise Exception('None of the requested types are enabled')
return self._choose_from_weights(weights)
def choose_func_signature(self, signatures):
'''Return a signature chosen from "signatures".'''
if not signatures:
raise Exception('At least one signature is required')
type_weights = self.weights('TYPES')
# First a function will be chosen then a signature. This is done so that the number
# of signatures a function has doesn't influence its likelihood of being chosen.
# Functions will be weighted based on the weight of the types in their arguments.
# The weights will be normalized by the number of arguments in the signature. The
# weight of a function will be the maximum weight out of all of it's signatures.
# If any signature has a type with a weight of zero, the signature will not be used.
#
# Example: type_weights = {Int: 10, Float: 1},
# funcs = [foo(Int), foo(Float), bar(Int, Float)]
#
# max signature length = 2 # from bar(Int, Float)
# weight of foo(Int) = (10 * 2)
# weight of foo(Float) = (1 * 2)
# weight of bar(Int, Float) = ((10 + 1) * 1)
# func_weights = {foo: 20, bar: 11}
#
# Note that this only selects a function, the function signature will be selected
# later. This is done to prevent function with a greater number of signatures from
# being selected more frequently.
func_weights = dict()
# The length of the signature in func_weights
signature_length_by_func = dict()
for signature in signatures:
signature_weight = type_weights[signature.return_type]
signature_length = 1
for arg in signature.args:
if arg.is_subquery:
for subtype in arg.type:
signature_weight *= type_weights[subtype]
signature_length += 1
else:
signature_weight *= type_weights[arg.type]
signature_length += 1
if not signature_weight:
continue
if not signature.func in func_weights \
or signature_weight > func_weights[signature.func]:
func_weights[signature.func] = signature_weight
signature_length_by_func[signature.func] = signature_length
if not func_weights:
raise Exception(
'All functions disallowed based on signature types')
distinct_signature_lengths = set(signature_length_by_func.values())
for func, weight in func_weights.iteritems():
signature_length = signature_length_by_func[func]
func_weights[func] = reduce(
lambda x, y: x * y,
distinct_signature_lengths - set([signature_length]),
func_weights[func])
func = self._choose_from_weights(func_weights)
# Same idea as above but for the signatures of the selected function.
signature_weights = dict()
signature_lengths = dict()
for idx, signature in enumerate(func.signatures()):
if signature not in signatures:
continue
signature_weight = type_weights[signature.return_type]
signature_length = 1
for arg in signature.args:
if arg.is_subquery:
for subtype in arg.type:
signature_weight *= type_weights[subtype]
signature_length += 1
else:
signature_weight *= type_weights[arg.type]
signature_length += 1
if signature_weight:
signature_weights[idx] = signature_weight
signature_lengths[idx] = signature_length
distinct_signature_lengths = set(signature_lengths.values())
for idx, weight in signature_weights.iteritems():
signature_length = signature_lengths[idx]
signature_weights[idx] = reduce(
lambda x, y: x * y,
distinct_signature_lengths - set([signature_length]),
signature_weights[idx])
idx = self._choose_from_weights(signature_weights)
return func.signatures()[idx]
def allow_func_signature(self, signature):
weights = self.weights('TYPES')
if not weights[signature.return_type]:
return False
for arg in signature.args:
if arg.is_subquery:
if not all(weights[subtype] for subtype in arg.type):
return False
elif not weights[arg.type]:
return False
return True
def __init__(self):
# Bounds are (min, max) values, the actual value used will be selected from the
# bounds and each value within the range has an equal probability of being selected.
self._bounds = {
"MAX_NESTED_QUERY_COUNT": (0, 2),
"MAX_NESTED_EXPR_COUNT": (0, 2),
"SELECT_ITEM_COUNT": (1, 5),
"WITH_TABLE_COUNT": (1, 3),
"TABLE_COUNT": (1, 2),
"ANALYTIC_LEAD_LAG_OFFSET": (1, 100),
"ANALYTIC_WINDOW_OFFSET": (1, 100),
}
# Below are interdependent weights used to determine probabilities. The probability
# of any item being selected should be (item weight) / sum(weights). A weight of
# zero means the item will never be selected.
self._weights = {
"SELECT_ITEM_CATEGORY": {"AGG": 3, "ANALYTIC": 1, "BASIC": 10},
"TYPES": {Boolean: 1, Char: 1, Decimal: 1, Float: 1, Int: 10, Timestamp: 1},
"ANALYTIC_WINDOW": {
("ROWS", UNBOUNDED_PRECEDING, None): 1,
("ROWS", UNBOUNDED_PRECEDING, PRECEDING): 2,
("ROWS", UNBOUNDED_PRECEDING, CURRENT_ROW): 1,
("ROWS", UNBOUNDED_PRECEDING, FOLLOWING): 2,
("ROWS", UNBOUNDED_PRECEDING, UNBOUNDED_FOLLOWING): 2,
("ROWS", PRECEDING, None): 1,
("ROWS", PRECEDING, PRECEDING): 2,
("ROWS", PRECEDING, CURRENT_ROW): 1,
("ROWS", PRECEDING, FOLLOWING): 2,
("ROWS", PRECEDING, UNBOUNDED_FOLLOWING): 2,
("ROWS", CURRENT_ROW, None): 1,
("ROWS", CURRENT_ROW, CURRENT_ROW): 1,
("ROWS", CURRENT_ROW, FOLLOWING): 2,
("ROWS", CURRENT_ROW, UNBOUNDED_FOLLOWING): 2,
("ROWS", FOLLOWING, FOLLOWING): 2,
("ROWS", FOLLOWING, UNBOUNDED_FOLLOWING): 2,
# Ranges not yet supported
("RANGE", UNBOUNDED_PRECEDING, None): 0,
("RANGE", UNBOUNDED_PRECEDING, PRECEDING): 0,
("RANGE", UNBOUNDED_PRECEDING, CURRENT_ROW): 0,
("RANGE", UNBOUNDED_PRECEDING, FOLLOWING): 0,
("RANGE", UNBOUNDED_PRECEDING, UNBOUNDED_FOLLOWING): 0,
("RANGE", PRECEDING, None): 0,
("RANGE", PRECEDING, PRECEDING): 0,
("RANGE", PRECEDING, CURRENT_ROW): 0,
("RANGE", PRECEDING, FOLLOWING): 0,
("RANGE", PRECEDING, UNBOUNDED_FOLLOWING): 0,
("RANGE", CURRENT_ROW, None): 0,
("RANGE", CURRENT_ROW, CURRENT_ROW): 0,
("RANGE", CURRENT_ROW, FOLLOWING): 0,
("RANGE", CURRENT_ROW, UNBOUNDED_FOLLOWING): 0,
("RANGE", FOLLOWING, FOLLOWING): 0,
("RANGE", FOLLOWING, UNBOUNDED_FOLLOWING): 0,
},
"JOIN": {"INNER": 90, "LEFT": 30, "RIGHT": 10, "FULL_OUTER": 3, "CROSS": 1},
"SUBQUERY_PREDICATE": {
("Exists", "AGG", "CORRELATED"): 0, # Not supported
("Exists", "AGG", "UNCORRELATED"): 1,
("Exists", "NON_AGG", "CORRELATED"): 1,
("Exists", "NON_AGG", "UNCORRELATED"): 1,
("NotExists", "AGG", "CORRELATED"): 0, # Not supported
("NotExists", "AGG", "UNCORRELATED"): 0, # Not supported
("NotExists", "NON_AGG", "CORRELATED"): 1,
("NotExists", "NON_AGG", "UNCORRELATED"): 0, # Not supported
("In", "AGG", "CORRELATED"): 0, # Not supported
("In", "AGG", "UNCORRELATED"): 0, # Not supported
("In", "NON_AGG", "CORRELATED"): 1,
("In", "NON_AGG", "UNCORRELATED"): 1,
("NotIn", "AGG", "CORRELATED"): 0, # Not supported
("NotIn", "AGG", "UNCORRELATED"): 1,
("NotIn", "NON_AGG", "CORRELATED"): 1,
("NotIn", "NON_AGG", "UNCORRELATED"): 1,
("Scalar", "AGG", "CORRELATED"): 0, # Not supported
("Scalar", "AGG", "UNCORRELATED"): 1,
("Scalar", "NON_AGG", "CORRELATED"): 0, # Not supported
("Scalar", "NON_AGG", "UNCORRELATED"): 1,
},
"QUERY_EXECUTION": {"CREATE_TABLE_AS": 1, "RAW": 10, "VIEW": 1}, # Used by the discrepancy searcher
}
# On/off switches
self._flags = {
"ANALYTIC_DESIGNS": {
"TOP_LEVEL_QUERY_WITHOUT_LIMIT": True,
"DETERMINISTIC_ORDER_BY": True,
"NO_ORDER_BY": True,
"ONLY_SELECT_ITEM": True,
"UNBOUNDED_WINDOW": True,
"RANK_FUNC": True,
}
}
# Independent probabilities where 1 means 100%. These values may be ignored depending
# on the context. For example, GROUP_BY is almost always ignored and instead
# determined by the SELECT item weights above, since mixing aggregate and
# non-aggregate items requires the use of a GROUP BY. The GROUP_BY option below is
# only applied if all of the SELECT items are non-aggregate.
self._probabilities = {
"OPTIONAL_QUERY_CLAUSES": {
"WITH": 0.1, # MAX_NESTED_QUERY_COUNT bounds take precedence
"FROM": 1,
"WHERE": 0.5,
"GROUP_BY": 0.1, # special case, doesn't really do much, see comment above
"HAVING": 0.25,
"UNION": 0.1,
"ORDER_BY": 0.1,
},
"OPTIONAL_ANALYTIC_CLAUSES": {
"PARTITION_BY": 0.5,
"ORDER_BY": 0.5,
"WINDOW": 0.5,
}, # will only be used if ORDER BY is chosen
"MISC": {
"INLINE_VIEW": 0.1, # MAX_NESTED_QUERY_COUNT bounds take precedence
"SELECT_DISTINCT": 0.1,
"SCALAR_SUBQUERY": 0.1,
"UNION_ALL": 0.5,
},
} # Determines use of "ALL" but not "UNION"
self.__type_weights = {}
self.constant_generator = RandomValGenerator()
class DefaultProfile(object):
def __init__(self):
# Bounds are (min, max) values, the actual value used will be selected from the
# bounds and each value within the range has an equal probability of being selected.
self._bounds = {
"MAX_NESTED_QUERY_COUNT": (0, 2),
"MAX_NESTED_EXPR_COUNT": (0, 2),
"SELECT_ITEM_COUNT": (1, 5),
"WITH_TABLE_COUNT": (1, 3),
"TABLE_COUNT": (1, 2),
"ANALYTIC_LEAD_LAG_OFFSET": (1, 100),
"ANALYTIC_WINDOW_OFFSET": (1, 100),
}
# Below are interdependent weights used to determine probabilities. The probability
# of any item being selected should be (item weight) / sum(weights). A weight of
# zero means the item will never be selected.
self._weights = {
"SELECT_ITEM_CATEGORY": {"AGG": 3, "ANALYTIC": 1, "BASIC": 10},
"TYPES": {Boolean: 1, Char: 1, Decimal: 1, Float: 1, Int: 10, Timestamp: 1},
"ANALYTIC_WINDOW": {
("ROWS", UNBOUNDED_PRECEDING, None): 1,
("ROWS", UNBOUNDED_PRECEDING, PRECEDING): 2,
("ROWS", UNBOUNDED_PRECEDING, CURRENT_ROW): 1,
("ROWS", UNBOUNDED_PRECEDING, FOLLOWING): 2,
("ROWS", UNBOUNDED_PRECEDING, UNBOUNDED_FOLLOWING): 2,
("ROWS", PRECEDING, None): 1,
("ROWS", PRECEDING, PRECEDING): 2,
("ROWS", PRECEDING, CURRENT_ROW): 1,
("ROWS", PRECEDING, FOLLOWING): 2,
("ROWS", PRECEDING, UNBOUNDED_FOLLOWING): 2,
("ROWS", CURRENT_ROW, None): 1,
("ROWS", CURRENT_ROW, CURRENT_ROW): 1,
("ROWS", CURRENT_ROW, FOLLOWING): 2,
("ROWS", CURRENT_ROW, UNBOUNDED_FOLLOWING): 2,
("ROWS", FOLLOWING, FOLLOWING): 2,
("ROWS", FOLLOWING, UNBOUNDED_FOLLOWING): 2,
# Ranges not yet supported
("RANGE", UNBOUNDED_PRECEDING, None): 0,
("RANGE", UNBOUNDED_PRECEDING, PRECEDING): 0,
("RANGE", UNBOUNDED_PRECEDING, CURRENT_ROW): 0,
("RANGE", UNBOUNDED_PRECEDING, FOLLOWING): 0,
("RANGE", UNBOUNDED_PRECEDING, UNBOUNDED_FOLLOWING): 0,
("RANGE", PRECEDING, None): 0,
("RANGE", PRECEDING, PRECEDING): 0,
("RANGE", PRECEDING, CURRENT_ROW): 0,
("RANGE", PRECEDING, FOLLOWING): 0,
("RANGE", PRECEDING, UNBOUNDED_FOLLOWING): 0,
("RANGE", CURRENT_ROW, None): 0,
("RANGE", CURRENT_ROW, CURRENT_ROW): 0,
("RANGE", CURRENT_ROW, FOLLOWING): 0,
("RANGE", CURRENT_ROW, UNBOUNDED_FOLLOWING): 0,
("RANGE", FOLLOWING, FOLLOWING): 0,
("RANGE", FOLLOWING, UNBOUNDED_FOLLOWING): 0,
},
"JOIN": {"INNER": 90, "LEFT": 30, "RIGHT": 10, "FULL_OUTER": 3, "CROSS": 1},
"SUBQUERY_PREDICATE": {
("Exists", "AGG", "CORRELATED"): 0, # Not supported
("Exists", "AGG", "UNCORRELATED"): 1,
("Exists", "NON_AGG", "CORRELATED"): 1,
("Exists", "NON_AGG", "UNCORRELATED"): 1,
("NotExists", "AGG", "CORRELATED"): 0, # Not supported
("NotExists", "AGG", "UNCORRELATED"): 0, # Not supported
("NotExists", "NON_AGG", "CORRELATED"): 1,
("NotExists", "NON_AGG", "UNCORRELATED"): 0, # Not supported
("In", "AGG", "CORRELATED"): 0, # Not supported
("In", "AGG", "UNCORRELATED"): 0, # Not supported
("In", "NON_AGG", "CORRELATED"): 1,
("In", "NON_AGG", "UNCORRELATED"): 1,
("NotIn", "AGG", "CORRELATED"): 0, # Not supported
("NotIn", "AGG", "UNCORRELATED"): 1,
("NotIn", "NON_AGG", "CORRELATED"): 1,
("NotIn", "NON_AGG", "UNCORRELATED"): 1,
("Scalar", "AGG", "CORRELATED"): 0, # Not supported
("Scalar", "AGG", "UNCORRELATED"): 1,
("Scalar", "NON_AGG", "CORRELATED"): 0, # Not supported
("Scalar", "NON_AGG", "UNCORRELATED"): 1,
},
"QUERY_EXECUTION": {"CREATE_TABLE_AS": 1, "RAW": 10, "VIEW": 1}, # Used by the discrepancy searcher
}
# On/off switches
self._flags = {
"ANALYTIC_DESIGNS": {
"TOP_LEVEL_QUERY_WITHOUT_LIMIT": True,
"DETERMINISTIC_ORDER_BY": True,
"NO_ORDER_BY": True,
"ONLY_SELECT_ITEM": True,
"UNBOUNDED_WINDOW": True,
"RANK_FUNC": True,
}
}
# Independent probabilities where 1 means 100%. These values may be ignored depending
# on the context. For example, GROUP_BY is almost always ignored and instead
# determined by the SELECT item weights above, since mixing aggregate and
# non-aggregate items requires the use of a GROUP BY. The GROUP_BY option below is
# only applied if all of the SELECT items are non-aggregate.
self._probabilities = {
"OPTIONAL_QUERY_CLAUSES": {
"WITH": 0.1, # MAX_NESTED_QUERY_COUNT bounds take precedence
"FROM": 1,
"WHERE": 0.5,
"GROUP_BY": 0.1, # special case, doesn't really do much, see comment above
"HAVING": 0.25,
"UNION": 0.1,
"ORDER_BY": 0.1,
},
"OPTIONAL_ANALYTIC_CLAUSES": {
"PARTITION_BY": 0.5,
"ORDER_BY": 0.5,
"WINDOW": 0.5,
}, # will only be used if ORDER BY is chosen
"MISC": {
"INLINE_VIEW": 0.1, # MAX_NESTED_QUERY_COUNT bounds take precedence
"SELECT_DISTINCT": 0.1,
"SCALAR_SUBQUERY": 0.1,
"UNION_ALL": 0.5,
},
} # Determines use of "ALL" but not "UNION"
self.__type_weights = {}
self.constant_generator = RandomValGenerator()
def _get_config_value(self, start_config, *keys):
value = start_config
for key in keys:
value = value[key]
return value
def weights(self, *keys):
"""Convenience method for getting the values of named weights"""
return self._get_config_value(self._weights, *keys)
def bounds(self, *keys):
"""Convenience method for getting the values of named bounds"""
return self._get_config_value(self._bounds, *keys)
def probability(self, *keys):
"""Convenience method for getting the value of named probabilities"""
return self._get_config_value(self._probabilities, *keys)
def _choose_from_bounds(self, *bounds):
"""Returns a value that is within the given bounds. Each value has an equal chance
of being chosen.
"""
if isinstance(bounds[0], str):
lower, upper = self.bounds(*bounds)
else:
lower, upper = bounds
return randint(lower, upper)
def _choose_from_weights(self, *weights):
"""Returns a value that is selected from the keys of weights with the probability
determined by the values of weights.
"""
if isinstance(weights[0], str):
weights = self.weights(*weights)
else:
weights = weights[0]
total_weight = sum(weights.itervalues())
numeric_choice = randint(1, total_weight)
for choice_, weight in weights.iteritems():
if weight <= 0:
continue
if numeric_choice <= weight:
return choice_
numeric_choice -= weight
def _choose_from_filtered_weights(self, filter, *weights):
"""Convenience method, apply the given filter before choosing a value."""
if isinstance(weights[0], str):
weights = self.weights(*weights)
else:
weights = weights[0]
return self._choose_from_weights(
dict((choice_, weight) for choice_, weight in weights.iteritems() if filter(choice_))
)
def _decide_from_probability(self, *keys):
return random() < self.probability(*keys)
def get_max_nested_query_count(self):
"""Return the maximum number of queries the top level query may contain."""
return self._choose_from_bounds("MAX_NESTED_QUERY_COUNT")
def use_with_clause(self):
return self._decide_from_probability("OPTIONAL_QUERY_CLAUSES", "WITH")
def get_with_clause_table_ref_count(self):
"""Return the number of table ref entries a WITH clause should contain."""
return self._choose_from_bounds("WITH_TABLE_COUNT")
def get_select_item_count(self):
return self._choose_from_bounds("SELECT_ITEM_COUNT")
def choose_nested_expr_count(self):
return self._choose_from_bounds("MAX_NESTED_EXPR_COUNT")
def allowed_analytic_designs(self):
return [design for design, is_enabled in self._flags["ANALYTIC_DESIGNS"].iteritems() if is_enabled]
def use_partition_by_clause_in_analytic(self):
return self._decide_from_probability("OPTIONAL_ANALYTIC_CLAUSES", "PARTITION_BY")
def use_order_by_clause_in_analytic(self):
return self._decide_from_probability("OPTIONAL_ANALYTIC_CLAUSES", "ORDER_BY")
def use_window_in_analytic(self):
return self._decide_from_probability("OPTIONAL_ANALYTIC_CLAUSES", "WINDOW")
def choose_window_type(self):
return self._choose_from_weights("ANALYTIC_WINDOW")
def get_window_offset(self):
return self._choose_from_bounds("ANALYTIC_WINDOW_OFFSET")
def get_offset_for_analytic_lead_or_lag(self):
return self._choose_from_bounds("ANALYTIC_LEAD_LAG_OFFSET")
def get_table_count(self):
return self._choose_from_bounds("TABLE_COUNT")
def use_inline_view(self):
return self._decide_from_probability("MISC", "INLINE_VIEW")
def choose_table(self, table_exprs):
return choice(table_exprs)
def choose_join_type(self, join_types):
return self._choose_from_filtered_weights(lambda join_type: join_type in join_types, "JOIN")
def get_join_condition_count(self):
return self._choose_from_bounds("MAX_NESTED_EXPR_COUNT")
def use_where_clause(self):
return self._decide_from_probability("OPTIONAL_QUERY_CLAUSES", "WHERE")
def use_scalar_subquery(self):
return self._decide_from_probability("MISC", "SCALAR_SUBQUERY")
def choose_subquery_predicate_category(self, func_name, allow_correlated):
weights = self.weights("SUBQUERY_PREDICATE")
func_names = set(name for name, _, _ in weights.iterkeys())
if func_name not in func_names:
func_name = "Scalar"
allow_agg = self.weights("SELECT_ITEM_CATEGORY").get("AGG", 0)
if allow_correlated and self.bounds("TABLE_COUNT")[1] == 0:
allow_correlated = False
weights = dict(
((name, use_agg, use_correlated), weight)
for (name, use_agg, use_correlated), weight in weights.iteritems()
if name == func_name and (allow_agg or use_agg == "NON_AGG") and weight
)
if weights:
return self._choose_from_weights(weights)
def use_distinct(self):
return self._decide_from_probability("MISC", "SELECT_DISTINCT")
def use_distinct_in_func(self):
return self._decide_from_probability("MISC", "SELECT_DISTINCT")
def use_group_by_clause(self):
return self._decide_from_probability("OPTIONAL_QUERY_CLAUSES", "GROUP_BY")
def use_having_clause(self):
return self._decide_from_probability("OPTIONAL_QUERY_CLAUSES", "HAVING")
def use_union_clause(self):
return self._decide_from_probability("OPTIONAL_QUERY_CLAUSES", "UNION")
def use_union_all(self):
return self._decide_from_probability("MISC", "UNION_ALL")
def get_query_execution(self):
return self._choose_from_weights("QUERY_EXECUTION")
def choose_val_expr(self, val_exprs, types=TYPES):
if not val_exprs:
raise Exception("At least on value is required")
if not types:
raise Exception("At least one type is required")
available_types = set(types) & set(val_exprs.by_type)
if not available_types:
raise Exception("None of the provided values return any of the required types")
val_type = self.choose_type(available_types)
return choice(val_exprs.by_type[val_type])
def choose_constant(self, return_type=None, allow_null=True):
if not return_type:
return_type = self.choose_type()
while True:
val = self.constant_generator.generate_val(return_type)
if val is None and not allow_null:
continue
return return_type(val)
def choose_type(self, types=TYPES):
type_weights = self.weights("TYPES")
weights = dict((type_, type_weights[type_]) for type_ in types)
if not weights:
raise Exception("None of the requested types are enabled")
return self._choose_from_weights(weights)
def choose_func_signature(self, signatures):
"""Return a signature chosen from "signatures"."""
if not signatures:
raise Exception("At least one signature is required")
type_weights = self.weights("TYPES")
# First a function will be chosen then a signature. This is done so that the number
# of signatures a function has doesn't influence its likelihood of being chosen.
# Functions will be weighted based on the weight of the types in their arguments.
# The weights will be normalized by the number of arguments in the signature. The
# weight of a function will be the maximum weight out of all of it's signatures.
# If any signature has a type with a weight of zero, the signature will not be used.
#
# Example: type_weights = {Int: 10, Float: 1},
# funcs = [foo(Int), foo(Float), bar(Int, Float)]
#
# max signature length = 2 # from bar(Int, Float)
# weight of foo(Int) = (10 * 2)
# weight of foo(Float) = (1 * 2)
# weight of bar(Int, Float) = ((10 + 1) * 1)
# func_weights = {foo: 20, bar: 11}
#
# Note that this only selects a function, the function signature will be selected
# later. This is done to prevent function with a greater number of signatures from
# being selected more frequently.
func_weights = dict()
# The length of the signature in func_weights
signature_length_by_func = dict()
for signature in signatures:
signature_weight = type_weights[signature.return_type]
signature_length = 1
for arg in signature.args:
if arg.is_subquery:
for subtype in arg.type:
signature_weight *= type_weights[subtype]
signature_length += 1
else:
signature_weight *= type_weights[arg.type]
signature_length += 1
if not signature_weight:
continue
if not signature.func in func_weights or signature_weight > func_weights[signature.func]:
func_weights[signature.func] = signature_weight
signature_length_by_func[signature.func] = signature_length
if not func_weights:
raise Exception("All functions disallowed based on signature types")
distinct_signature_lengths = set(signature_length_by_func.values())
for func, weight in func_weights.iteritems():
signature_length = signature_length_by_func[func]
func_weights[func] = reduce(
lambda x, y: x * y, distinct_signature_lengths - set([signature_length]), func_weights[func]
)
func = self._choose_from_weights(func_weights)
# Same idea as above but for the signatures of the selected function.
signature_weights = dict()
signature_lengths = dict()
for idx, signature in enumerate(func.signatures()):
if signature not in signatures:
continue
signature_weight = type_weights[signature.return_type]
signature_length = 1
for arg in signature.args:
if arg.is_subquery:
for subtype in arg.type:
signature_weight *= type_weights[subtype]
signature_length += 1
else:
signature_weight *= type_weights[arg.type]
signature_length += 1
if signature_weight:
signature_weights[idx] = signature_weight
signature_lengths[idx] = signature_length
distinct_signature_lengths = set(signature_lengths.values())
for idx, weight in signature_weights.iteritems():
signature_length = signature_lengths[idx]
signature_weights[idx] = reduce(
lambda x, y: x * y, distinct_signature_lengths - set([signature_length]), signature_weights[idx]
)
idx = self._choose_from_weights(signature_weights)
return func.signatures()[idx]
def allow_func_signature(self, signature):
weights = self.weights("TYPES")
if not weights[signature.return_type]:
return False
for arg in signature.args:
if arg.is_subquery:
if not all(weights[subtype] for subtype in arg.type):
return False
elif not weights[arg.type]:
return False
return True
def __init__(self):
# Bounds are (min, max) values, the actual value used will be selected from the
# bounds and each value within the range has an equal probability of being selected.
self._bounds = {
'MAX_NESTED_QUERY_COUNT': (0, 2),
'MAX_NESTED_EXPR_COUNT': (0, 2),
'SELECT_ITEM_COUNT': (1, 5),
'WITH_TABLE_COUNT': (1, 3),
'TABLE_COUNT': (1, 2),
'ANALYTIC_LEAD_LAG_OFFSET': (1, 100),
'ANALYTIC_WINDOW_OFFSET': (1, 100)}
# Below are interdependent weights used to determine probabilities. The probability
# of any item being selected should be (item weight) / sum(weights). A weight of
# zero means the item will never be selected.
self._weights = {
'SELECT_ITEM_CATEGORY': {
'AGG': 3,
'ANALYTIC': 1,
'BASIC': 10},
'TYPES': {
Boolean: 1,
Char: 1,
Decimal: 1,
Float: 1,
Int: 10,
Timestamp: 1},
'ANALYTIC_WINDOW': {
('ROWS', UNBOUNDED_PRECEDING, None): 1,
('ROWS', UNBOUNDED_PRECEDING, PRECEDING): 2,
('ROWS', UNBOUNDED_PRECEDING, CURRENT_ROW): 1,
('ROWS', UNBOUNDED_PRECEDING, FOLLOWING): 2,
('ROWS', UNBOUNDED_PRECEDING, UNBOUNDED_FOLLOWING): 2,
('ROWS', PRECEDING, None): 1,
('ROWS', PRECEDING, PRECEDING): 2,
('ROWS', PRECEDING, CURRENT_ROW): 1,
('ROWS', PRECEDING, FOLLOWING): 2,
('ROWS', PRECEDING, UNBOUNDED_FOLLOWING): 2,
('ROWS', CURRENT_ROW, None): 1,
('ROWS', CURRENT_ROW, CURRENT_ROW): 1,
('ROWS', CURRENT_ROW, FOLLOWING): 2,
('ROWS', CURRENT_ROW, UNBOUNDED_FOLLOWING): 2,
('ROWS', FOLLOWING, FOLLOWING): 2,
('ROWS', FOLLOWING, UNBOUNDED_FOLLOWING): 2,
# Ranges not yet supported
('RANGE', UNBOUNDED_PRECEDING, None): 0,
('RANGE', UNBOUNDED_PRECEDING, PRECEDING): 0,
('RANGE', UNBOUNDED_PRECEDING, CURRENT_ROW): 0,
('RANGE', UNBOUNDED_PRECEDING, FOLLOWING): 0,
('RANGE', UNBOUNDED_PRECEDING, UNBOUNDED_FOLLOWING): 0,
('RANGE', PRECEDING, None): 0,
('RANGE', PRECEDING, PRECEDING): 0,
('RANGE', PRECEDING, CURRENT_ROW): 0,
('RANGE', PRECEDING, FOLLOWING): 0,
('RANGE', PRECEDING, UNBOUNDED_FOLLOWING): 0,
('RANGE', CURRENT_ROW, None): 0,
('RANGE', CURRENT_ROW, CURRENT_ROW): 0,
('RANGE', CURRENT_ROW, FOLLOWING): 0,
('RANGE', CURRENT_ROW, UNBOUNDED_FOLLOWING): 0,
('RANGE', FOLLOWING, FOLLOWING): 0,
('RANGE', FOLLOWING, UNBOUNDED_FOLLOWING): 0},
'JOIN': {
'INNER': 90,
'LEFT': 30,
'RIGHT': 10,
'FULL_OUTER': 3,
'CROSS': 1},
'SUBQUERY_PREDICATE': {
('Exists', 'AGG', 'CORRELATED'): 0, # Not supported
('Exists', 'AGG', 'UNCORRELATED'): 1,
('Exists', 'NON_AGG', 'CORRELATED'): 1,
('Exists', 'NON_AGG', 'UNCORRELATED'): 1,
('NotExists', 'AGG', 'CORRELATED'): 0, # Not supported
('NotExists', 'AGG', 'UNCORRELATED'): 0, # Not supported
('NotExists', 'NON_AGG', 'CORRELATED'): 1,
('NotExists', 'NON_AGG', 'UNCORRELATED'): 0, # Not supported
('In', 'AGG', 'CORRELATED'): 0, # Not supported
('In', 'AGG', 'UNCORRELATED'): 0, # Not supported
('In', 'NON_AGG', 'CORRELATED'): 1,
('In', 'NON_AGG', 'UNCORRELATED'): 1,
('NotIn', 'AGG', 'CORRELATED'): 0, # Not supported
('NotIn', 'AGG', 'UNCORRELATED'): 1,
('NotIn', 'NON_AGG', 'CORRELATED'): 1,
('NotIn', 'NON_AGG', 'UNCORRELATED'): 1,
('Scalar', 'AGG', 'CORRELATED'): 0, # Not supported
('Scalar', 'AGG', 'UNCORRELATED'): 1,
('Scalar', 'NON_AGG', 'CORRELATED'): 0, # Not supported
('Scalar', 'NON_AGG', 'UNCORRELATED'): 1},
'QUERY_EXECUTION': { # Used by the discrepancy searcher
'CREATE_TABLE_AS': 1,
'RAW': 10,
'VIEW': 1}}
# On/off switches
self._flags = {
'ANALYTIC_DESIGNS': {
'TOP_LEVEL_QUERY_WITHOUT_LIMIT': True,
'DETERMINISTIC_ORDER_BY': True,
'NO_ORDER_BY': True,
'ONLY_SELECT_ITEM': True,
'UNBOUNDED_WINDOW': True,
'RANK_FUNC': True}}
# Independent probabilities where 1 means 100%. These values may be ignored depending
# on the context. For example, GROUP_BY is almost always ignored and instead
# determined by the SELECT item weights above, since mixing aggregate and
# non-aggregate items requires the use of a GROUP BY. The GROUP_BY option below is
# only applied if all of the SELECT items are non-aggregate.
self._probabilities = {
'OPTIONAL_QUERY_CLAUSES': {
'WITH': 0.1, # MAX_NESTED_QUERY_COUNT bounds take precedence
'FROM': 1,
'WHERE': 0.5,
'GROUP_BY': 0.1, # special case, doesn't really do much, see comment above
'HAVING': 0.25,
'UNION': 0.1,
'ORDER_BY': 0.1},
'OPTIONAL_ANALYTIC_CLAUSES': {
'PARTITION_BY': 0.5,
'ORDER_BY': 0.5,
'WINDOW': 0.5}, # will only be used if ORDER BY is chosen
'MISC': {
'INLINE_VIEW': 0.1, # MAX_NESTED_QUERY_COUNT bounds take precedence
'SELECT_DISTINCT': 0.1,
'SCALAR_SUBQUERY': 0.1,
'UNION_ALL': 0.5}} # Determines use of "ALL" but not "UNION"
self.__type_weights = {}
self.constant_generator = RandomValGenerator()
class DefaultProfile(object):
def __init__(self):
# Bounds are (min, max) values, the actual value used will be selected from the
# bounds and each value within the range has an equal probability of being selected.
self._bounds = {
'MAX_NESTED_QUERY_COUNT': (0, 2),
'MAX_NESTED_EXPR_COUNT': (0, 2),
'SELECT_ITEM_COUNT': (1, 5),
'WITH_TABLE_COUNT': (1, 3),
'TABLE_COUNT': (1, 2),
'ANALYTIC_LEAD_LAG_OFFSET': (1, 100),
'ANALYTIC_WINDOW_OFFSET': (1, 100)}
# Below are interdependent weights used to determine probabilities. The probability
# of any item being selected should be (item weight) / sum(weights). A weight of
# zero means the item will never be selected.
self._weights = {
'SELECT_ITEM_CATEGORY': {
'AGG': 3,
'ANALYTIC': 1,
'BASIC': 10},
'TYPES': {
Boolean: 1,
Char: 1,
Decimal: 1,
Float: 1,
Int: 10,
Timestamp: 1},
'ANALYTIC_WINDOW': {
('ROWS', UNBOUNDED_PRECEDING, None): 1,
('ROWS', UNBOUNDED_PRECEDING, PRECEDING): 2,
('ROWS', UNBOUNDED_PRECEDING, CURRENT_ROW): 1,
('ROWS', UNBOUNDED_PRECEDING, FOLLOWING): 2,
('ROWS', UNBOUNDED_PRECEDING, UNBOUNDED_FOLLOWING): 2,
('ROWS', PRECEDING, None): 1,
('ROWS', PRECEDING, PRECEDING): 2,
('ROWS', PRECEDING, CURRENT_ROW): 1,
('ROWS', PRECEDING, FOLLOWING): 2,
('ROWS', PRECEDING, UNBOUNDED_FOLLOWING): 2,
('ROWS', CURRENT_ROW, None): 1,
('ROWS', CURRENT_ROW, CURRENT_ROW): 1,
('ROWS', CURRENT_ROW, FOLLOWING): 2,
('ROWS', CURRENT_ROW, UNBOUNDED_FOLLOWING): 2,
('ROWS', FOLLOWING, FOLLOWING): 2,
('ROWS', FOLLOWING, UNBOUNDED_FOLLOWING): 2,
# Ranges not yet supported
('RANGE', UNBOUNDED_PRECEDING, None): 0,
('RANGE', UNBOUNDED_PRECEDING, PRECEDING): 0,
('RANGE', UNBOUNDED_PRECEDING, CURRENT_ROW): 0,
('RANGE', UNBOUNDED_PRECEDING, FOLLOWING): 0,
('RANGE', UNBOUNDED_PRECEDING, UNBOUNDED_FOLLOWING): 0,
('RANGE', PRECEDING, None): 0,
('RANGE', PRECEDING, PRECEDING): 0,
('RANGE', PRECEDING, CURRENT_ROW): 0,
('RANGE', PRECEDING, FOLLOWING): 0,
('RANGE', PRECEDING, UNBOUNDED_FOLLOWING): 0,
('RANGE', CURRENT_ROW, None): 0,
('RANGE', CURRENT_ROW, CURRENT_ROW): 0,
('RANGE', CURRENT_ROW, FOLLOWING): 0,
('RANGE', CURRENT_ROW, UNBOUNDED_FOLLOWING): 0,
('RANGE', FOLLOWING, FOLLOWING): 0,
('RANGE', FOLLOWING, UNBOUNDED_FOLLOWING): 0},
'JOIN': {
'INNER': 90,
'LEFT': 30,
'RIGHT': 10,
'FULL_OUTER': 3,
'CROSS': 1},
'SUBQUERY_PREDICATE': {
('Exists', 'AGG', 'CORRELATED'): 0, # Not supported
('Exists', 'AGG', 'UNCORRELATED'): 1,
('Exists', 'NON_AGG', 'CORRELATED'): 1,
('Exists', 'NON_AGG', 'UNCORRELATED'): 1,
('NotExists', 'AGG', 'CORRELATED'): 0, # Not supported
('NotExists', 'AGG', 'UNCORRELATED'): 0, # Not supported
('NotExists', 'NON_AGG', 'CORRELATED'): 1,
('NotExists', 'NON_AGG', 'UNCORRELATED'): 0, # Not supported
('In', 'AGG', 'CORRELATED'): 0, # Not supported
('In', 'AGG', 'UNCORRELATED'): 0, # Not supported
('In', 'NON_AGG', 'CORRELATED'): 1,
('In', 'NON_AGG', 'UNCORRELATED'): 1,
('NotIn', 'AGG', 'CORRELATED'): 0, # Not supported
('NotIn', 'AGG', 'UNCORRELATED'): 1,
('NotIn', 'NON_AGG', 'CORRELATED'): 1,
('NotIn', 'NON_AGG', 'UNCORRELATED'): 1,
('Scalar', 'AGG', 'CORRELATED'): 0, # Not supported
('Scalar', 'AGG', 'UNCORRELATED'): 1,
('Scalar', 'NON_AGG', 'CORRELATED'): 0, # Not supported
('Scalar', 'NON_AGG', 'UNCORRELATED'): 1},
'QUERY_EXECUTION': { # Used by the discrepancy searcher
'CREATE_TABLE_AS': 1,
'RAW': 10,
'VIEW': 1}}
# On/off switches
self._flags = {
'ANALYTIC_DESIGNS': {
'TOP_LEVEL_QUERY_WITHOUT_LIMIT': True,
'DETERMINISTIC_ORDER_BY': True,
'NO_ORDER_BY': True,
'ONLY_SELECT_ITEM': True,
'UNBOUNDED_WINDOW': True,
'RANK_FUNC': True}}
# Independent probabilities where 1 means 100%. These values may be ignored depending
# on the context. For example, GROUP_BY is almost always ignored and instead
# determined by the SELECT item weights above, since mixing aggregate and
# non-aggregate items requires the use of a GROUP BY. The GROUP_BY option below is
# only applied if all of the SELECT items are non-aggregate.
self._probabilities = {
'OPTIONAL_QUERY_CLAUSES': {
'WITH': 0.1, # MAX_NESTED_QUERY_COUNT bounds take precedence
'FROM': 1,
'WHERE': 0.5,
'GROUP_BY': 0.1, # special case, doesn't really do much, see comment above
'HAVING': 0.25,
'UNION': 0.1,
'ORDER_BY': 0.1},
'OPTIONAL_ANALYTIC_CLAUSES': {
'PARTITION_BY': 0.5,
'ORDER_BY': 0.5,
'WINDOW': 0.5}, # will only be used if ORDER BY is chosen
'MISC': {
'INLINE_VIEW': 0.1, # MAX_NESTED_QUERY_COUNT bounds take precedence
'SELECT_DISTINCT': 0.1,
'SCALAR_SUBQUERY': 0.1,
'UNION_ALL': 0.5}} # Determines use of "ALL" but not "UNION"
self.__type_weights = {}
self.constant_generator = RandomValGenerator()
def _get_config_value(self, start_config, *keys):
value = start_config
for key in keys:
value = value[key]
return value
def weights(self, *keys):
'''Convenience method for getting the values of named weights'''
return self._get_config_value(self._weights, *keys)
def bounds(self, *keys):
'''Convenience method for getting the values of named bounds'''
return self._get_config_value(self._bounds, *keys)
def probability(self, *keys):
'''Convenience method for getting the value of named probabilities'''
return self._get_config_value(self._probabilities, *keys)
def _choose_from_bounds(self, *bounds):
'''Returns a value that is within the given bounds. Each value has an equal chance
of being chosen.
'''
if isinstance(bounds[0], str):
lower, upper = self.bounds(*bounds)
else:
lower, upper = bounds
return randint(lower, upper)
def _choose_from_weights(self, *weights):
'''Returns a value that is selected from the keys of weights with the probability
determined by the values of weights.
'''
if isinstance(weights[0], str):
weights = self.weights(*weights)
else:
weights = weights[0]
total_weight = sum(weights.itervalues())
numeric_choice = randint(1, total_weight)
for choice_, weight in weights.iteritems():
if weight <= 0:
continue
if numeric_choice <= weight:
return choice_
numeric_choice -= weight
def _choose_from_filtered_weights(self, filter, *weights):
'''Convenience method, apply the given filter before choosing a value.'''
if isinstance(weights[0], str):
weights = self.weights(*weights)
else:
weights = weights[0]
return self._choose_from_weights(dict(
(choice_, weight) for choice_, weight in weights.iteritems() if filter(choice_)))
def _decide_from_probability(self, *keys):
return random() < self.probability(*keys)
def get_max_nested_query_count(self):
'''Return the maximum number of queries the top level query may contain.'''
return self._choose_from_bounds('MAX_NESTED_QUERY_COUNT')
def use_with_clause(self):
return self._decide_from_probability('OPTIONAL_QUERY_CLAUSES', 'WITH')
def get_with_clause_table_ref_count(self):
'''Return the number of table ref entries a WITH clause should contain.'''
return self._choose_from_bounds('WITH_TABLE_COUNT')
def get_select_item_count(self):
return self._choose_from_bounds('SELECT_ITEM_COUNT')
def choose_nested_expr_count(self):
return self._choose_from_bounds('MAX_NESTED_EXPR_COUNT')
def allowed_analytic_designs(self):
return [design for design, is_enabled in self._flags['ANALYTIC_DESIGNS'].iteritems()
if is_enabled]
def use_partition_by_clause_in_analytic(self):
return self._decide_from_probability('OPTIONAL_ANALYTIC_CLAUSES', 'PARTITION_BY')
def use_order_by_clause_in_analytic(self):
return self._decide_from_probability('OPTIONAL_ANALYTIC_CLAUSES', 'ORDER_BY')
def use_window_in_analytic(self):
return self._decide_from_probability('OPTIONAL_ANALYTIC_CLAUSES', 'WINDOW')
def choose_window_type(self):
return self._choose_from_weights('ANALYTIC_WINDOW')
def get_window_offset(self):
return self._choose_from_bounds('ANALYTIC_WINDOW_OFFSET')
def get_offset_for_analytic_lead_or_lag(self):
return self._choose_from_bounds('ANALYTIC_LEAD_LAG_OFFSET')
def get_table_count(self):
return self._choose_from_bounds('TABLE_COUNT')
def use_inline_view(self):
return self._decide_from_probability('MISC', 'INLINE_VIEW')
def choose_table(self, table_exprs):
return choice(table_exprs)
def choose_join_type(self, join_types):
return self._choose_from_filtered_weights(
lambda join_type: join_type in join_types, 'JOIN')
def get_join_condition_count(self):
return self._choose_from_bounds('MAX_NESTED_EXPR_COUNT')
def use_where_clause(self):
return self._decide_from_probability('OPTIONAL_QUERY_CLAUSES', 'WHERE')
def use_scalar_subquery(self):
return self._decide_from_probability('MISC', 'SCALAR_SUBQUERY')
def choose_subquery_predicate_category(self, func_name, allow_correlated):
weights = self.weights('SUBQUERY_PREDICATE')
func_names = set(name for name, _, _ in weights.iterkeys())
if func_name not in func_names:
func_name = 'Scalar'
allow_agg = self.weights('SELECT_ITEM_CATEGORY').get('AGG', 0)
if allow_correlated and self.bounds('TABLE_COUNT')[1] == 0:
allow_correlated = False
weights = dict(((name, use_agg, use_correlated), weight)
for (name, use_agg, use_correlated), weight in weights.iteritems()
if name == func_name \
and (allow_agg or use_agg == 'NON_AGG') \
and weight)
if weights:
return self._choose_from_weights(weights)
def use_distinct(self):
return self._decide_from_probability('MISC', 'SELECT_DISTINCT')
def use_distinct_in_func(self):
return self._decide_from_probability('MISC', 'SELECT_DISTINCT')
def use_group_by_clause(self):
return self._decide_from_probability('OPTIONAL_QUERY_CLAUSES', 'GROUP_BY')
def use_having_clause(self):
return self._decide_from_probability('OPTIONAL_QUERY_CLAUSES', 'HAVING')
def use_union_clause(self):
return self._decide_from_probability('OPTIONAL_QUERY_CLAUSES', 'UNION')
def use_union_all(self):
return self._decide_from_probability('MISC', 'UNION_ALL')
def get_query_execution(self):
return self._choose_from_weights('QUERY_EXECUTION')
def use_having_without_groupby(self):
return True
def use_nested_with(self):
return True
# Workaround for Hive null ordering differences, and lack of 'NULL FIRST', 'NULL LAST'
# specifications. The ref db will order nulls as specified for ASC sorting to make it
# identifical to Hive. Valid return values are: 'BEFORE', 'AFTER', or 'DEFAULT',
# the latter means no specification needed.
def nulls_order_asc(self):
return 'DEFAULT'
def choose_val_expr(self, val_exprs, types=TYPES):
if not val_exprs:
raise Exception('At least on value is required')
if not types:
raise Exception('At least one type is required')
available_types = set(types) & set(val_exprs.by_type)
if not available_types:
raise Exception('None of the provided values return any of the required types')
val_type = self.choose_type(available_types)
return choice(val_exprs.by_type[val_type])
def choose_constant(self, return_type=None, allow_null=True):
if not return_type:
return_type = self.choose_type()
while True:
val = self.constant_generator.generate_val(return_type)
if val is None and not allow_null:
continue
return return_type(val)
def choose_type(self, types=TYPES):
type_weights = self.weights('TYPES')
weights = dict((type_, type_weights[type_]) for type_ in types)
if not weights:
raise Exception('None of the requested types are enabled')
return self._choose_from_weights(weights)
def choose_func_signature(self, signatures):
'''Return a signature chosen from "signatures".'''
if not signatures:
raise Exception('At least one signature is required')
type_weights = self.weights('TYPES')
# First a function will be chosen then a signature. This is done so that the number
# of signatures a function has doesn't influence its likelihood of being chosen.
# Functions will be weighted based on the weight of the types in their arguments.
# The weights will be normalized by the number of arguments in the signature. The
# weight of a function will be the maximum weight out of all of it's signatures.
# If any signature has a type with a weight of zero, the signature will not be used.
#
# Example: type_weights = {Int: 10, Float: 1},
# funcs = [foo(Int), foo(Float), bar(Int, Float)]
#
# max signature length = 2 # from bar(Int, Float)
# weight of foo(Int) = (10 * 2)
# weight of foo(Float) = (1 * 2)
# weight of bar(Int, Float) = ((10 + 1) * 1)
# func_weights = {foo: 20, bar: 11}
#
# Note that this only selects a function, the function signature will be selected
# later. This is done to prevent function with a greater number of signatures from
# being selected more frequently.
func_weights = dict()
# The length of the signature in func_weights
signature_length_by_func = dict()
for signature in signatures:
signature_weight = type_weights[signature.return_type]
signature_length = 1
for arg in signature.args:
if arg.is_subquery:
for subtype in arg.type:
signature_weight *= type_weights[subtype]
signature_length += 1
else:
signature_weight *= type_weights[arg.type]
signature_length += 1
if not signature_weight:
continue
if not signature.func in func_weights \
or signature_weight > func_weights[signature.func]:
func_weights[signature.func] = signature_weight
signature_length_by_func[signature.func] = signature_length
if not func_weights:
raise Exception('All functions disallowed based on signature types')
distinct_signature_lengths = set(signature_length_by_func.values())
for func, weight in func_weights.iteritems():
signature_length = signature_length_by_func[func]
func_weights[func] = reduce(
lambda x, y: x * y,
distinct_signature_lengths - set([signature_length]),
func_weights[func])
func = self._choose_from_weights(func_weights)
# Same idea as above but for the signatures of the selected function.
signature_weights = dict()
signature_lengths = dict()
for idx, signature in enumerate(func.signatures()):
if signature not in signatures:
continue
signature_weight = type_weights[signature.return_type]
signature_length = 1
for arg in signature.args:
if arg.is_subquery:
for subtype in arg.type:
signature_weight *= type_weights[subtype]
signature_length += 1
else:
signature_weight *= type_weights[arg.type]
signature_length += 1
if signature_weight:
signature_weights[idx] = signature_weight
signature_lengths[idx] = signature_length
distinct_signature_lengths = set(signature_lengths.values())
for idx, weight in signature_weights.iteritems():
signature_length = signature_lengths[idx]
signature_weights[idx] = reduce(
lambda x, y: x * y,
distinct_signature_lengths - set([signature_length]),
signature_weights[idx])
idx = self._choose_from_weights(signature_weights)
return func.signatures()[idx]
def allow_func_signature(self, signature):
weights = self.weights('TYPES')
if not weights[signature.return_type]:
return False
for arg in signature.args:
if arg.is_subquery:
if not all(weights[subtype] for subtype in arg.type):
return False
elif not weights[arg.type]:
return False
return True