def create_column(self, col_name, col_type):
''' Takes the output from parse_col_desc and creates the right column type. This
method returns one of Column, ArrayColumn, MapColumn, StructColumn.'''
if isinstance(col_type, str):
if col_type.upper() == 'VARCHAR':
col_type = 'STRING'
type_name = self.TYPE_NAME_ALIASES.get(col_type.upper())
return Column(owner=None,
name=col_name.lower(),
exact_type=self.TYPES_BY_NAME[type_name])
general_class = col_type[0]
if general_class.upper() == 'ARRAY':
return ArrayColumn(owner=None,
name=col_name.lower(),
item=self.create_column(col_name='item',
col_type=col_type[1]))
if general_class.upper() == 'MAP':
return MapColumn(owner=None,
name=col_name.lower(),
key=self.create_column(col_name='key',
col_type=col_type[1]),
value=self.create_column(col_name='value',
col_type=col_type[2]))
if general_class.upper() == 'STRUCT':
struct_col = StructColumn(owner=None, name=col_name.lower())
for field_name, field_type in col_type[1:]:
struct_col.add_col(self.create_column(field_name, field_type))
return struct_col
general_class = self.TYPE_NAME_ALIASES.get(col_type[0].upper())
if general_class.upper() == 'DECIMAL':
return Column(owner=None,
name=col_name.lower(),
exact_type=get_decimal_class(int(col_type[1]),
int(col_type[2])))
if general_class.upper() == 'CHAR':
return Column(owner=None,
name=col_name.lower(),
exact_type=get_char_class(int(col_type[1])))
if general_class.upper() == 'VARCHAR':
type_size = int(col_type[1])
if type_size <= VarChar.MAX:
cur_type = get_varchar_class(type_size)
else:
cur_type = self.TYPES_BY_NAME['STRING']
return Column(owner=None,
name=col_name.lower(),
exact_type=cur_type)
raise Exception('unable to parse: {0}, type: {1}'.format(
col_name, col_type))
def FakeColumn(name, type_, is_primary_key=False): """ Return a Column, the creation of which allows the user not to have to specify the first argument, which is the table to which the column belongs. Typical use should be when creating a FakeTable, use FakeColumns as arguments. """ col = Column(None, name, type_) col.is_primary_key = is_primary_key return col
def test_table_model(self, cursor, hive_cursor):
table = Table("some_test_table")
cursor.drop_table(table.name, if_exists=True)
table.storage_format = 'textfile'
table.add_col(Column(table, "bigint_col", BigInt))
table.add_col(Column(table, "string_col", String))
cursor.create_table(table)
try:
other = hive_cursor.describe_table(table.name)
assert other.name == table.name
assert other.cols == table.cols
finally:
cursor.drop_table(table.name)
def create_join_predicate(self, parent_table, child_table):
for col in parent_table.cols:
if col.name == 'id':
parent_id_col = col
break
else:
parent_id_col = Column(parent_table, 'id', BigInt)
parent_id_col.for_flattening = True
parent_table.add_col(parent_id_col)
child_col_name = self.flat_collection_name(parent_table) + '_id'
child_col = Column(None, child_col_name, BigInt)
child_table.add_col(child_col)
return Equals.create_from_args(parent_id_col, child_col)
def _create_random_table(self, table_name, min_col_count, max_col_count,
allowed_storage_formats):
'''Create and return a Table with a random number of cols.'''
col_count = randint(min_col_count, max_col_count)
storage_format = choice(allowed_storage_formats)
table = Table(table_name)
table.storage_format = storage_format
allowed_types = list(TYPES)
# Avro doesn't support timestamps yet.
if table.storage_format == 'AVRO':
allowed_types.remove(Timestamp)
# TODO: 'table.cols' returns a copy of all scalar cols, so 'table.cols.append()'
# doesn't actually modify the table's columns. 'table.cols' should be changed
# to allow access to the real columns.
cols = table.cols
for col_idx in xrange(col_count):
col_type = choice(allowed_types)
col_type = choice(
filter(lambda type_: issubclass(type_, col_type), EXACT_TYPES))
if issubclass(col_type,
VarChar) and not issubclass(col_type, String):
col_type = get_varchar_class(randint(1, VarChar.MAX))
elif issubclass(col_type,
Char) and not issubclass(col_type, String):
col_type = get_char_class(randint(1, Char.MAX))
elif issubclass(col_type, Decimal):
max_digits = randint(1, Decimal.MAX_DIGITS)
col_type = get_decimal_class(max_digits,
randint(1, max_digits))
col = Column(
table, '%s_col_%s' % (col_type.__name__.lower(), col_idx + 1),
col_type)
cols.append(col)
table.cols = cols
return table
def create_random_table(self, table_name, min_number_of_cols,
max_number_of_cols, allowed_storage_formats):
'''Create and return a Table with a random number of cols.'''
col_count = randint(min_number_of_cols, max_number_of_cols)
storage_format = choice(allowed_storage_formats)
table = Table(table_name)
table.storage_format = storage_format
for col_idx in xrange(col_count):
col_type = choice(TYPES)
col_type = choice(
filter(lambda type_: issubclass(type_, col_type), EXACT_TYPES))
if issubclass(col_type,
VarChar) and not issubclass(col_type, String):
col_type = get_varchar_class(randint(1, VarChar.MAX))
elif issubclass(col_type,
Char) and not issubclass(col_type, String):
col_type = get_char_class(randint(1, Char.MAX))
elif issubclass(col_type, Decimal):
max_digits = randint(1, Decimal.MAX_DIGITS)
col_type = get_decimal_class(max_digits,
randint(1, max_digits))
col = Column(
table, '%s_col_%s' % (col_type.__name__.lower(), col_idx + 1),
col_type)
table.cols.append(col)
return table
def test_hive_create_equality_only_joins():
"""
Tests that QueryGenerator produces a join condition with only equality functions if the
HiveProfile is used.
"""
class FakeHiveQueryProfile(HiveProfile):
"""
A fake QueryProfile that extends the HiveProfile, various weights are modified in
order to ensure that this test is deterministic.
"""
def choose_join_condition_count(self):
"""
There should be only one operator in the JOIN condition
"""
return 1
def choose_conjunct_disjunct_fill_ratio(self):
"""
There should be no AND or OR operators
"""
return 0
def choose_relational_func_fill_ratio(self):
"""
Force all operators to be relational
"""
return 1
query_generator = QueryGenerator(FakeHiveQueryProfile())
# Create two tables that have one joinable Column
right_table_expr_list = TableExprList()
right_table = Table("right_table")
right_table.add_col(Column("right_table", "right_col", Int))
right_table_expr_list.append(right_table)
left_table_expr_list = TableExprList()
left_table = Table("left_table")
left_table.add_col(Column("left_table", "left_col", Int))
left_table_expr_list.append(left_table)
# Validate the root predicate is an Equals funcs
assert isinstance(query_generator._create_relational_join_condition(
right_table_expr_list, left_table_expr_list), Equals)
def test_use_nested_width_subquery():
"""
Tests that setting DefaultProfile.use_nested_with to False works properly. Setting this
method to return False should prevent a WITH clause from being used inside a sub-query.
"""
class MockQueryProfile(DefaultProfile):
"""
A mock QueryProfile that sets use_nested_with to False and forces the
QueryGenerator to created nested queries.
"""
def __init__(self):
super(MockQueryProfile, self).__init__()
# Force the QueryGenerator to create nested queries
self._bounds['MAX_NESTED_QUERY_COUNT'] = (4, 4)
# Force the QueryGenerator to use WITH clauses whenever possible
self._probabilities['OPTIONAL_QUERY_CLAUSES']['WITH'] = 1
# Force the QueryGenerator to create inline views whenever possible
self._probabilities['MISC']['INLINE_VIEW'] = 1
def use_nested_with(self):
return False
mock_query_gen = QueryGenerator(MockQueryProfile())
# Create two tables
table_expr_list = TableExprList()
right_table = Table("right_table")
right_table.add_col(Column("right_table", "right_col", Int))
table_expr_list.append(right_table)
left_table = Table("left_table")
left_table.add_col(Column("left_table", "left_col", Int))
table_expr_list.append(left_table)
# Check that each nested_query doesn't have a with clause
for nested_query in mock_query_gen.generate_statement(table_expr_list).nested_queries:
assert nested_query.with_clause is None
def describe_table(self, table_name):
'''Return a Table with table and col names always in lowercase.'''
rows = self.execute_and_fetchall(self.make_describe_table_sql(table_name))
table = Table(table_name.lower())
for row in rows:
col_name, data_type = row[:2]
if data_type == 'tinyint(1)':
# Just assume this is a boolean...
data_type = 'boolean'
if 'decimal' not in data_type and '(' in data_type:
# Strip the size of the data type
data_type = data_type[:data_type.index('(')]
table.cols.append(Column(table, col_name.lower(), self.parse_data_type(data_type)))
return table
def describe_table(self, table_name):
'''Return a Table with table and col names always in lowercase.'''
rows = self.execute_and_fetchall(self.make_describe_table_sql(table_name))
table = Table(table_name.lower())
for row in rows:
col_name, data_type = row[:2]
match = self.SQL_TYPE_PATTERN.match(data_type)
if not match:
raise Exception('Unexpected data type format: %s' % data_type)
type_name = self.TYPE_NAME_ALIASES.get(match.group(1).upper())
if not type_name:
raise Exception('Unknown data type: ' + match.group(1))
if len(match.groups()) > 1 and match.group(2) is not None:
type_size = [int(size) for size in match.group(2)[1:-1].split(',')]
else:
type_size = None
table.cols.append(
Column(table, col_name.lower(), self.parse_data_type(type_name, type_size)))
self.load_unique_col_metadata(table)
return table
def cols(self):
return ValExprList(
Column(self, item.name, item.type)
for item in self.query.select_clause.items)