def test_basic_breakdown_statements() -> None:
"""
Test that multiple statements are parsed correctly.
"""
query = ParsedQuery(
"""
SELECT * FROM birth_names;
SELECT * FROM birth_names LIMIT 1;
"""
)
assert query.get_statements() == [
"SELECT * FROM birth_names",
"SELECT * FROM birth_names LIMIT 1",
]
def test_kql_is_select_query(app_context: AppContext, kql: str,
expected: bool) -> None:
"""
Make sure that KQL dialect consider only statements that do not start with "." (dot)
as a SELECT statements
"""
from superset.db_engine_specs.kusto import KustoKqlEngineSpec
from superset.sql_parse import ParsedQuery
parsed_query = ParsedQuery(kql)
is_select = KustoKqlEngineSpec.is_select_query(parsed_query)
assert expected == is_select
def test_kql_is_readonly_query(
app_context: AppContext, kql: str, expected: bool
) -> None:
"""
Make sure that KQL dialect consider only SELECT statements as read-only
"""
from superset.db_engine_specs.kusto import KustoKqlEngineSpec
from superset.sql_parse import ParsedQuery
parsed_query = ParsedQuery(kql)
is_readonly = KustoKqlEngineSpec.is_readonly_query(parsed_query)
assert expected == is_readonly
def test_is_valid_cvas(self):
"""A valid CVAS has a single SELECT statement"""
query = "SELECT * FROM table"
parsed = ParsedQuery(query, strip_comments=True)
assert parsed.is_valid_cvas()
query = """
-- comment
SELECT * FROM table
-- comment 2
"""
parsed = ParsedQuery(query, strip_comments=True)
assert parsed.is_valid_cvas()
query = """
-- comment
SET @value = 42;
SELECT @value as foo;
-- comment 2
"""
parsed = ParsedQuery(query, strip_comments=True)
assert not parsed.is_valid_cvas()
query = """
-- comment
EXPLAIN SELECT * FROM table
-- comment 2
"""
parsed = ParsedQuery(query, strip_comments=True)
assert not parsed.is_valid_ctas()
query = """
SELECT * FROM table;
INSERT INTO TABLE (foo) VALUES (42);
"""
parsed = ParsedQuery(query, strip_comments=True)
assert not parsed.is_valid_ctas()
def test_messy_breakdown_statements() -> None:
"""
Test the messy multiple statements are parsed correctly.
"""
query = ParsedQuery("""
SELECT 1;\t\n\n\n \t
\t\nSELECT 2;
SELECT * FROM birth_names;;;
SELECT * FROM birth_names LIMIT 1
""")
assert query.get_statements() == [
"SELECT 1",
"SELECT 2",
"SELECT * FROM birth_names",
"SELECT * FROM birth_names LIMIT 1",
]
def process_statement(cls, statement: str, database: "Database",
user_name: str) -> str:
"""
Process a SQL statement by stripping and mutating it.
:param statement: A single SQL statement
:param database: Database instance
:param username: Effective username
:return: Dictionary with different costs
"""
parsed_query = ParsedQuery(statement)
sql = parsed_query.stripped()
sql_query_mutator = config["SQL_QUERY_MUTATOR"]
if sql_query_mutator:
sql = sql_query_mutator(sql, user_name, security_manager, database)
return sql
def test_messy_breakdown_statements(self):
multi_sql = """
SELECT 1;\t\n\n\n \t
\t\nSELECT 2;
SELECT * FROM birth_names;;;
SELECT * FROM birth_names LIMIT 1
"""
parsed = ParsedQuery(multi_sql)
statements = parsed.get_statements()
self.assertEqual(len(statements), 4)
expected = [
"SELECT 1",
"SELECT 2",
"SELECT * FROM birth_names",
"SELECT * FROM birth_names LIMIT 1",
]
self.assertEqual(statements, expected)
def test_cte_is_select() -> None:
"""
Some CTEs are not correctly identified as SELECTS.
"""
# `AS(` gets parsed as a function
sql = ParsedQuery("""WITH foo AS(
SELECT
FLOOR(__time TO WEEK) AS "week",
name,
COUNT(DISTINCT user_id) AS "unique_users"
FROM "druid"."my_table"
GROUP BY 1,2
)
SELECT
f.week,
f.name,
f.unique_users
FROM foo f""")
assert sql.is_select()
def get_virtual_table_metadata(dataset: "SqlaTable") -> List[Dict[str, str]]:
"""Use SQLparser to get virtual dataset metadata"""
if not dataset.sql:
raise SupersetGenericDBErrorException(
message=_("Virtual dataset query cannot be empty"),
)
db_engine_spec = dataset.database.db_engine_spec
engine = dataset.database.get_sqla_engine(schema=dataset.schema)
sql = dataset.get_template_processor().process_template(
dataset.sql, **dataset.template_params_dict
)
parsed_query = ParsedQuery(sql)
if not db_engine_spec.is_readonly_query(parsed_query):
raise SupersetSecurityException(
SupersetError(
error_type=SupersetErrorType.DATASOURCE_SECURITY_ACCESS_ERROR,
message=_("Only `SELECT` statements are allowed"),
level=ErrorLevel.ERROR,
)
)
statements = parsed_query.get_statements()
if len(statements) > 1:
raise SupersetSecurityException(
SupersetError(
error_type=SupersetErrorType.DATASOURCE_SECURITY_ACCESS_ERROR,
message=_("Only single queries supported"),
level=ErrorLevel.ERROR,
)
)
# TODO(villebro): refactor to use same code that's used by
# sql_lab.py:execute_sql_statements
try:
with closing(engine.raw_connection()) as conn:
cursor = conn.cursor()
query = dataset.database.apply_limit_to_sql(statements[0])
db_engine_spec.execute(cursor, query)
result = db_engine_spec.fetch_data(cursor, limit=1)
result_set = SupersetResultSet(result, cursor.description, db_engine_spec)
cols = result_set.columns
except Exception as exc:
raise SupersetGenericDBErrorException(message=str(exc))
return cols
def validate(
cls,
sql: str,
schema: str,
database: Any,
) -> List[SQLValidationAnnotation]:
"""
Presto supports query-validation queries by running them with a
prepended explain.
For example, "SELECT 1 FROM default.mytable" becomes "EXPLAIN (TYPE
VALIDATE) SELECT 1 FROM default.mytable.
"""
user_name = g.user.username if g.user else None
parsed_query = ParsedQuery(sql)
statements = parsed_query.get_statements()
logging.info(f'Validating {len(statements)} statement(s)')
engine = database.get_sqla_engine(
schema=schema,
nullpool=True,
user_name=user_name,
source=sources.get('sql_lab', None),
)
# Sharing a single connection and cursor across the
# execution of all statements (if many)
annotations: List[SQLValidationAnnotation] = []
with closing(engine.raw_connection()) as conn:
with closing(conn.cursor()) as cursor:
for statement in parsed_query.get_statements():
annotation = cls.validate_statement(
statement,
database,
cursor,
user_name,
)
if annotation:
annotations.append(annotation)
logging.debug(f'Validation found {len(annotations)} error(s)')
return annotations
def test_is_explain() -> None:
"""
Test that ``EXPLAIN`` is detected correctly.
"""
assert ParsedQuery("EXPLAIN SELECT 1").is_explain() is True
assert ParsedQuery("EXPLAIN SELECT 1").is_select() is False
assert (ParsedQuery("""
-- comment
EXPLAIN select * from table
-- comment 2
""").is_explain() is True)
assert (ParsedQuery("""
-- comment
EXPLAIN select * from table
where col1 = 'something'
-- comment 2
-- comment 3
EXPLAIN select * from table
where col1 = 'something'
-- comment 4
""").is_explain() is True)
assert (ParsedQuery("""
-- This is a comment
-- this is another comment but with a space in the front
EXPLAIN SELECT * FROM TABLE
""").is_explain() is True)
assert (ParsedQuery("""
/* This is a comment
with stars instead */
EXPLAIN SELECT * FROM TABLE
""").is_explain() is True)
assert (ParsedQuery("""
-- comment
select * from table
where col1 = 'something'
-- comment 2
""").is_explain() is False)
def estimate_statement_cost( # pylint: disable=too-many-locals
cls, statement: str, database: "Database", cursor: Any,
user_name: str) -> Dict[str, Any]:
"""
Run a SQL query that estimates the cost of a given statement.
:param statement: A single SQL statement
:param database: Database instance
:param cursor: Cursor instance
:param username: Effective username
:return: JSON response from Presto
"""
parsed_query = ParsedQuery(statement)
sql = parsed_query.stripped()
sql_query_mutator = config["SQL_QUERY_MUTATOR"]
if sql_query_mutator:
sql = sql_query_mutator(sql, user_name, security_manager, database)
sql = f"EXPLAIN (TYPE IO, FORMAT JSON) {sql}"
cursor.execute(sql)
# the output from Presto is a single column and a single row containing
# JSON:
#
# {
# ...
# "estimate" : {
# "outputRowCount" : 8.73265878E8,
# "outputSizeInBytes" : 3.41425774958E11,
# "cpuCost" : 3.41425774958E11,
# "maxMemory" : 0.0,
# "networkCost" : 3.41425774958E11
# }
# }
result = json.loads(cursor.fetchone()[0])
return result
def extract_tables(self, query):
return ParsedQuery(query).tables
def execute_sql_statements(
query_id,
rendered_query,
return_results=True,
store_results=False,
user_name=None,
session=None,
start_time=None,
expand_data=False,
): # pylint: disable=too-many-arguments, too-many-locals, too-many-statements
"""Executes the sql query returns the results."""
if store_results and start_time:
# only asynchronous queries
stats_logger.timing("sqllab.query.time_pending",
now_as_float() - start_time)
query = get_query(query_id, session)
payload = dict(query_id=query_id)
database = query.database
db_engine_spec = database.db_engine_spec
db_engine_spec.patch()
if database.allow_run_async and not results_backend:
raise SqlLabException("Results backend isn't configured.")
# Breaking down into multiple statements
parsed_query = ParsedQuery(rendered_query)
statements = parsed_query.get_statements()
logger.info(f"Query {query_id}: Executing {len(statements)} statement(s)")
logger.info(f"Query {query_id}: Set query to 'running'")
query.status = QueryStatus.RUNNING
query.start_running_time = now_as_float()
session.commit()
engine = database.get_sqla_engine(
schema=query.schema,
nullpool=True,
user_name=user_name,
source=sources.get("sql_lab", None),
)
# Sharing a single connection and cursor across the
# execution of all statements (if many)
with closing(engine.raw_connection()) as conn:
with closing(conn.cursor()) as cursor:
statement_count = len(statements)
for i, statement in enumerate(statements):
# Check if stopped
query = get_query(query_id, session)
if query.status == QueryStatus.STOPPED:
return None
# Run statement
msg = f"Running statement {i+1} out of {statement_count}"
logger.info(f"Query {query_id}: {msg}")
query.set_extra_json_key("progress", msg)
session.commit()
try:
cdf = execute_sql_statement(statement, query, user_name,
session, cursor)
except Exception as e: # pylint: disable=broad-except
msg = str(e)
if statement_count > 1:
msg = f"[Statement {i+1} out of {statement_count}] " + msg
payload = handle_query_error(msg, query, session, payload)
return payload
# Success, updating the query entry in database
query.rows = cdf.size
query.progress = 100
query.set_extra_json_key("progress", None)
if query.select_as_cta:
query.select_sql = database.select_star(
query.tmp_table_name,
limit=query.limit,
schema=database.force_ctas_schema,
show_cols=False,
latest_partition=False,
)
query.end_time = now_as_float()
data, selected_columns, all_columns, expanded_columns = _serialize_and_expand_data(
cdf, db_engine_spec, store_results and results_backend_use_msgpack,
expand_data)
payload.update({
"status": QueryStatus.SUCCESS,
"data": data,
"columns": all_columns,
"selected_columns": selected_columns,
"expanded_columns": expanded_columns,
"query": query.to_dict(),
})
payload["query"]["state"] = QueryStatus.SUCCESS
if store_results and results_backend:
key = str(uuid.uuid4())
logger.info(
f"Query {query_id}: Storing results in results backend, key: {key}"
)
with stats_timing("sqllab.query.results_backend_write", stats_logger):
with stats_timing(
"sqllab.query.results_backend_write_serialization",
stats_logger):
serialized_payload = _serialize_payload(
payload, results_backend_use_msgpack)
cache_timeout = database.cache_timeout
if cache_timeout is None:
cache_timeout = config["CACHE_DEFAULT_TIMEOUT"]
compressed = zlib_compress(serialized_payload)
logger.debug(
f"*** serialized payload size: {getsizeof(serialized_payload)}"
)
logger.debug(
f"*** compressed payload size: {getsizeof(compressed)}")
results_backend.set(key, compressed, cache_timeout)
query.results_key = key
query.status = QueryStatus.SUCCESS
session.commit()
if return_results:
return payload
return None
def execute_sql_statement(sql_statement, query, user_name, session, cursor):
"""Executes a single SQL statement"""
database = query.database
db_engine_spec = database.db_engine_spec
parsed_query = ParsedQuery(sql_statement)
sql = parsed_query.stripped()
if not parsed_query.is_readonly() and not database.allow_dml:
raise SqlLabSecurityException(
_("Only `SELECT` statements are allowed against this database"))
if query.select_as_cta:
if not parsed_query.is_select():
raise SqlLabException(
_("Only `SELECT` statements can be used with the CREATE TABLE "
"feature."))
if not query.tmp_table_name:
start_dttm = datetime.fromtimestamp(query.start_time)
query.tmp_table_name = "tmp_{}_table_{}".format(
query.user_id, start_dttm.strftime("%Y_%m_%d_%H_%M_%S"))
sql = parsed_query.as_create_table(query.tmp_table_name)
query.select_as_cta_used = True
if parsed_query.is_select():
if SQL_MAX_ROW and (not query.limit or query.limit > SQL_MAX_ROW):
query.limit = SQL_MAX_ROW
if query.limit:
sql = database.apply_limit_to_sql(sql, query.limit)
# Hook to allow environment-specific mutation (usually comments) to the SQL
if SQL_QUERY_MUTATOR:
sql = SQL_QUERY_MUTATOR(sql, user_name, security_manager, database)
try:
if log_query:
log_query(
query.database.sqlalchemy_uri,
query.executed_sql,
query.schema,
user_name,
__name__,
security_manager,
)
query.executed_sql = sql
session.commit()
with stats_timing("sqllab.query.time_executing_query", stats_logger):
logger.info(f"Query {query.id}: Running query: \n{sql}")
db_engine_spec.execute(cursor, sql, async_=True)
logger.info(f"Query {query.id}: Handling cursor")
db_engine_spec.handle_cursor(cursor, query, session)
with stats_timing("sqllab.query.time_fetching_results", stats_logger):
logger.debug(
"Query %d: Fetching data for query object: %s",
query.id,
str(query.to_dict()),
)
data = db_engine_spec.fetch_data(cursor, query.limit)
except SoftTimeLimitExceeded as e:
logger.exception(f"Query {query.id}: {e}")
raise SqlLabTimeoutException(
"SQL Lab timeout. This environment's policy is to kill queries "
"after {} seconds.".format(SQLLAB_TIMEOUT))
except Exception as e:
logger.exception(f"Query {query.id}: {e}")
raise SqlLabException(db_engine_spec.extract_error_message(e))
logger.debug(f"Query {query.id}: Fetching cursor description")
cursor_description = cursor.description
return SupersetDataFrame(data, cursor_description, db_engine_spec)
def sql_tables(self) -> List[Table]:
return list(ParsedQuery(self.sql).tables)
def estimate_statement_cost( # pylint: disable=too-many-locals
cls, statement: str, database, cursor,
user_name: str) -> Dict[str, str]:
"""
Generate a SQL query that estimates the cost of a given statement.
:param statement: A single SQL statement
:param database: Database instance
:param cursor: Cursor instance
:param username: Effective username
"""
parsed_query = ParsedQuery(statement)
sql = parsed_query.stripped()
sql_query_mutator = config["SQL_QUERY_MUTATOR"]
if sql_query_mutator:
sql = sql_query_mutator(sql, user_name, security_manager, database)
sql = f"EXPLAIN (TYPE IO, FORMAT JSON) {sql}"
cursor.execute(sql)
# the output from Presto is a single column and a single row containing
# JSON:
#
# {
# ...
# "estimate" : {
# "outputRowCount" : 8.73265878E8,
# "outputSizeInBytes" : 3.41425774958E11,
# "cpuCost" : 3.41425774958E11,
# "maxMemory" : 0.0,
# "networkCost" : 3.41425774958E11
# }
# }
result = json.loads(cursor.fetchone()[0])
estimate = result["estimate"]
def humanize(value: Any, suffix: str) -> str:
try:
value = int(value)
except ValueError:
return str(value)
prefixes = ["K", "M", "G", "T", "P", "E", "Z", "Y"]
prefix = ""
to_next_prefix = 1000
while value > to_next_prefix and prefixes:
prefix = prefixes.pop(0)
value //= to_next_prefix
return f"{value} {prefix}{suffix}"
cost = {}
columns = [
("outputRowCount", "Output count", " rows"),
("outputSizeInBytes", "Output size", "B"),
("cpuCost", "CPU cost", ""),
("maxMemory", "Max memory", "B"),
("networkCost", "Network cost", ""),
]
for key, label, suffix in columns:
if key in estimate:
cost[label] = humanize(estimate[key], suffix)
return cost
def test_get_query_with_new_limit_comment_with_limit(self):
sql = "SELECT * FROM birth_names -- SOME COMMENT WITH LIMIT 555"
parsed = ParsedQuery(sql)
newsql = parsed.set_or_update_query_limit(1000)
self.assertEqual(newsql, sql + "\nLIMIT 1000")
def is_readonly(sql: str) -> bool:
return PrestoEngineSpec.is_readonly_query(ParsedQuery(sql))
def test_update_not_select(self):
sql = ParsedQuery("UPDATE t1 SET col1 = NULL")
self.assertEqual(False, sql.is_select())
def execute_sql_statement(
sql_statement: str,
query: Query,
user_name: Optional[str],
session: Session,
cursor: Any,
log_params: Optional[Dict[str, Any]],
apply_ctas: bool = False,
) -> SupersetResultSet:
"""Executes a single SQL statement"""
database = query.database
db_engine_spec = database.db_engine_spec
parsed_query = ParsedQuery(sql_statement)
sql = parsed_query.stripped()
if not db_engine_spec.is_readonly_query(
parsed_query) and not database.allow_dml:
raise SqlLabSecurityException(
_("Only `SELECT` statements are allowed against this database"))
if apply_ctas:
if not query.tmp_table_name:
start_dttm = datetime.fromtimestamp(query.start_time)
query.tmp_table_name = "tmp_{}_table_{}".format(
query.user_id, start_dttm.strftime("%Y_%m_%d_%H_%M_%S"))
sql = parsed_query.as_create_table(
query.tmp_table_name,
schema_name=query.tmp_schema_name,
method=query.ctas_method,
)
query.select_as_cta_used = True
# Do not apply limit to the CTA queries when SQLLAB_CTAS_NO_LIMIT is set to true
if parsed_query.is_select() and not (query.select_as_cta_used
and SQLLAB_CTAS_NO_LIMIT):
if SQL_MAX_ROW and (not query.limit or query.limit > SQL_MAX_ROW):
query.limit = SQL_MAX_ROW
if query.limit:
sql = database.apply_limit_to_sql(sql, query.limit)
# Hook to allow environment-specific mutation (usually comments) to the SQL
if SQL_QUERY_MUTATOR:
sql = SQL_QUERY_MUTATOR(sql, user_name, security_manager, database)
try:
if log_query:
log_query(
query.database.sqlalchemy_uri,
query.executed_sql,
query.schema,
user_name,
__name__,
security_manager,
log_params,
)
query.executed_sql = sql
session.commit()
with stats_timing("sqllab.query.time_executing_query", stats_logger):
logger.debug("Query %d: Running query: %s", query.id, sql)
db_engine_spec.execute(cursor, sql, async_=True)
logger.debug("Query %d: Handling cursor", query.id)
db_engine_spec.handle_cursor(cursor, query, session)
with stats_timing("sqllab.query.time_fetching_results", stats_logger):
logger.debug(
"Query %d: Fetching data for query object: %s",
query.id,
str(query.to_dict()),
)
data = db_engine_spec.fetch_data(cursor, query.limit)
except SoftTimeLimitExceeded as ex:
logger.error("Query %d: Time limit exceeded", query.id)
logger.debug("Query %d: %s", query.id, ex)
raise SqlLabTimeoutException(
"SQL Lab timeout. This environment's policy is to kill queries "
"after {} seconds.".format(SQLLAB_TIMEOUT))
except Exception as ex:
logger.error("Query %d: %s", query.id, type(ex))
logger.debug("Query %d: %s", query.id, ex)
raise SqlLabException(db_engine_spec.extract_error_message(ex))
logger.debug("Query %d: Fetching cursor description", query.id)
cursor_description = cursor.description
return SupersetResultSet(data, cursor_description, db_engine_spec)
def execute_sql_statement(
sql_statement: str,
query: Query,
user_name: Optional[str],
session: Session,
cursor: Any,
log_params: Optional[Dict[str, Any]],
apply_ctas: bool = False,
) -> SupersetResultSet:
"""Executes a single SQL statement"""
database = query.database
db_engine_spec = database.db_engine_spec
parsed_query = ParsedQuery(sql_statement)
sql = parsed_query.stripped()
# This is a test to see if the query is being
# limited by either the dropdown or the sql.
# We are testing to see if more rows exist than the limit.
increased_limit = None if query.limit is None else query.limit + 1
if not db_engine_spec.is_readonly_query(parsed_query) and not database.allow_dml:
raise SupersetErrorException(
SupersetError(
message=__("Only SELECT statements are allowed against this database."),
error_type=SupersetErrorType.DML_NOT_ALLOWED_ERROR,
level=ErrorLevel.ERROR,
)
)
if apply_ctas:
if not query.tmp_table_name:
start_dttm = datetime.fromtimestamp(query.start_time)
query.tmp_table_name = "tmp_{}_table_{}".format(
query.user_id, start_dttm.strftime("%Y_%m_%d_%H_%M_%S")
)
sql = parsed_query.as_create_table(
query.tmp_table_name,
schema_name=query.tmp_schema_name,
method=query.ctas_method,
)
query.select_as_cta_used = True
# Do not apply limit to the CTA queries when SQLLAB_CTAS_NO_LIMIT is set to true
if db_engine_spec.is_select_query(parsed_query) and not (
query.select_as_cta_used and SQLLAB_CTAS_NO_LIMIT
):
if SQL_MAX_ROW and (not query.limit or query.limit > SQL_MAX_ROW):
query.limit = SQL_MAX_ROW
if query.limit:
# We are fetching one more than the requested limit in order
# to test whether there are more rows than the limit.
# Later, the extra row will be dropped before sending
# the results back to the user.
sql = database.apply_limit_to_sql(sql, increased_limit, force=True)
# Hook to allow environment-specific mutation (usually comments) to the SQL
sql = SQL_QUERY_MUTATOR(sql, user_name, security_manager, database)
try:
query.executed_sql = sql
if log_query:
log_query(
query.database.sqlalchemy_uri,
query.executed_sql,
query.schema,
user_name,
__name__,
security_manager,
log_params,
)
session.commit()
with stats_timing("sqllab.query.time_executing_query", stats_logger):
logger.debug("Query %d: Running query: %s", query.id, sql)
db_engine_spec.execute(cursor, sql, async_=True)
logger.debug("Query %d: Handling cursor", query.id)
db_engine_spec.handle_cursor(cursor, query, session)
with stats_timing("sqllab.query.time_fetching_results", stats_logger):
logger.debug(
"Query %d: Fetching data for query object: %s",
query.id,
str(query.to_dict()),
)
data = db_engine_spec.fetch_data(cursor, increased_limit)
if query.limit is None or len(data) <= query.limit:
query.limiting_factor = LimitingFactor.NOT_LIMITED
else:
# return 1 row less than increased_query
data = data[:-1]
except SoftTimeLimitExceeded as ex:
logger.warning("Query %d: Time limit exceeded", query.id)
logger.debug("Query %d: %s", query.id, ex)
raise SupersetErrorException(
SupersetError(
message=__(
f"The query was killed after {SQLLAB_TIMEOUT} seconds. It might "
"be too complex, or the database might be under heavy load."
),
error_type=SupersetErrorType.SQLLAB_TIMEOUT_ERROR,
level=ErrorLevel.ERROR,
)
)
except Exception as ex:
logger.error("Query %d: %s", query.id, type(ex), exc_info=True)
logger.debug("Query %d: %s", query.id, ex)
raise SqlLabException(db_engine_spec.extract_error_message(ex))
logger.debug("Query %d: Fetching cursor description", query.id)
cursor_description = cursor.description
return SupersetResultSet(data, cursor_description, db_engine_spec)
def execute_sql_statements(
ctask,
query_id,
rendered_query,
return_results=True,
store_results=False,
user_name=None,
session=None,
start_time=None,
):
"""Executes the sql query returns the results."""
if store_results and start_time:
# only asynchronous queries
stats_logger.timing("sqllab.query.time_pending", now_as_float() - start_time)
query = get_query(query_id, session)
payload = dict(query_id=query_id)
database = query.database
db_engine_spec = database.db_engine_spec
db_engine_spec.patch()
if store_results and not results_backend:
raise SqlLabException("Results backend isn't configured.")
# Breaking down into multiple statements
parsed_query = ParsedQuery(rendered_query)
statements = parsed_query.get_statements()
logging.info(f"Executing {len(statements)} statement(s)")
logging.info("Set query to 'running'")
query.status = QueryStatus.RUNNING
query.start_running_time = now_as_float()
engine = database.get_sqla_engine(
schema=query.schema,
nullpool=True,
user_name=user_name,
source=sources.get("sql_lab", None),
)
# Sharing a single connection and cursor across the
# execution of all statements (if many)
with closing(engine.raw_connection()) as conn:
with closing(conn.cursor()) as cursor:
query.connection_id = db_engine_spec.get_connection_id(cursor)
session.commit()
statement_count = len(statements)
for i, statement in enumerate(statements):
# check if the query was stopped
session.refresh(query)
if query.status == QueryStatus.STOPPED:
payload.update({"status": query.status})
return payload
msg = f"Running statement {i+1} out of {statement_count}"
logging.info(msg)
query.set_extra_json_key("progress", msg)
session.commit()
try:
cdf = execute_sql_statement(
statement, query, user_name, session, cursor
)
msg = f"Running statement {i+1} out of {statement_count}"
except Exception as e:
# query can be stopped in another thread/worker
# but in synchronized mode it may lead to an error
# skip error the error in such case
session.refresh(query)
if query.status == QueryStatus.STOPPED:
payload.update({"status": query.status})
return payload
msg = str(e)
if statement_count > 1:
msg = f"[Statement {i+1} out of {statement_count}] " + msg
payload = handle_query_error(msg, query, session, payload)
return payload
# Success, updating the query entry in database
query.rows = cdf.size
query.progress = 100
query.set_extra_json_key("progress", None)
query.connection_id = None
if query.select_as_cta:
query.select_sql = database.select_star(
query.tmp_table_name,
limit=query.limit,
schema=database.force_ctas_schema,
show_cols=False,
latest_partition=False,
)
query.end_time = now_as_float()
selected_columns = cdf.columns or []
data = cdf.data or []
all_columns, data, expanded_columns = db_engine_spec.expand_data(
selected_columns, data
)
payload.update(
{
"status": QueryStatus.SUCCESS,
"data": data,
"columns": all_columns,
"selected_columns": selected_columns,
"expanded_columns": expanded_columns,
"query": query.to_dict(),
}
)
payload["query"]["state"] = QueryStatus.SUCCESS
# go over each row, find bytes columns that start with the magic UAST
# sequence b'\x00bgr', and replace it with a string containing the
# UAST in JSON
for row in payload["data"]:
for k, v in row.items():
if isinstance(v, bytes) and len(v) > 4 and v[0:4] == b"\x00bgr":
try:
ctx = decode(v, format=0)
row[k] = json.dumps(ctx.load())
except Exception:
pass
if store_results:
key = str(uuid.uuid4())
logging.info(f"Storing results in results backend, key: {key}")
with stats_timing("sqllab.query.results_backend_write", stats_logger):
json_payload = json.dumps(
payload, default=json_iso_dttm_ser, ignore_nan=True
)
cache_timeout = database.cache_timeout
if cache_timeout is None:
cache_timeout = config.get("CACHE_DEFAULT_TIMEOUT", 0)
results_backend.set(key, zlib_compress(json_payload), cache_timeout)
query.results_key = key
query.status = QueryStatus.SUCCESS
session.commit()
if return_results:
return payload
def validate_statement(
cls,
statement,
database,
cursor,
user_name,
) -> Optional[SQLValidationAnnotation]:
# pylint: disable=too-many-locals
db_engine_spec = database.db_engine_spec
parsed_query = ParsedQuery(statement)
sql = parsed_query.stripped()
# Hook to allow environment-specific mutation (usually comments) to the SQL
# pylint: disable=invalid-name
SQL_QUERY_MUTATOR = config.get('SQL_QUERY_MUTATOR')
if SQL_QUERY_MUTATOR:
sql = SQL_QUERY_MUTATOR(sql, user_name, security_manager, database)
# Transform the final statement to an explain call before sending it on
# to presto to validate
sql = f'EXPLAIN (TYPE VALIDATE) {sql}'
# Invoke the query against presto. NB this deliberately doesn't use the
# engine spec's handle_cursor implementation since we don't record
# these EXPLAIN queries done in validation as proper Query objects
# in the superset ORM.
from pyhive.exc import DatabaseError
try:
db_engine_spec.execute(cursor, sql)
polled = cursor.poll()
while polled:
logging.info('polling presto for validation progress')
stats = polled.get('stats', {})
if stats:
state = stats.get('state')
if state == 'FINISHED':
break
time.sleep(0.2)
polled = cursor.poll()
db_engine_spec.fetch_data(cursor, MAX_ERROR_ROWS)
return None
except DatabaseError as db_error:
# The pyhive presto client yields EXPLAIN (TYPE VALIDATE) responses
# as though they were normal queries. In other words, it doesn't
# know that errors here are not exceptional. To map this back to
# ordinary control flow, we have to trap the category of exception
# raised by the underlying client, match the exception arguments
# pyhive provides against the shape of dictionary for a presto query
# invalid error, and restructure that error as an annotation we can
# return up.
# Confirm the first element in the DatabaseError constructor is a
# dictionary with error information. This is currently provided by
# the pyhive client, but may break if their interface changes when
# we update at some point in the future.
if not db_error.args or not isinstance(db_error.args[0], dict):
raise PrestoSQLValidationError(
'The pyhive presto client returned an unhandled '
'database error.',
) from db_error
error_args: Dict[str, Any] = db_error.args[0]
# Confirm the two fields we need to be able to present an annotation
# are present in the error response -- a message, and a location.
if 'message' not in error_args:
raise PrestoSQLValidationError(
'The pyhive presto client did not report an error message',
) from db_error
if 'errorLocation' not in error_args:
raise PrestoSQLValidationError(
'The pyhive presto client did not report an error location',
) from db_error
# Pylint is confused about the type of error_args, despite the hints
# and checks above.
# pylint: disable=invalid-sequence-index
message = error_args['message']
err_loc = error_args['errorLocation']
line_number = err_loc.get('lineNumber', None)
start_column = err_loc.get('columnNumber', None)
end_column = err_loc.get('columnNumber', None)
return SQLValidationAnnotation(
message=message,
line_number=line_number,
start_column=start_column,
end_column=end_column,
)
except Exception as e:
logging.exception(f'Unexpected error running validation query: {e}')
raise e
def test_explain(self):
sql = ParsedQuery("EXPLAIN SELECT 1")
self.assertEqual(True, sql.is_explain())
self.assertEqual(False, sql.is_select())
def test_update() -> None:
"""
Test that ``UPDATE`` is not detected as ``SELECT``.
"""
assert ParsedQuery("UPDATE t1 SET col1 = NULL").is_select() is False
def execute_sql_statements( # pylint: disable=too-many-arguments, too-many-locals, too-many-statements, too-many-branches
query_id: int,
rendered_query: str,
return_results: bool,
store_results: bool,
user_name: Optional[str],
session: Session,
start_time: Optional[float],
expand_data: bool,
log_params: Optional[Dict[str, Any]],
) -> Optional[Dict[str, Any]]:
"""Executes the sql query returns the results."""
if store_results and start_time:
# only asynchronous queries
stats_logger.timing("sqllab.query.time_pending", now_as_float() - start_time)
query = get_query(query_id, session)
payload: Dict[str, Any] = dict(query_id=query_id)
database = query.database
db_engine_spec = database.db_engine_spec
db_engine_spec.patch()
if database.allow_run_async and not results_backend:
raise SupersetErrorException(
SupersetError(
message=__("Results backend is not configured."),
error_type=SupersetErrorType.RESULTS_BACKEND_NOT_CONFIGURED_ERROR,
level=ErrorLevel.ERROR,
)
)
# Breaking down into multiple statements
parsed_query = ParsedQuery(rendered_query, strip_comments=True)
if not db_engine_spec.run_multiple_statements_as_one:
statements = parsed_query.get_statements()
logger.info(
"Query %s: Executing %i statement(s)", str(query_id), len(statements)
)
else:
statements = [rendered_query]
logger.info("Query %s: Executing query as a single statement", str(query_id))
logger.info("Query %s: Set query to 'running'", str(query_id))
query.status = QueryStatus.RUNNING
query.start_running_time = now_as_float()
session.commit()
# Should we create a table or view from the select?
if (
query.select_as_cta
and query.ctas_method == CtasMethod.TABLE
and not parsed_query.is_valid_ctas()
):
raise SupersetErrorException(
SupersetError(
message=__(
"CTAS (create table as select) can only be run with a query where "
"the last statement is a SELECT. Please make sure your query has "
"a SELECT as its last statement. Then, try running your query "
"again."
),
error_type=SupersetErrorType.INVALID_CTAS_QUERY_ERROR,
level=ErrorLevel.ERROR,
)
)
if (
query.select_as_cta
and query.ctas_method == CtasMethod.VIEW
and not parsed_query.is_valid_cvas()
):
raise SupersetErrorException(
SupersetError(
message=__(
"CVAS (create view as select) can only be run with a query with "
"a single SELECT statement. Please make sure your query has only "
"a SELECT statement. Then, try running your query again."
),
error_type=SupersetErrorType.INVALID_CVAS_QUERY_ERROR,
level=ErrorLevel.ERROR,
)
)
engine = database.get_sqla_engine(
schema=query.schema,
nullpool=True,
user_name=user_name,
source=QuerySource.SQL_LAB,
)
# Sharing a single connection and cursor across the
# execution of all statements (if many)
with closing(engine.raw_connection()) as conn:
# closing the connection closes the cursor as well
cursor = conn.cursor()
statement_count = len(statements)
for i, statement in enumerate(statements):
# Check if stopped
query = get_query(query_id, session)
if query.status == QueryStatus.STOPPED:
return None
# For CTAS we create the table only on the last statement
apply_ctas = query.select_as_cta and (
query.ctas_method == CtasMethod.VIEW
or (query.ctas_method == CtasMethod.TABLE and i == len(statements) - 1)
)
# Run statement
msg = f"Running statement {i+1} out of {statement_count}"
logger.info("Query %s: %s", str(query_id), msg)
query.set_extra_json_key("progress", msg)
session.commit()
try:
result_set = execute_sql_statement(
statement,
query,
user_name,
session,
cursor,
log_params,
apply_ctas,
)
except Exception as ex: # pylint: disable=broad-except
msg = str(ex)
prefix_message = (
f"[Statement {i+1} out of {statement_count}]"
if statement_count > 1
else ""
)
payload = handle_query_error(
ex, query, session, payload, prefix_message
)
return payload
# Commit the connection so CTA queries will create the table.
conn.commit()
# Success, updating the query entry in database
query.rows = result_set.size
query.progress = 100
query.set_extra_json_key("progress", None)
if query.select_as_cta:
query.select_sql = database.select_star(
query.tmp_table_name,
schema=query.tmp_schema_name,
limit=query.limit,
show_cols=False,
latest_partition=False,
)
query.end_time = now_as_float()
use_arrow_data = store_results and cast(bool, results_backend_use_msgpack)
data, selected_columns, all_columns, expanded_columns = _serialize_and_expand_data(
result_set, db_engine_spec, use_arrow_data, expand_data
)
# TODO: data should be saved separately from metadata (likely in Parquet)
payload.update(
{
"status": QueryStatus.SUCCESS,
"data": data,
"columns": all_columns,
"selected_columns": selected_columns,
"expanded_columns": expanded_columns,
"query": query.to_dict(),
}
)
payload["query"]["state"] = QueryStatus.SUCCESS
if store_results and results_backend:
key = str(uuid.uuid4())
logger.info(
"Query %s: Storing results in results backend, key: %s", str(query_id), key
)
with stats_timing("sqllab.query.results_backend_write", stats_logger):
with stats_timing(
"sqllab.query.results_backend_write_serialization", stats_logger
):
serialized_payload = _serialize_payload(
payload, cast(bool, results_backend_use_msgpack)
)
cache_timeout = database.cache_timeout
if cache_timeout is None:
cache_timeout = config["CACHE_DEFAULT_TIMEOUT"]
compressed = zlib_compress(serialized_payload)
logger.debug(
"*** serialized payload size: %i", getsizeof(serialized_payload)
)
logger.debug("*** compressed payload size: %i", getsizeof(compressed))
results_backend.set(key, compressed, cache_timeout)
query.results_key = key
query.status = QueryStatus.SUCCESS
session.commit()
if return_results:
# since we're returning results we need to create non-arrow data
if use_arrow_data:
(
data,
selected_columns,
all_columns,
expanded_columns,
) = _serialize_and_expand_data(
result_set, db_engine_spec, False, expand_data
)
payload.update(
{
"data": data,
"columns": all_columns,
"selected_columns": selected_columns,
"expanded_columns": expanded_columns,
}
)
return payload
return None
def execute_sql_statements( # pylint: disable=too-many-arguments, too-many-locals, too-many-statements
query_id: int,
rendered_query: str,
return_results: bool,
store_results: bool,
user_name: Optional[str],
session: Session,
start_time: Optional[float],
expand_data: bool,
log_params: Optional[Dict[str, Any]],
) -> Optional[Dict[str, Any]]:
"""Executes the sql query returns the results."""
if store_results and start_time:
# only asynchronous queries
stats_logger.timing("sqllab.query.time_pending",
now_as_float() - start_time)
query = get_query(query_id, session)
payload: Dict[str, Any] = dict(query_id=query_id)
database = query.database
db_engine_spec = database.db_engine_spec
db_engine_spec.patch()
if database.allow_run_async and not results_backend:
raise SqlLabException("Results backend isn't configured.")
# Breaking down into multiple statements
parsed_query = ParsedQuery(rendered_query)
statements = parsed_query.get_statements()
logger.info("Query %s: Executing %i statement(s)", str(query_id),
len(statements))
logger.info("Query %s: Set query to 'running'", str(query_id))
query.status = QueryStatus.RUNNING
query.start_running_time = now_as_float()
session.commit()
engine = database.get_sqla_engine(
schema=query.schema,
nullpool=True,
user_name=user_name,
source=QuerySource.SQL_LAB,
)
# Sharing a single connection and cursor across the
# execution of all statements (if many)
with closing(engine.raw_connection()) as conn:
with closing(conn.cursor()) as cursor:
statement_count = len(statements)
for i, statement in enumerate(statements):
# Check if stopped
query = get_query(query_id, session)
if query.status == QueryStatus.STOPPED:
return None
# Run statement
msg = f"Running statement {i+1} out of {statement_count}"
logger.info("Query %s: %s", str(query_id), msg)
query.set_extra_json_key("progress", msg)
session.commit()
try:
result_set = execute_sql_statement(statement, query,
user_name, session,
cursor, log_params)
except Exception as ex: # pylint: disable=broad-except
msg = str(ex)
if statement_count > 1:
msg = f"[Statement {i+1} out of {statement_count}] " + msg
payload = handle_query_error(msg, query, session, payload)
return payload
# Commit the connection so CTA queries will create the table.
conn.commit()
# Success, updating the query entry in database
query.rows = result_set.size
query.progress = 100
query.set_extra_json_key("progress", None)
if query.select_as_cta:
query.select_sql = database.select_star(
query.tmp_table_name,
schema=query.tmp_schema_name,
limit=query.limit,
show_cols=False,
latest_partition=False,
)
query.end_time = now_as_float()
use_arrow_data = store_results and cast(bool, results_backend_use_msgpack)
data, selected_columns, all_columns, expanded_columns = _serialize_and_expand_data(
result_set, db_engine_spec, use_arrow_data, expand_data)
# TODO: data should be saved separately from metadata (likely in Parquet)
payload.update({
"status": QueryStatus.SUCCESS,
"data": data,
"columns": all_columns,
"selected_columns": selected_columns,
"expanded_columns": expanded_columns,
"query": query.to_dict(),
})
payload["query"]["state"] = QueryStatus.SUCCESS
if store_results and results_backend:
key = str(uuid.uuid4())
logger.info("Query %s: Storing results in results backend, key: %s",
str(query_id), key)
with stats_timing("sqllab.query.results_backend_write", stats_logger):
with stats_timing(
"sqllab.query.results_backend_write_serialization",
stats_logger):
serialized_payload = _serialize_payload(
payload, cast(bool, results_backend_use_msgpack))
cache_timeout = database.cache_timeout
if cache_timeout is None:
cache_timeout = config["CACHE_DEFAULT_TIMEOUT"]
compressed = zlib_compress(serialized_payload)
logger.debug("*** serialized payload size: %i",
getsizeof(serialized_payload))
logger.debug("*** compressed payload size: %i",
getsizeof(compressed))
results_backend.set(key, compressed, cache_timeout)
query.results_key = key
query.status = QueryStatus.SUCCESS
session.commit()
if return_results:
# since we're returning results we need to create non-arrow data
if use_arrow_data:
(
data,
selected_columns,
all_columns,
expanded_columns,
) = _serialize_and_expand_data(result_set, db_engine_spec, False,
expand_data)
payload.update({
"data": data,
"columns": all_columns,
"selected_columns": selected_columns,
"expanded_columns": expanded_columns,
})
return payload
return None
def get_table_metadata(database: Database, table_name: str,
schema_name: Optional[str]) -> Dict:
"""
Get table metadata information, including type, pk, fks.
This function raises SQLAlchemyError when a schema is not found.
:param database: The database model
:param table_name: Table name
:param schema_name: schema name
:return: Dict table metadata ready for API response
"""
keys: List = []
columns = database.get_columns(table_name, schema_name)
# define comment dict by tsl
comment_dict = {}
primary_key = database.get_pk_constraint(table_name, schema_name)
if primary_key and primary_key.get("constrained_columns"):
primary_key["column_names"] = primary_key.pop("constrained_columns")
primary_key["type"] = "pk"
keys += [primary_key]
# get dialect name
dialect_name = database.get_dialect().name
if isinstance(dialect_name, bytes):
dialect_name = dialect_name.decode()
# get column comment, presto & hive
if dialect_name == "presto" or dialect_name == "hive":
db_engine_spec = database.db_engine_spec
sql = ParsedQuery("desc {a}.{b}".format(a=schema_name,
b=table_name)).stripped()
engine = database.get_sqla_engine(schema_name)
conn = engine.raw_connection()
cursor = conn.cursor()
query = Query()
session = Session(bind=engine)
query.executed_sql = sql
query.__tablename__ = table_name
session.commit()
db_engine_spec.execute(cursor, sql, async_=False)
data = db_engine_spec.fetch_data(cursor, query.limit)
# parse list data into dict by tsl; hive and presto is different
if dialect_name == "presto":
for d in data:
d[3]
comment_dict[d[0]] = d[3]
else:
for d in data:
d[2]
comment_dict[d[0]] = d[2]
conn.commit()
foreign_keys = get_foreign_keys_metadata(database, table_name, schema_name)
indexes = get_indexes_metadata(database, table_name, schema_name)
keys += foreign_keys + indexes
payload_columns: List[Dict] = []
for col in columns:
dtype = get_col_type(col)
if len(comment_dict) > 0:
payload_columns.append({
"name":
col["name"],
"type":
dtype.split("(")[0] if "(" in dtype else dtype,
"longType":
dtype,
"keys":
[k for k in keys if col["name"] in k.get("column_names")],
"comment":
comment_dict[col["name"]],
})
elif dialect_name == "mysql":
payload_columns.append({
"name":
col["name"],
"type":
dtype.split("(")[0] if "(" in dtype else dtype,
"longType":
dtype,
"keys":
[k for k in keys if col["name"] in k.get("column_names")],
"comment":
col["comment"],
})
else:
payload_columns.append({
"name":
col["name"],
"type":
dtype.split("(")[0] if "(" in dtype else dtype,
"longType":
dtype,
"keys":
[k for k in keys if col["name"] in k.get("column_names")],
# "comment": col["comment"],
})
return {
"name":
table_name,
"columns":
payload_columns,
"selectStar":
database.select_star(
table_name,
schema=schema_name,
show_cols=True,
indent=True,
cols=columns,
latest_partition=True,
),
"primaryKey":
primary_key,
"foreignKeys":
foreign_keys,
"indexes":
keys,
}
def extract_tables(query: str) -> Set[Table]:
"""
Helper function to extract tables referenced in a query.
"""
return ParsedQuery(query).tables
