sql_lab.py

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
# pylint: disable=consider-using-transaction
import dataclasses
import logging
import uuid
from contextlib import closing
from datetime import datetime
from sys import getsizeof
from typing import Any, cast, Optional, Union

import backoff
import msgpack
from celery.exceptions import SoftTimeLimitExceeded
from flask import current_app
from flask_babel import gettext as __

from superset import (
    app,
    db,
    is_feature_enabled,
    results_backend,
    results_backend_use_msgpack,
    security_manager,
)
from superset.common.db_query_status import QueryStatus
from superset.constants import QUERY_CANCEL_KEY, QUERY_EARLY_CANCEL_KEY
from superset.dataframe import df_to_records
from superset.db_engine_specs import BaseEngineSpec
from superset.errors import ErrorLevel, SupersetError, SupersetErrorType
from superset.exceptions import (
    OAuth2RedirectError,
    SupersetErrorException,
    SupersetErrorsException,
)
from superset.extensions import celery_app, event_logger
from superset.models.core import Database
from superset.models.sql_lab import Query
from superset.result_set import SupersetResultSet
from superset.sql_parse import (
    CtasMethod,
    insert_rls_as_subquery,
    insert_rls_in_predicate,
    ParsedQuery,
    Table,
)
from superset.sqllab.limiting_factor import LimitingFactor
from superset.sqllab.utils import write_ipc_buffer
from superset.utils import json
from superset.utils.core import (
    override_user,
    QuerySource,
    zlib_compress,
)
from superset.utils.dates import now_as_float
from superset.utils.decorators import stats_timing

config = app.config
stats_logger = config["STATS_LOGGER"]
SQLLAB_TIMEOUT = config["SQLLAB_ASYNC_TIME_LIMIT_SEC"]
SQLLAB_HARD_TIMEOUT = SQLLAB_TIMEOUT + 60
SQL_MAX_ROW = config["SQL_MAX_ROW"]
SQLLAB_CTAS_NO_LIMIT = config["SQLLAB_CTAS_NO_LIMIT"]
log_query = config["QUERY_LOGGER"]
logger = logging.getLogger(__name__)


class SqlLabException(Exception):
    pass


class SqlLabSecurityException(SqlLabException):
    pass


class SqlLabQueryStoppedException(SqlLabException):
    pass


def handle_query_error(
    ex: Exception,
    query: Query,
    payload: Optional[dict[str, Any]] = None,
    prefix_message: str = "",
) -> dict[str, Any]:
    """Local method handling error while processing the SQL"""
    payload = payload or {}
    msg = f"{prefix_message} {str(ex)}".strip()
    query.error_message = msg
    query.tmp_table_name = None
    query.status = QueryStatus.FAILED
    # TODO: re-enable this after updating the frontend to properly display timeout status
    # if query.status != QueryStatus.TIMED_OUT:
    #   query.status = QueryStatus.FAILED
    if not query.end_time:
        query.end_time = now_as_float()

    # extract DB-specific errors (invalid column, eg)
    if isinstance(ex, SupersetErrorException):
        errors = [ex.error]
    elif isinstance(ex, SupersetErrorsException):
        errors = ex.errors
    else:
        errors = query.database.db_engine_spec.extract_errors(str(ex))

    errors_payload = [dataclasses.asdict(error) for error in errors]
    if errors:
        query.set_extra_json_key("errors", errors_payload)

    db.session.commit()
    payload.update({"status": query.status, "error": msg, "errors": errors_payload})
    if troubleshooting_link := config["TROUBLESHOOTING_LINK"]:
        payload["link"] = troubleshooting_link
    return payload


def get_query_backoff_handler(details: dict[Any, Any]) -> None:
    query_id = details["kwargs"]["query_id"]
    logger.error(
        "Query with id `%s` could not be retrieved", str(query_id), exc_info=True
    )
    stats_logger.incr(f"error_attempting_orm_query_{details['tries'] - 1}")
    logger.error(
        "Query %s: Sleeping for a sec before retrying...", str(query_id), exc_info=True
    )


def get_query_giveup_handler(_: Any) -> None:
    stats_logger.incr("error_failed_at_getting_orm_query")


@backoff.on_exception(
    backoff.constant,
    SqlLabException,
    interval=1,
    on_backoff=get_query_backoff_handler,
    on_giveup=get_query_giveup_handler,
    max_tries=5,
)
def get_query(query_id: int) -> Query:
    """attempts to get the query and retry if it cannot"""
    try:
        return db.session.query(Query).filter_by(id=query_id).one()
    except Exception as ex:
        raise SqlLabException("Failed at getting query") from ex


@celery_app.task(
    name="sql_lab.get_sql_results",
    time_limit=SQLLAB_HARD_TIMEOUT,
    soft_time_limit=SQLLAB_TIMEOUT,
)
def get_sql_results(  # pylint: disable=too-many-arguments
    query_id: int,
    rendered_query: str,
    return_results: bool = True,
    store_results: bool = False,
    username: Optional[str] = None,
    start_time: Optional[float] = None,
    expand_data: bool = False,
    log_params: Optional[dict[str, Any]] = None,
) -> Optional[dict[str, Any]]:
    """Executes the sql query returns the results."""
    with current_app.test_request_context():
        with override_user(security_manager.find_user(username)):
            try:
                return execute_sql_statements(
                    query_id,
                    rendered_query,
                    return_results,
                    store_results,
                    start_time=start_time,
                    expand_data=expand_data,
                    log_params=log_params,
                )
            except Exception as ex:  # pylint: disable=broad-except
                logger.debug("Query %d: %s", query_id, ex)
                stats_logger.incr("error_sqllab_unhandled")
                query = get_query(query_id)
                return handle_query_error(ex, query)


def execute_sql_statement(  # pylint: disable=too-many-statements
    sql_statement: str,
    query: Query,
    cursor: Any,
    log_params: Optional[dict[str, Any]],
    apply_ctas: bool = False,
) -> SupersetResultSet:
    """Executes a single SQL statement"""
    database: Database = query.database
    db_engine_spec = database.db_engine_spec

    parsed_query = ParsedQuery(sql_statement, engine=db_engine_spec.engine)
    if is_feature_enabled("RLS_IN_SQLLAB"):
        # There are two ways to insert RLS: either replacing the table with a subquery
        # that has the RLS, or appending the RLS to the ``WHERE`` clause. The former is
        # safer, but not supported in all databases.
        insert_rls = (
            insert_rls_as_subquery
            if database.db_engine_spec.allows_subqueries
            and database.db_engine_spec.allows_alias_in_select
            else insert_rls_in_predicate
        )

        # Insert any applicable RLS predicates
        parsed_query = ParsedQuery(
            str(
                insert_rls(
                    parsed_query._parsed[0],  # pylint: disable=protected-access
                    database.id,
                    query.schema,
                )
            ),
            engine=db_engine_spec.engine,
        )

    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 = (
                f'tmp_{query.user_id}_table_{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
        sql = apply_limit_if_exists(database, increased_limit, query, sql)

    # Hook to allow environment-specific mutation (usually comments) to the SQL
    sql = database.mutate_sql_based_on_config(sql)
    try:
        query.executed_sql = sql
        if log_query:
            log_query(
                query.database.sqlalchemy_uri,
                query.executed_sql,
                query.schema,
                __name__,
                security_manager,
                log_params,
            )
        db.session.commit()
        with event_logger.log_context(
            action="execute_sql",
            database=database,
            object_ref=__name__,
        ):
            with stats_timing("sqllab.query.time_executing_query", stats_logger):
                db_engine_spec.execute_with_cursor(cursor, sql, query)

            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:
        query.status = QueryStatus.TIMED_OUT

        logger.warning("Query %d: Time limit exceeded", query.id)
        logger.debug("Query %d: %s", query.id, ex)
        raise SupersetErrorException(
            SupersetError(
                message=__(
                    "The query was killed after %(sqllab_timeout)s seconds. It might "
                    "be too complex, or the database might be under heavy load.",
                    sqllab_timeout=SQLLAB_TIMEOUT,
                ),
                error_type=SupersetErrorType.SQLLAB_TIMEOUT_ERROR,
                level=ErrorLevel.ERROR,
            )
        ) from ex
    except OAuth2RedirectError:
        # user needs to authenticate with OAuth2 in order to run query
        raise
    except Exception as ex:
        # query is stopped in another thread/worker
        # stopping raises expected exceptions which we should skip
        db.session.refresh(query)
        if query.status == QueryStatus.STOPPED:
            raise SqlLabQueryStoppedException() from ex

        logger.debug("Query %d: %s", query.id, ex)
        raise SqlLabException(db_engine_spec.extract_error_message(ex)) from ex

    logger.debug("Query %d: Fetching cursor description", query.id)
    cursor_description = cursor.description
    return SupersetResultSet(data, cursor_description, db_engine_spec)


def apply_limit_if_exists(
    database: Database, increased_limit: Optional[int], query: Query, sql: str
) -> str:
    if query.limit and increased_limit:
        # We are fetching one more than the requested limit in order
        # to test whether there are more rows than the limit. According to the DB
        # Engine support it will choose top or limit parse
        # 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)
    return sql


def _serialize_payload(
    payload: dict[Any, Any], use_msgpack: Optional[bool] = False
) -> Union[bytes, str]:
    logger.debug("Serializing to msgpack: %r", use_msgpack)
    if use_msgpack:
        return msgpack.dumps(payload, default=json.json_iso_dttm_ser, use_bin_type=True)

    return json.dumps(payload, default=json.json_iso_dttm_ser, ignore_nan=True)


def _serialize_and_expand_data(
    result_set: SupersetResultSet,
    db_engine_spec: BaseEngineSpec,
    use_msgpack: Optional[bool] = False,
    expand_data: bool = False,
) -> tuple[Union[bytes, str], list[Any], list[Any], list[Any]]:
    selected_columns = result_set.columns
    all_columns: list[Any]
    expanded_columns: list[Any]

    if use_msgpack:
        with stats_timing(
            "sqllab.query.results_backend_pa_serialization", stats_logger
        ):
            data = write_ipc_buffer(result_set.pa_table).to_pybytes()

        # expand when loading data from results backend
        all_columns, expanded_columns = (selected_columns, [])
    else:
        df = result_set.to_pandas_df()
        data = df_to_records(df) or []

        if expand_data:
            all_columns, data, expanded_columns = db_engine_spec.expand_data(
                selected_columns, data
            )
        else:
            all_columns = selected_columns
            expanded_columns = []

    return (data, selected_columns, all_columns, expanded_columns)


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,
    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)
    payload: dict[str, Any] = {"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,
        engine=db_engine_spec.engine,
    )
    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()
    db.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,
            )
        )

    with database.get_raw_connection(
        catalog=query.catalog,
        schema=query.schema,
        source=QuerySource.SQL_LAB,
    ) as conn:
        # Sharing a single connection and cursor across the
        # execution of all statements (if many)
        cursor = conn.cursor()
        cancel_query_id = db_engine_spec.get_cancel_query_id(cursor, query)
        if cancel_query_id is not None:
            query.set_extra_json_key(QUERY_CANCEL_KEY, cancel_query_id)
            db.session.commit()
        statement_count = len(statements)
        for i, statement in enumerate(statements):
            # Check if stopped
            db.session.refresh(query)
            if query.status == QueryStatus.STOPPED:
                payload.update({"status": query.status})
                return payload
            # 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 = __(
                "Running statement %(statement_num)s out of %(statement_count)s",
                statement_num=i + 1,
                statement_count=statement_count,
            )
            logger.info("Query %s: %s", str(query_id), msg)
            query.set_extra_json_key("progress", msg)
            db.session.commit()
            try:
                result_set = execute_sql_statement(
                    statement,
                    query,
                    cursor,
                    log_params,
                    apply_ctas,
                )
            except SqlLabQueryStoppedException:
                payload.update({"status": QueryStatus.STOPPED})
                return payload
            except Exception as ex:  # pylint: disable=broad-except
                msg = str(ex)
                prefix_message = (
                    __(
                        "Statement %(statement_num)s out of %(statement_count)s",
                        statement_num=i + 1,
                        statement_count=statement_count,
                    )
                    if statement_count > 1
                    else ""
                )
                payload = handle_query_error(ex, query, payload, prefix_message)
                return payload

        # Commit the connection so CTA queries will create the table and any DML.
        should_commit = (
            not db_engine_spec.is_select_query(parsed_query)  # check if query is DML
            or apply_ctas
        )
        if should_commit:
            conn.commit()

    # Success, updating the query entry in database
    query.rows = result_set.size
    query.progress = 100
    query.set_extra_json_key("progress", None)
    query.set_extra_json_key("columns", result_set.columns)
    if query.select_as_cta:
        query.select_sql = database.select_star(
            Table(query.tmp_table_name, 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())
        payload["query"]["resultsKey"] = key
        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
    db.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 cancel_query(query: Query) -> bool:
    """
    Cancel a running query.

    Note some engines implicitly handle the cancelation of a query and thus no explicit
    action is required.

    :param query: Query to cancel
    :return: True if query cancelled successfully, False otherwise
    """

    if query.database.db_engine_spec.has_implicit_cancel():
        return True

    # Some databases may need to make preparations for query cancellation
    query.database.db_engine_spec.prepare_cancel_query(query)

    if query.extra.get(QUERY_EARLY_CANCEL_KEY):
        # Query has been cancelled prior to being able to set the cancel key.
        # This can happen if the query cancellation key can only be acquired after the
        # query has been executed
        return True

    cancel_query_id = query.extra.get(QUERY_CANCEL_KEY)
    if cancel_query_id is None:
        return False

    with query.database.get_sqla_engine(
        catalog=query.catalog,
        schema=query.schema,
        source=QuerySource.SQL_LAB,
    ) as engine:
        with closing(engine.raw_connection()) as conn:
            with closing(conn.cursor()) as cursor:
                return query.database.db_engine_spec.cancel_query(
                    cursor, query, cancel_query_id
                )