from time import sleep from datetime import datetime import json import logging import pandas as pd import sqlalchemy import uuid from celery.exceptions import SoftTimeLimitExceeded from sqlalchemy.pool import NullPool from sqlalchemy.orm import sessionmaker from superset import ( app, db, utils, dataframe, results_backend) from superset.models.sql_lab import Query from superset.sql_parse import SupersetQuery from superset.db_engine_specs import LimitMethod from superset.jinja_context import get_template_processor from superset.utils import QueryStatus, get_celery_app config = app.config celery_app = get_celery_app(config) stats_logger = app.config.get('STATS_LOGGER') SQLLAB_TIMEOUT = config.get('SQLLAB_ASYNC_TIME_LIMIT_SEC', 600) class SqlLabException(Exception): pass def dedup(l, suffix='__'): """De-duplicates a list of string by suffixing a counter Always returns the same number of entries as provided, and always returns unique values. >>> dedup(['foo', 'bar', 'bar', 'bar']) ['foo', 'bar', 'bar__1', 'bar__2'] """ new_l = [] seen = {} for s in l: if s in seen: seen[s] += 1 s += suffix + str(seen[s]) else: seen[s] = 0 new_l.append(s) return new_l def get_query(query_id, session, retry_count=5): """attemps to get the query and retry if it cannot""" query = None attempt = 0 while not query and attempt < retry_count: try: query = session.query(Query).filter_by(id=query_id).one() except Exception: attempt += 1 logging.error( "Query with id `{}` could not be retrieved".format(query_id)) stats_logger.incr('error_attempting_orm_query_' + str(attempt)) logging.error("Sleeping for a sec before retrying...") sleep(1) if not query: stats_logger.incr('error_failed_at_getting_orm_query') raise SqlLabException("Failed at getting query") return query def get_session(nullpool): if nullpool: engine = sqlalchemy.create_engine( app.config.get('SQLALCHEMY_DATABASE_URI'), poolclass=NullPool) session_class = sessionmaker() session_class.configure(bind=engine) return session_class() else: session = db.session() session.commit() # HACK return session @celery_app.task(bind=True, soft_time_limit=SQLLAB_TIMEOUT) def get_sql_results( ctask, query_id, return_results=True, store_results=False): """Executes the sql query returns the results.""" try: return execute_sql( ctask, query_id, return_results, store_results) except Exception as e: logging.exception(e) stats_logger.incr('error_sqllab_unhandled') sesh = get_session(not ctask.request.called_directly) query = get_query(query_id, sesh) query.error_message = str(e) query.status = QueryStatus.FAILED query.tmp_table_name = None sesh.commit() def execute_sql(ctask, query_id, return_results=True, store_results=False): """Executes the sql query returns the results.""" session = get_session(not ctask.request.called_directly) 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() def handle_error(msg): """Local method handling error while processing the SQL""" query.error_message = msg query.status = QueryStatus.FAILED query.tmp_table_name = None session.commit() payload.update({ 'status': query.status, 'error_essage': msg, }) return payload if store_results and not results_backend: return handle_error("Results backend isn't configured.") # Limit enforced only for retrieving the data, not for the CTA queries. superset_query = SupersetQuery(query.sql) executed_sql = superset_query.stripped() if not superset_query.is_select() and not database.allow_dml: return handle_error( "Only `SELECT` statements are allowed against this database") if query.select_as_cta: if not superset_query.is_select(): return handle_error( "Only `SELECT` statements can be used with the CREATE TABLE " "feature.") return 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')) executed_sql = superset_query.as_create_table(query.tmp_table_name) query.select_as_cta_used = True elif ( query.limit and superset_query.is_select() and db_engine_spec.limit_method == LimitMethod.WRAP_SQL): executed_sql = database.wrap_sql_limit(executed_sql, query.limit) query.limit_used = True try: template_processor = get_template_processor( database=database, query=query) executed_sql = template_processor.process_template(executed_sql) executed_sql = db_engine_spec.sql_preprocessor(executed_sql) except Exception as e: logging.exception(e) msg = "Template rendering failed: " + utils.error_msg_from_exception(e) return handle_error(msg) query.executed_sql = executed_sql query.status = QueryStatus.RUNNING query.start_running_time = utils.now_as_float() session.merge(query) session.commit() logging.info("Set query to 'running'") engine = database.get_sqla_engine( schema=query.schema, nullpool=not ctask.request.called_directly) try: engine = database.get_sqla_engine( schema=query.schema, nullpool=not ctask.request.called_directly) conn = engine.raw_connection() cursor = conn.cursor() logging.info("Running query: \n{}".format(executed_sql)) logging.info(query.executed_sql) cursor.execute( query.executed_sql, **db_engine_spec.cursor_execute_kwargs) logging.info("Handling cursor") db_engine_spec.handle_cursor(cursor, query, session) logging.info("Fetching data: {}".format(query.to_dict())) data = db_engine_spec.fetch_data(cursor, query.limit) except SoftTimeLimitExceeded as e: logging.exception(e) conn.close() return handle_error( "SQL Lab timeout. This environment's policy is to kill queries " "after {} seconds.".format(SQLLAB_TIMEOUT)) except Exception as e: logging.exception(e) conn.close() return handle_error(db_engine_spec.extract_error_message(e)) conn.commit() conn.close() if query.status == utils.QueryStatus.STOPPED: return json.dumps({ 'query_id': query.id, 'status': query.status, 'query': query.to_dict(), }, default=utils.json_iso_dttm_ser) column_names = ( [col[0] for col in cursor.description] if cursor.description else []) column_names = dedup(column_names) cdf = dataframe.SupersetDataFrame(pd.DataFrame( list(data), columns=column_names)) query.rows = cdf.size query.progress = 100 query.status = QueryStatus.SUCCESS if query.select_as_cta: query.select_sql = '{}'.format(database.select_star( query.tmp_table_name, limit=query.limit, schema=database.force_ctas_schema, show_cols=False, latest_partition=False, )) query.end_time = utils.now_as_float() session.merge(query) session.flush() payload.update({ 'status': query.status, 'data': cdf.data if cdf.data else [], 'columns': cdf.columns if cdf.columns else [], 'query': query.to_dict(), }) if store_results: key = '{}'.format(uuid.uuid4()) logging.info("Storing results in results backend, key: {}".format(key)) json_payload = json.dumps(payload, default=utils.json_iso_dttm_ser) results_backend.set(key, utils.zlib_compress(json_payload)) query.results_key = key query.end_result_backend_time = utils.now_as_float() session.merge(query) session.commit() if return_results: return payload