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